BUDS AND BLOOMS A Journal of Research

WE CAN!

GOVERNMENT ARTS COLLEGE FOR WOMEN

(Affiliated to Mother Teresa Womens University)

Nilakottai

Dindigul District, Tamilandu

2018-2019

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From Principal’s Desk

Knowledge is wealth which no thief can rob; making is the best

and safest treasure to acquire. Research is a creation of knowledge

which leads to new and effective solutions for the society. Sharing

ones knowledge is a best way to achieve immortality.

I‟m proud that our faculty and Government Arts College,

Nilakottai encourages and constantly provides the opportunity in

upgrading their research orientation by contributing articles and

papers at various levels.

Writing a perfect article/paper for the Journal is lot like a military

operation, involves discipline, foresight, research and strategy. If

done right, ends in total victory.

The Journal titled “Buds and Blooms – A Journal of Research” is a

manifestation of skills and caliber of the faculty of this institution

as knowledge has a beginning but no end.

I congratulate the members of the faculty who had been

instrumental in achieving this innovative effort. This journal

marks as an important milestone in the history of this college. My

hearty congratulations to the Editor, Editorial Board, reviewer and

the contributors of articles and research papers on various subjects

with lot of information and scope for further research.

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Account of the Memoir of the Learners, Smash of their Scholastic

Episodes in the Campus Novels of Chetan Bhagat

Dr.A.Latha M.A., M.Phil. CGT.,SLET.,Ph.D

Head & Asst.Prof of English,

Government Arts College for Women,

Nilakottai. 624208.

Abstract

This article intent to explore the elements of academic novel forms in the literary creations

of Chetan Bhagat. Academic centers are the places where one can find blend of languages,

culture, religion and beliefs. Indeed Bhagat‟s characters are also come in the preview of this

category. Accomplishing the assigned tasks successfully and professional prosperity are the two

basic features of any campus novels. Hence Bhagat‟s most of the novels have them as their inputs

in his novels.

Key words: Success, Ambition, Education, Country Development, Self-development.

Novel is a literary genre; long work of a narrative fiction, usually written in prose form

and which is ordinarily issued as a book. The genre „Novel‟ has its fountain about 2000 years

from Greek and medieval and unseasonably contemporary romance and in the convention of the

Italian renaissance „Novella‟. Since the 18th

century, the term „Novella‟ or „Novelle‟ in German,

has been exercised in English and other European languages to set out a short story or a short

novel.

Tale of Genji which is written during 1010 by Murasaki Shikibu is rewarded as the

world‟s first novel. Tale of Genji is a classical work of Japanese literature. The prototype

manuscripts no longer subsist. The work is a idiomatic delineation lifestyle of lofty courtiers

during the Heian period, written in archaic language and a poetic and confusing style that make it

illegible to the norm Japanese without consecrate study. The first English translation of the novel

was assayed in 1882.

Due to contrivance of printing machines, books were printed in number. So, the broadcast

of printed books in china led to the aspect of the classical Chinese novels by the Ming Dynasty.

4

Resembling European evolutions befell after the concoction of printing press. Miguel De

Cervantes, the author of Don Quixote is habitually adduced as the first revelatory European

novelist. Walter Scott produced pre-eminence novel, in which “events are accommodated to the

prosaic train of human events and the modern states of society” and romance which defined as,

“A fictitious narrative in prose or verse the interest of which turns upon marvelous and

uncommon incidents” However, many such romances, including the historical romances of Scott,

Emily Bronte‟s Wuthering Heights and Hermen Melvilles‟s Moby Dick are also frequently called

novels, and Scott describes romance as a “kindred term”. This sort of romance is in turn different

from the genre fiction love romance or romance novel. Like romance novel, there are many kinds

of novel as the following;

Magnitude prototype of the novel was the Picaresque commentary, which surfaced during

sixteenth century. Early instance of the picaresque narrative form is the Gil Blas in 1715 by the

Frenchman Lesage. „Picaro‟ is the Spanish word for „Rogue‟ and archetypal story pertains the

frolics an apathy knave who lives by his gagster and flaunt a trifle picaresque fiction is realistic in

etiquette, episodic in structure, and often satiric in aim. Novel of Incident, Daniel Defoe is

frequently credited with writing the first novel of incident. In 1719, He wrote Robinson Crusoe

and in 1722, Moll Flanders. Robinson Crusoe is contributed an executed unity of action by its

focus on the challenge of making it through on an uninhabited island.

Novel of Character or Psychological novel credit for having the first English novel of

character or is given to Samuel Richardson for hi Pamela or Virtue Rewarded in 1740. Pamela is

the story of a sentimental but clear-eyed young woman. This is also an instance of Epistolary

novel; Narrative is imparted exclusively by swapping of letters.

Realistic novel can be limned as the fictional endeavor to give away the resultant of

realism by depicted complex characters with incorporated motives who are cradled in social class,

handle in a forward social structure, interact with many other characters and undergo

presumptive, everyday modes of proficiency.

Bildungsroman or Erziehungsroman are German terms weighing Novel of Formation or

Novel of Education. The content of these novels is the expansion of the protagonist‟s mind and

character in the passage from childhood through contrasted endures and continually through

incorporated conjuncture into adulthood.

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The Social novel illuminates the clout of the convivial and economic trims of an epoch on

acclimating characters and arbitrating events. For instance Harriet Beecher Stowe‟s Uncle Tom’s

Cabin in 1852 is the early social novel. A Marxist interpretation of the social novel, representing

the asperity witnessed by the depressed working class, and conventionally written to instigate the

reader to radical political exploit is called the proletarian novel.

Charles Dicken‟s Tale of Two Cities written during 1859 set in Paris and London during

the French revolution, George Eliot‟s Romola in 1863 set in the Florence during the renaissance;

are the instances of Historical novel. Instances of Localities are „Wessex‟ in the novels of

Thomas Hardy, „Malgudi‟ in R.K.Narayan‟s novels are denoting the Regional novel.

Novel that explains the story that related to the college and university are called Campus

novels. Stories of the students, their families, friendship, love and romance inside any academic

situation is written in the pages of Academic novels. Chetan Bhagat is the kind of author whose

characters are the academic personalities who study or work there in the academic campus.

Chetan Bhagat is an Indian author, columnist, screen writer, television personality and

motivational speaker, known for his Indian-English novels about young urban middle class

Indians. Bhagat also writes for columns about youth, career development and current affairs 5for

„Times of India‟ and „Dainik Baskar‟. Three mistakes of my Life, Five Point Someone,Half

girlfriend, 2 states: The Story of My Marriage, One night at the call Centre, One Indian girl,

Revolution 2020: Love, Corruption, Ambition are his famous novels. Girl in Room 105 is the

novel got published very recently in the previous year. Making India awesome, What young India

Wants are the two non-fiction works of Chetan Bhagat.

All his novels have the some milieu of every academic institution. His novels could be the

exemplification of Bildungsroman novels too. All his characters are academically promoting. His

male and female protagonists catch up in colleges or in universities. Occurrence that are

delineated in the novels perpetually deal with the academic course. The ambient of his novels at

every turn pivots about the College or University because Chetan Bhagat incessantly professed to

write upon the Indian youngsters. And his visualization about Indian youngsters as well-read in

the country, bestows reflect that most of the Indian youngsters are focusing on their education for

outmatch of their self-fertile and besides for the country.

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In Britain, the academic as novelist tends towards comedy. After Angus Wilson, Malcolm

Bradbury, and David Lodge continued to explore society in novels which introduce a new

element of reader awareness and intellectual subject matter to literature. The setting is often a

University or College, the characters often academics or writers. For Instance Bradbury‟s The

History Man in the year 1977 and Cuts in the year 1987. The problems, however, remain the

standard concerns of love and money, religion, and success or failure. Where, in earlier writing,

success was seen in social terms, here the scope is often reduced to academic success, with the

result that there is a profoundly comic questioning of the whole ethos of success, failure, career,

private life, extending well beyond the English University System.

“Disappointment will come when your effort does not give you the

expected return. Failure is extremely difficult to handle, but those that do come out

stronger what did this failure teach me? Is the question you will need to ask? You

will feel miserable, you will want to quit, like I wanted to when nine publishers

rejected my first book. Some IITians kill themselves over low grades-how silly is

that? But that is how much failure can hurt you.” (2 states: The Story of My

Marriage)

“Campus novels” are similarly grasped as “Academic Fiction”. It is a very enthralling genre

that has gained worldwide readership. Campuses are the emplacement where stories propagate

like green lands. David Lodge is one of the most popular contemporary writers of this genre in

Britain. There was some campus novels published even before David Lodge started writing such.

Thus “Pnin” by Vladimir Nabokov and “Lucky Jim” by Kingsley Amis; both the novels have the

characteristic of a campus novel. It hearts on the academy. Academy is the place that attracts most

people because of its imaginary idealistic notions. Academic institution is the venue which filled

with candor, radiance and illuminate ideologies.

”Life is not to be taken seriously, as we are really temporary here. We are

like pre-paid card with limited validity. If we are lucky, we may last another 50

years. And 50 years is just 2,500 weekends. Do we really mud to get so worked

up? Its okay, bunk a few classes, fall in love. We are people, not programmed

devices.” (2 States: The Story of my Marriage.)

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Education is not the tool to earn money; it is to elevate ourselves as a rational thinker, and to

put together us as an enthusiastic partner in making Indian stronger. All his characters are very

ambitious that they are not only concentrating on the evolution of themselves but correspondingly

interestedly engaged with regard to the country.

“In our education system, we are taught to munch figures and

remember them for lifetime. But does it help? We are not taught how to

make decisions. The younger generation is surrounded by the internet,

surrounded by the internet, apps and video games. But somehow, my books

make read.” (2 States: The Story of my Marriage)

Chetan Bhagat has strained to acquaint the distinguishable characters to the readers

through his novels. The characters he created are known for the person with superior intellect and

smart behavior. Academic fictions are claiming since they capitalize the tension between idealism

and corruption. Though Chetan Bhagat talks about campus, education, students, he did not skip

any chance of delineating the corruption that is been running around the educational tract.

Revolution 2020: Love, Corruption, Ambition is such a novel that discourse about a person who is

been corrupted by the society and big heads in the field of politics in the process of building a

new academic institution.

His campus novels also rendered the multi culture of India. “2 states” is the novel accords

of the major characters from Tamilnadu and Gujarat. It deals with the cultural clash between the

two families when they scheme accumulate their relationship into next level. His novels

consistently deal with of alterity into it. According to “Oxford Dictionary of literary terms” by

Charis Baldick, alterity means,

“A Latinate term meaning of „Otherness‟ is commonly found in philosophy

and literary theory since the 1970s. It often arises in the analyses of

relations between the self and other (person), in discussions of encounters

between different cultures, and in observations upon the difficulty of

understanding the art and thought of past ages”. (Oxford Dictionary of

literary terms)

Academic fiction has the new tendency of dealing with new society which thinks widely

and holds the nuances of the social circumference that commemorates everything stereotypically.

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This kind of fiction surfaces to bring about a value in the society, and to instruct the people who is

very rational in thinking.

“Stupid people go to college but 'small people own them‟” (2 States: The

Story of my Marriage.)

The author desiderates to train his youthful Indian generation amidst his non-fiction with

his psyche, What young India wants and Making India Awesome. Among this both the non-fiction

works, he desires to contribute apprehensive about the economical part of India. Even his fiction

works Revolution 2020: Love Corruption, Ambition, 2 states, One Indian girl, Half Girlfriend has

the major theme that circulates the economic segment of India.

Five Point Someone is the novel that swings around the campus and along with the details

of the liability of the so called Indian education network. The significant observation of the

education is to have a liberal reasoning complement. But the actual education system in India is

always about the memorizing capacity, examination and their score. The Corruption in the

education field is portrayed in the novel through the characters.

The author is invariably promising the young generation to look over the self-elaboration

and also the expansion of the country. He in any event declares to read economics, because the

future of the country is hanging on Economics. He wants the young generation to be educated to

have a rational thinking across the gender, religion, community and caste that makes the country

worst. Hence all the novels of Chetan Bhagat is been visualized naturally by the readers who read

his novels.

Works Cited: https://shodhganga.inflibnet.ac.in>bitstream

https://en.m.wikipedia.org/wiki/campus-novel

www.iosrjournals.org>paper>version-7

www.chetanbhagat.com

https://fictioneditorsopinions.com

GREEN SYNTHESIS OF SILVER NANOPARTICLES USING CROTON BONPLANDIANUS

LEAF EXTRACT AND THEIR ANTIBACTERIAL AND ANTIFUNGAL ACTIVITIES

aN.Sumathi,

bDr. N Sadaiyandi,

cP.Rajapandi,

dK.Elumalai,

eK.Chellammal,

fM.Vidhya,

gR.Archana

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aAssistant Professor & Head, Department of Physics, Government Arts College for

Women,Nilakottai, Tamil Nadu, India b,c,d,e,f,g

Assistant Professor, Department of Physics, Government Arts College for

Women,Nilakottai, Tamil Nadu, India

Abstract

Biologically synthesized nanoparticles have been widely used in the field of medicine.

Especially, Silver nanoparticles (Ag NPs) synthesized by the leaf extract lead the biological

activity. In the present work, the synthesized Ag NPs by using the leaf extract of croton

bonplandianus Ag NPs were characterized by using UV–Visible (UV–Vis) absorption

spectroscopy, X–ray diffraction (XRD), Field Emission Scanning electron microscopy (FE–SEM)

along with Energy Dispersive X–ray (EDX) Spectroscopy and Fourier Infrared (FT–IR)

Spectroscopy, respectively. UV–Vis peak at 457 nm confirmed the Ag NPs due to the absorption.

Cubic structural analysis and 16 nm particle size of the Ag NPs were calculated by using XRD

analysis. The surface morphology along with the presence of Ag NPs was identified by using FE–

SEM and EDX, respectively. The FT–IR study revealed with the functional groups of the Ag

NPs. Finally, the present research has been explored to exhibit the significant antimicrobial

activities.

Key words: Silver nanoparticles, croton bonplandianus, Antimicrobial activity.

1. Introduction

Nanoscience and nanotechnology has seen major development in the bio–fabrication

process of metal nanoparticles (MNPs) [1]. This field is developing gradually, making an impact

in all spheres of human life [2]. The MNPs are broadly applied in the nanoscience and

nanotechnology such as medicine, biology, biotechnology, chemistry, physics, catalysis,

electronics and material sciences with reference to biocompatibility, efficient, fast, safety and cost

effective. Silver nanoparticles have unique optical, electrical, and thermal properties that

play an very important role in drug delivery, diagnostics, imaging, sensing, gene delivery,

artificial implants and it is not necessary to use high pressure, energy, temperature and

toxic chemicals, antimicrobial efficacy against bacteria, viruses and other eukaryotic

microorganisms[3,4]. Previously, reported that the green synthesis by plant extracts [5], and their

various applications such as pharmacology, electronics, cosmetics [6,7], drug delivery systems

[8], biosensors [9], interaction with biomolecules, Cancer therapeutics, Fungi, and antifungal [10–

10

12], respectively. Further, the survey of earlier literatures suggest that leaf extracts from various

plants such as Azadirachta indica [13], Caesalpinia coriaria [14], and Trianthema decandra [15],

respectively. Therefore, present study has made to design the green synthesis of silver

nanoparticles using croton bonplandianus leaf extract because, it has rich sources of secondary

metabolites, and it can easily reduce AgNO3 into the Ag NPs.

In the present work, croton bonplandianus (Euphorbiaceae) leaves were used for the

synthesis of Ag NPs using the green way and characterized by spectral analyses and biological

analyses.

2. Experimental Details

2.1. Materials

All the chemicals and reagents used in this study were of analytical grade, silver nitrate

(AgNO3, 99.9%) was obtained from Sigma–Aldrich Chemical Company. All glassware were

washed in dilute HNO3 and dried in oven. The leaf croton bonplandianus leaves were collected

from institution campus as shown in Fig.1.

2.2. Preparation of leaf extract.

The fresh plant leaves were washed with running tap water and also distilled water. 10 g

of leaves added in 100 mL sterile distilled water, and 60 to 80o

C boiled in water bath for 15 min,

and after that cooling at room temperature. Finally, this extract was filtered through Whatman

filter No.1 paper and stored at 4 o

C for further synthesis.

2.3. Synthesis of Silver nanoparticles.

1x10–3

M silver nitrate solution was prepared and stored in brown bottles. 5 mL of leaf

extract was added to 50 mL of 1x10–3

M AgNO3 solution for bio reduction process at 29 o

C in

room temperature. The color change of the leaf extract from pale yellow to dark brown indicated

that the formation of Ag NPs as shown in Fig.2.

2.4. Characterization

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The reduction of the Ag+ ions by the supernatant of the test plant extracts in the solutions

and formation of Ag NPs were characterized by UV–visible spectroscopy monitored by sampling

the aqueous component (2.0 mL) and measuring the UV–VIS spectrum of solution. The UV–VIS

spectrum of this sample was measured on a UV–2450 (Shimadzu) spectrophotometer operated at

a resolution of 1 nm. The bio–reduction of silver ions in aqueous solution was monitored by UV–

Vis spectrum of the solution between the ranges of 200–800 nm. X–ray diffraction was performed

on an X–ray diffractometer operated at 40 kV and 30 mA. The pattern was recorded by CuKα

radiation with λ of 1.5406 Å and nickel monochoromator in the ranges of 2θ between 10° to 90°.

Morphological characterization of the sample was done by FE–SEM (JEOL JSM 6701–F), a

pinch of dried sample was coated on a carbon tape. It was again coated with platinum in an auto

fine coater and then the material was subjected to analysis. For EDX analysis, the reduced silver

was dried on a carbon tape placed on a copper stub and performed on a HITACHI SU6600

FESEM equipped with an EDX attachment. Further characterization was done by FT–IR (Bruker

tensor 27) spectrometer. In order to remove any free biomass residue, the residual solution after

reaction was centrifuged at 4000 rpm for 20 min and the resulting suspension was redispersed in

10 mL sterile distilled water. The centrifuging and redispersing processes were repeated for three

times. Finally, the dried samples were palletized with KBr and analyzed using FT–IR. The

antimicrobial susceptibility of Ag NPs was evaluated using the disc diffusion method against

antibactorial and antifungal activitivities. Whatman filter paper (No.1) disc of 6mm diameter were

impregnated with 10µl of the solution containing crude extracts obtained from different solvents

(at a concentration of 100 mg/mL) and these disc were evaporated at 37oC for 24 hrs. References

standard disc were prepared with Amacilin (50µg/mL) to compare the antibacterial activity

results of fungal extracts. After drying, the disc with fungal extract and Amacilin were placed on

Muller Hinton Agar (MHA) plates were the bacterial culture was swabbed on the surface of the

agar and incubated for 24 hrs at 37oC. After incubation, plates were examined for clear zone

around the disc with fungal extracts and Amacilin. A clear zone with diameter more than 2 cm

was taken as an antibacterial activity and the results were expressed in mm as shown in Table 2.

3. Results and discussion

3.1. UV–Vis spectra of Ag nanoparticles

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The formation and stability of the reduced Ag NPs in the colloidal solution was monitored

by UV–Vis spectrophotometer analysis and showed the maximum absorbance at 457 nm

corresponding to the surface Plasmon resonance of Ag NPs. The observation indicates that the

reduction of Ag+ ions took place extracellular as shown in Fig.3. This result confirmed that the

formation of Ag NPs using leaf extract of croton bonplandianus. Therefore, the present study is

highly co–related and supported with the previous study, the optical absorption spectrum of Ag

NPs in the region of 320–620 nm [16].

3.2. XRD studies

The XRD patterns of Ag NPs synthesized through leaf extract of croton bonplandianus

was observed, and the number of Bragg reflections with 2θ values of 38.13◦, 44.36

◦, 64.78

◦, and

77.60◦, respectively. The sets of corresponding lattice planes were observed, which may be well

indexed to the (111), (200), (220) and (311) facts of silver respectively as presented in Fig.4.

XRD patterns also represent the Face Centered Cubic (FCC) structure of metallic silver and the

data was found to be correlation with that of the database as JCPDS file number: 89–3722. It

might be thought that the unassigned peaks are due to the crystallization of bioorganic phases that

occur on the surface of the NPs [17–19]. The XRD patterns thus clearly shows that the Ag NPs

formed by the reduction of Ag+ ions by the cell filtrate are crystalline in nature similar to earlier

published results [20–22]. The average grain size of the Ag NPs were determined using Scherer‟s

equation,

D= Kλ/β cosθ ------------ (1)

Where,

„D‟ is the particle diameter size in nm, „K‟ is a constant, „λ‟ is the wavelength of X–ray source

(0.15406 nm), „β‟ is the full width half maximum (FWHM) and „θ‟ is the diffraction angle in

theta. By using the above relation (1), the average size of the Ag NPs is estimated (Table.1) to be

around 26 nm using Scherer‟s equation.

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Table 1

S.No 2θ

(degree)

d spacing

(10-10

m)

FWHM (β)

(radians) Miller indices

Particle size (D)

(nm)

1. 38.13 2.35675 0.47840 [111] 17.5

2. 44.36 2.03100 0.33180 [200] 25.58

3. 64.78 1.44114 0.30830 [220] 30.52

4. 77.60 1.23039 0.33010 [311] 30.89

Average particle

size D=26nm.

3.3. FE–SEM along with EDX analysis

The Ag NPs were observed predominately adopt a near spherical morphology with smooth

surface under the Field Emission Scanning Electron Microscopy in different magnifications

30,000 and 50,000 as shown in Fig. 5 A, B, respectively. The FE–SEM images of Ag NPs were

assembled on to the surface due to the interactions such as hydrogen bond and electrostatic

interactions between the bioorganic capping molecules bound to the Ag NPs [23]. Similar

phenomenon has been reported previously, where the FE–SEM micrograph shows crystalline

spherical Ag NPs [24, 25]. The sizes of the particles were ranges from 22–52 nm as shown in Fig.

5 A, B, respectively. The information of elemental compositions for Ag NPs was carried by EDX

analysis and is depicted in Fig.5C. EDX spectrum confirmed the presence of strong elemental

signal of the silver at 3 keV which is typical for the absorption of metallic silver nanocrystallites

due to surface Plasmon resonance [26].

3.2. FT–IR analysis

The FTIR spectra indicate various functional groups present at different positions. The FT–IR

spectra of control leaf extract (before reaction without Ag NO3) and synthesized Ag NPs (after

reaction with Ag NO3) are shown in Fig.6 A, B, respectively. The corresponding transmittance

peaks at 3400, 2125, 1641, 777 and 597cm–1

for control leaf extract, and at 3404, 2927, 2366,

1637, 1381, 1076 and 627 cm–1

for synthesized Ag NPs are observed from Fig.6 A, B,

14

respectively. The broad and strong bands at (3400–3404) cm–1

were due to bounded hydroxyl (–

OH) or amine groups (–NH) of leaf extract. The peaks observed at 2927 cm–1

can be assigned to

the C–H group. The peaks at (1641–1637) cm–1

were attributed to stretching vibration of carboxyl

group (–C=O). The transmittance at around 1381 cm–1

notably showed NO3 existed in residual

amount. The transmittance peak located at around 1076 cm–1

can be assigned as the −C−O−C

vibration. Further, the result of the present study indicates that the Carboxyl (–C=O), hydroxyl (–

OH) and Amine (–NH) groups of leaf extracts are mainly involved in fabrication of Ag NPs [27].

The appearance of peaks in the amide I and amide I1 regions characteristic of proteins/enzymes

that have been found to be responsible for the reduction of metal ions when using the leaf extract

for the synthesis of Ag NPs. Similar to the use of microorganisms such as fungi for the synthesis

of MNPs [28], indicates the binding of the NPs with proteins. The FT–IR measurements were

carried out to identify the Ag NPs in the Croton Bonplandianus leaf extract and observed

absorbance spectra bands at 1641 cm–1

was more characteristic and mainly responsible for the

bio-reduction of silver ions [29].

3.3 Study of antimicrobial activity Ag NPs

The inhibitory activities of Ag NPs against different pathogens have been tabulated as

shown in Table 1 and 2, respectively, and compared with standard antibiotic amacilin. The zone

of inhibitions of Ag NPs activity against various microorganisms for bacteria Staphylococcus

aureus, E–coli, Bacillus substilis [30], as shown in Fig.7 A, B, C, respectively, and fungi Mucor

Sp, Tricoderma sp, Aspergillus nigar [31], as shown in Fig.8 A, B, C, respectively.

The significant effect of Ag NPs along with antibiotics was also checked various

multiple drugs resistance pathogenic bacteria and fungus. The mechanism of the bactericidal

effect of Ag colloid particles against bacteria is not very well–known. Ag NPs may attach to the

surface of the cell membrane and disturb its power function such as permeability and respiration.

It is reasonable to state that the binding of the particles to the bacteria depends on the surface area

available for interaction. Smaller particles having the larger surface area available for interaction

will give more bactericidal effect than the larger particles [32]. Bactericide effects of ionic silver,

the antimicrobial activity of colloid silver particles are influenced by the dimensions of the

particles the smaller the particles, the greater antimicrobial effect this suggests the possibility that

15

the Ag NPs may also penetrate inside the bacteria and fungi causing damage by interacting with

electron phosphorous and sulphur containing compounds such as DNA [33, 34].

Table 2

Organism

Silver

nanoparticles

(M1) (µl)

Leaf (Le)

(µl)

AgNO3(Ag)

(µl)

Control(C)(µl) Antibiotic

Amacilin

(s)

10mg/m

Staphylococcus

aureus (A)

1.3 0.8 0.5 0.6 1.3

E-coli (B) 1.9 0.8 0.4 0.6 1.8

Bacillus

substilis (C)

1.3 0.8 0.4 0.3 1.3

Table 3

Organism

AgNO3(M)

(µl)

Leaf (L)

(µl)

Silver

nanoparticles

(Ag) (µl)

Control(C)

(µl)

Antibiotic

Amacilin

(s)10mg/m

Mucor Sp (A) - 0.1 0.1 - 0.2

Tricoderma

sp (B)

0.1 - 0.1 - 0.3

Aspergillus

nigar (C)

- - 0.1 - 0.3

4. Conclusion

Silver nanoparticles are produced by the reduction of silver ions to colloidal silver. The

present studies were confirmed the formation of silver nanoparticles using croton bonplandianus

leaf extract from UV-Vis, XRD, FE-SEM, and with EDX and FTIR respectively. The UV- Vis

16

peak for silver nanoparticles at 457 nm. The particle size was confirmed from XRD and FE-SEM.

EDX spectra confirmed the predominate level of Ag. The FT–IR spectra of control leaf extract at

3400, 2125, 1641, 777 and 597cm–1

, and for synthesized Ag NPs were observed at 3404, 2927,

2366, 1637, 1381, 1076 and 627 cm–1

respectively. Therefore, the present study is highly

correlate and good agreements between all studies. In addition, investigation on the antibacterial

activity of the nanoparticles against Staphylococcus aureus, E–coli and Bacillus substilis, and

revealed high potential of croton bonplandianus leaf extract by disc diffusion method. Finally, it

was confirmed that the present study might be applicable on the pharmacology, electronics,

cosmetics, drug delivery systems, biosensors, interaction with biomolecules, Cancer therapeutics,

Fungi and antifungal, respectively.

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Figure Caption

Fig.1. The photography Croton bonplandianus plant.

Fig.2. The photography (A, leaf extract) (B, reduction of Ag NO3).

Fig.3. UV–vis spectrum of Ag NPs synthesized from Croton bonplandianus leaf extract.

Fig.4. XRD spectrum of Ag NPs synthesized from Croton bonplandianus leaf extract.

19

Fig.5A,B. FESEM and EDX spectrum of Ag NPs.

Fig.6. FT–IR spectra for the pure leaf extract and synthesized Ag NPs.

Fig.7. Anti–bacterial analysis, A– Staphylococcus aureus, B– E–coli, C– Bacillus substilis

Fig.8. Anti–fungal analysis, A–Mucor Sp, B– Tricoderma sp, C– Aspergillus nigar

Table caption

Table.1. Calculation the average particle size of Ag NPs of Croton bonplandianus by using Debye

Scherer‟s equation.

Table.2. Zone of inhibition (mm) of biosynthesized Ag NPs against antibacterial pathogens.

Table.3. Zone of inhibition (mm) of biosynthesized Ag NPs against antifungal pathogens.

Semantic Based Web Mining : A Survey

S.Meena and Dr.A.Pethalakshmi

Research Scholar, Mother Teresa Women's University, Kodaikanal, Tamilnadu.

E-mail : meenavidhya@gmail.com

Principal, Govt. Arts College(W),Nilakottai,Tamilnadu.

E-mail : pethalakshmi@ yahoo.com

ABSTRACT:

In this paper we survey the semantic based web mining which is the combination of two

fast evolving research area Semantic Web and Web mining. Most of the researchers are working

to improve the web mining results by exploiting semantic structure in WWW. The Semantic Web

can make mining of the Web much easier because of the availability of background knowledge

and Web Mining can also construct new semantic structures in the Web. This survey gives

detailed survey of researches in this area. It shows the positive effects of Semantic Web Mining,

20

the problems faced by researchers and propose number of approaches to deal with the very

complex data which are produced by the technologies of Semantic Web. Here we first introduces

the knowledge of Semantic Web and Web mining techniques, and then discusses the semantic-

based Web mining and its applications and finally discuss the survey on semantic based web

mining tools.

1 INTRODUCTION

The World Wide Web (WWW) is a huge resource of multiple types of information in

varied formats which is very useful for the analysis of business progress, which is very important

now days to stand in the competition of business. Researchers are beginning to investigate human

behavior in this distributed Web data warehouse and are trying to build models for understanding

human behavior in virtual environments. Data mining, often called Web mining when applied to

the Internet, is a process of extracting hidden predictive information and discovering meaningful

patterns, profiles, and trends from large databases. Web mining is an iterative process of

discovering knowledge and is proving to be a valuable strategy for understanding consumer and

business activity on the Web. There are three sub categories for mining web information. These

sub categories are

Web Content Mining

Web Structure Mining

Web Usage Mining

The Semantic Web is the second-generation WWW, enriched by machine-processable

information which supports the user in his tasks. Given the enormous size even of today‟s Web, it

is impossible to manually enrich all of these resources. Therefore, automated schemes for

learning the relevant information are increasingly being used. Web Mining aims at discovering

insights about the meaning of Web resources and their usage. Given the primarily syntactical

nature of the data being mined, the discovery of meaning is impossible based on these data only.

Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming

more and more common. Furthermore, mining the Semantic Web itself is another upcoming

application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill

their goals, but that the full potential of this convergence is not yet realized.

21

2. BACKGROUND INFORMATION

SEMANTIC WEB MINING CONCEPTS

The Semantic Web can be characterized as an expansion of the present web. Here the data

is exhibited in a well - characterized way, better empowering PCs and individuals to work in

participation. Information in the Semantic Web is characterized and connected in a way that can

be utilized for more successful disclosure, robotization, combination and reuse crosswise over

applications. This information can be shared and prepared via mechanized devices and also

individuals. The Semantic Web will give a framework that empowers website pages, as well as

databases, administrations, programs, sensors, individual gadgets, and even family unit machines

to both expend and create information on the web. Semantic web mining is basically mining the

data relating to the semantic Web. This implies mining Web pages so that the machine can better

comprehend the data. It likewise implies mining the information sources to build up a compelling

semantic Web.

2.1 Web Mining

Web Mining is the utilization of information mining methods to the substance, structure and use

of Web assets. Web mining is becoming quickly since its initiation in or around 1996, and new

procedures are being produced both utilizing traditional and delicate processing approaches

simultaneously [2]. Web mining, when looked upon in information mining terms, can be said to

have three operations of intrigues – grouping (eg. Discovering characteristic groupings of clients,

pages, and so on.), Affiliations (eg. which URLs have a tendency to be asked for together) and

successive examination (eg. the request in which URLs have a tendency to be gotten to).The

Three Main Areas Of Web Mining Are: Fig:1

A) Content Mining –

Analyses the substance of Web assets. It depicts the revelation of helpful data from the

web substance. For the most part in view of content mining systems, yet expansions to interactive

media substance is starting to develop in the examination. The web content comprises of a few

sorts of information, for example, printed, picture, sound, video, metadata, and additionally

hyperlinks. The vast majority of the endeavors on web content mining are focused on the content

22

or hypertext substance. The printed parts of web substance information comprise of unstructured

information, for example, content archives, semi organized information, for example, HTML

reports and that's just the beginning Organized information, for example, information in tables or

database-created HTML pages.

B) Structure Mining

Examinations the hyperlink structure between Web pages. Web Structure Mining is

Worried with finding the model fundamental the connection structures of the web. It is utilized to

think about the topology of the hyperlinks. This model can be utilized to sort site pages and is

helpful to create data, for example, the likeness and relationship between various sites. While

Web Content Mining endeavors to investigate the structure inside of a record (intra-report

structure), Web Structure Mining thinks about the structures of archives inside of the web itself

(between record structure).

C) Usage Mining

Examinations the client's snaps from Web server logs. Web Usage Mining manages

examining the information created by the web surfer's sessions or practices. Web Usage Mining

mines the auxiliary information got from web server access logs, intermediary server logs,

program logs, client profiles, enlistment information, client sessions or exchanges, treats, client

Questions, bookmark information, mouse snaps and parchments [3].

Fig:1 Classification of Web Mining

23

2.2 Semantic Web

The present WWW has an enormous measure of information that is frequently

unstructured and generally just human reasonable. The Semantic Web means to address this issue

by giving machine interpretable semantic s to give more noteworthy machine backing to the

client. Semantic Web is an augmentation of the present web in which data is given all around

characterized importance, better empowering PCs and individuals to work in cooperation. The

Semantic web will give wise access to heterogeneous, dispersed data empowering programming

items to intervene between client needs and the data source accessible.

Semantic Web is

1. Giving a typical punctuation to machine justifiable

articulations.

2. Building up basic vocabularies.

3. Concurring on a legitimate dialect.

4. Utilizing the dialect for trading proofs.

SEMANTIC WEB REPRESENTATION TECHNIQUES

Numerous accessible strategies and models are utilized to speak to and express the

semantic of information, for example, the standard systems suggested by W3C named Extensible

Mark-up Language, Resource Description Framework, and Web Ontology Language which are

quickly clarified beneath.

Extensible Mark-Up Language

The Extensible Mark-up Language (XML) procedure has and recovers information

on/from the web. By empowering clients to make their own particular labels, it permits them to

characterize their con-tent effectively. In this way, the information and its semantic connection

boats can be spoken to.

Resource Description Framework

The Resource Description Framework (RDF) is a common language that enables the

facility to store resources‟ information that are available in the World Wide Web using their own

domain vocabularies. Three types of elements contented in the RDF: resources (entities identified

24

by Uniform Resource Identifiers URIs), literals (atomics values like strings and numbers), and

properties (binary relationships identified by URIs). This is a very effective way to represent any

kind of data that could be defined on the web.

Web Ontology Language

The Web Ontology Language (OWL) is considered a more complex language with better

machine interpret- ability than RDF. It precisely identifies the resources‟ nature and their

relationships. To represent the Semantic Web information, this language uses ontology, a shared

machine-readable representation of formal explicit description of common conceptualization and

the fundamental key of Semantic Web Mining . Ontology creators are

expressing the interest domain which is based on classes, and properties (represent atomic distinct

concepts and rules in other semantic languages respectively). As examined by, the architecture of

Semantic Web that is based on the vision of Sir Berners-Lee, is divided into seven layers:

1) URI;

2)XML, NS, & XML schema;

3) RDF & RDF schema;

4) the ontology vocabulary;

5) Logic;

6) Proof;

7)Trust

First of all, URI which is in charge of resource encoding process and its identification.

Next, XML, NS, and XML schema layer which is in charge of 1) the separation of data content,

data structure, and the performance format based on linguistic; and 2) representing them using a

standard format language. Furthermore, the layer of RDF and RDF schema define the information

on World Wide Web and its type using a semantic model. More-over, the ontology vocabulary

layer is concentrated on revealing semantics among information by defining the knowledge

shared and the semantic relations within different sorts of information. Logic is the next layer

which takes the responsibility of providing the foundation of intelligent services such as logical

reasoning by supplying axioms and

inference principles. The last two layers are “proof” and “trust” which deal with enhancing the

security of web by using encryption and digital signature mechanisms to identify changes in

documents.

25

3. RELATED WORKS

Semantic Web Mining is a new and fast-developing research area combining Web Mining

and Semantic Web. In this paper a detailed state-of-the-art survey of on-going research in

Semantic Web Mining has been presented. This study analyzes the merging of trends from both

areas including a) using semantic structures in the Web to enrich the results of Web Mining and

b) to build the Se-mantic Web by employing the Web Mining techniques. We also have

provided justification that the two areas Web Mining and Semantic Web need each other to

achieve their goals, but that the full potential of this convergence is not yet realized.

Semantic Web Mining is a new and fast-developing re-search area combining Web Mining

and Semantic Web. In this paper a detailed state-of-the-art survey of on-go- ing research in

Semantic Web Mining has been presented. This study analyzes the merging of trends from both ar-

eas including a) using semantic structures in the Web to enrich the results of Web Mining and b)

to build the Se-mantic Web by employing the Web Mining techniques. We also have provided

justification that the two areas Web Mining and Semantic Web need each other to achieve their

goals, but that the full potential of this convergence is not yet realized.

Semantic Web usage mining. In the Semantic Web environment, we can give a clear

semantics to user behavior the body of knowledge based on the log file of semantic ontology

knowledge. On this basis, excavation shown to be effective in establishing the users gathering in

the same interest, which provides users with ontology-based personalized view to improve the

Web usage mining results.

Table1. Summary of existing works in semantic web mining.

S.NO. Author Domain Mining

Techniques

Work

1 Aarti Singh 2012 Semantic Web

Mining

Clustering Agent Based Framework for

Semantic Web Content

Mining

2 Sharma K,

Shrivastava and

Kumar V 2011

Web mining IR Web Mining Today and

Tommorow

26

3 Bhatia C.S. and

Jain S, 2011

Ontology Grammatical

Rule Extraction

Technique.

Semantic Web Mining:

Using Ontology Learning

and Grammatical Rule

Interface Technique

4 Jayatilaka A.D.S

and Wimalarathne

G.D.S.P 2011

Knowledge Ontology “Knowledge Extraction

for Semantic Web Using

Web Mining”

5 WANG Yong-gui,

JIA Zhen 2010

Semantic Web

Mining

Frame work of

Agent

Research on Semantic Web

Mining”, International

Conference on Computer

Design and Applications,

6 ZhusongLiu,Yuqin

Zhang, 2010

E-commerce Semantic

search Retrieval

Research and Design of E-

commerce Semantic Search

7 Soner Kara, zgur

Alan,

OrkuntSabuncu,

SametAkpınar,

Nihan K. C,

Ferda N. Alpaslan

2010

Soccer keyword-based

semantic

search

An Ontology-Based

Retrieval System Using

Semantic indexing

8 Brijendra Singh,

Hemant Kumar

Singh 2010

Data mining Summary of

data mining

techniques

Web data mining research: a

survey

9 Singh A., Juneja

D. and Sharma

A.K.2009

Knowledge Muti Agent

System

Design of Ontology-Driven

Agent based Focused

Crawlers

10 Meirong T.and

Xuedong C 2010

E-business Agent-based

Web mining

Application of Agent Based

Web Mining in E-business

11 O. Mustapaşa, A.

Karahoca, D.

Karahoca and H.

Uzunboylu 2011

Distance learning Association

rules

(Apriori

algorithm)

Hello World, Web Mining

for E-Learning

27

12 H. Liu 2010 Knowledge Weighted

feature-based

search model

Towards Semantic Data

Mining

13 V. Nebot and R.

Berlanga 2012

Biomedical Association

rules

Finding Association Rules in

Semantic Web Data

14 N. Lavrač, A.

Vavpetič, L.

Soldatova, I.

Trajkovski and P.

K. Novak 2011

Biology Classification

(CN2, CN2-

SD, SEGS & g-

SEGS)

Development of Semantic

Decision Tree.

15 A. Jain, I. Khan

and B. Verma

Education Association

rules

Secure and Intelligent

Decision Making in

Semantic Web Mining

16 V. Nebot and R.

Berlanga

Medical Association

rules (Apriori)

“Mining Association Rules

from Semantic Web Data

17 W. Yong-Gui and

J. Zhen

Agents Clustering Research on Semantic Web

Mining

18 Z. Abedjan and F.

Naumann

Web Association

rules

“Context and Target

Configurations for Mining

RDF Data

19 J. D. Velásquez, L.

E. Dujovne and G.

L‟Huillier

Web

personalization

Clustering

(SOFM and K-

mean

algorithms)

Extracting Significant

Website Key Objects: A

Semantic

Web Mining Approach

20 T.Raji,

B.L.Shivakumar

2016

Data mining Web mining

techniques

Semantic Web Mining

Technologies and Tools

Aarti Singh [1] focuses on proving agent-based framework for mining semantic web

contents. Author used clustering Techniques for providing clusters of query result which are

28

relevant. Aarti Singh [1] proposed agent based Semantic Web Mining System (SWMS) with the

aim to provide context based and knowledge oriented results to the user. Author also used

classification and clustering techniques on web contents, so as to provide knowledge based

response to the user and otherwise will point unnoticed patterns. The system contains Interface

agent, collection agent with ontology database, clustering agent and content mining agent.

Content mining agent uses descriptive Meta data agent and semantic Meta data agent. Author

pointed that combination of web mining techniques and agent technology will lead better results.

Implementation of this system is left for future work.

Sharma K [2] presents study about how to extract the useful information from the web and

also given the information and comparison about data mining. Sharma K [2] highlighted the past,

current and upcoming of web mining. Authors introduce online resources for retrieval of

Information from the web for web content mining and for detecting access patterns of the user

from web servers for web usage mining. Author also described the use of cloud computing in web

mining through cloud computing as future for web mining.

Bhatia [3] highlighted that searching some information in web many time gives result that

is unsatisfactory because information returned to user is less relevant. Authors pointed out that

retrieving precise information from World Wide Web is still researcher‟s main area of concern.

Authors also pointed out that semantic web is the solution to the problem. Authors suggested that

using the current web semantically result of web mining can be improved and which lead to

building of semantic Web. Bhatia [3] suggested the process of Ontology mapping for the

extraction of semantics information through Grammatical Rule Extraction Technique.

Jayatilaka [4] pointed out that Semantic web has its roots on ontology‟s and most of the

ontology databases are manually build which is monotonous task as well as time consuming and

significant domain knowledge is required for designer. Manually building ontology has

challenged the growth of semantic web development. Jayatilaka [4] examine the problems in

extracting information from large number of web pages in order to build ontology database and

proposed method that combines web content mining with web data usage mining in the

information extraction process. Authors have considered both the web users and web author‟s

perspectives with respect to web content which led to the extraction of more rational information.

The evaluated results also proved the effectiveness of the proposed methodology. Proposed

methodology by authors will be useful for conversion of large set of natural language web pages

to semantic web database and also can be used to build cross domain ontology database.

29

WANG Yong-gui [5] pointed out that semantic ontology based web mining cab be useful

to improve web services. Semantic ontology based web data mining is a combination of the

semantic web and web mining. Application of Semantic web helps to get results from web easily

as well as improves the effectiveness of web mining. WANG Yong-gui [5] firstly introduced the

related knowledge of Semantic Web and Web mining and then author has discussed the semantic

based Web Mining which proposed to build a semantic based Web mining model using the

framework of Agent. But due to the immaturity of the relevant technologies and various other

limitations, implementation is left for future work.

ZhusongLiu, Yuqin Zhang [6] has proposed that traditional grammar level based search

has led to low quality of results due to lack of semantics in it. Authors introduced techniques of

semantic web to e-commerce domain and designed a retrieval system with semantic ontology

network structure and highlighted the important technologies in ecommerce search with

semantics. Authors compared semantic structure and semantic retrieval algorithm with traditional

keyword based search and concluded that semantic search retrieves more relevant information to

users search query.

Soner Kara [7] and others proposed semantic ontology based information extraction from

web and search system. Authors demonstrated its application to soccer domain. They highlighted

important issues like search usability, scalability of system and retrieval performance in ontology

based semantic search. Authors proposed a keyword-based semantic search approach. Authors

also highlighted the fact that performance of the system can be considerably improved using

domain specific information extraction from web pages, domain specific inference of additional

information and applying domain specific rules. Performance in proposed system is improved by

using indexing semantic ontology database. They implement the system OWL and compared the

performance of system against traditional keyword based search method.

Brijendra Singh [8] highlighted importance of web mining in an area of Data Mining and

specifically importance of the extraction of information from the World Wide Web. Authors

classified web mining into three different categories based on data they mine namely web data

mining, web structure mining and web usages mining. Authors provided review of past, current

and future of web data mining, web structure mining and web usages mining. Authors had also

given future directions for research in the field. Authors also presented the comparative study and

summary of various techniques used for web data mining with their applications and several

important research issues.

30

Singh [9] proposed an ontology based special purpose crawler which improves existing

agent based crawlers by using ontology database and related information. Use of ontology

database in crawling improve retrieval performance over keyword based crawling as ontology

database based crawling makes use of semantic information present in web pages. Authors

proposed an adaptive and intelligent ontology mapping method for providing an interface

between structured ontology and unstructured web pages that facilitates agent interaction in all

type of ontology. Their work automates the semantic mapping job using multi-agent system that

not only defeat the annoyance of existing mapping methods but also is improves performance.

Meirong [10] highlighted that Agent-based web data mining has advantages of web data

mining and Agent. Also Agent-based Web mining mines data efficiently and intelligently. This

makes Agent-based Web mining important tool for E-business. Meirong [10] had given an

application model of Web mining in E-business environment.

4. CONCLUSION

In this paper we have highlighted the fast developing research areas, Semantic web and

Web Mining. The researchers discussed how Semantic web Mining can improve the results of

Web Mining by using the new semantic structures in the web. Web mining finds its applicability

in retrieving information from web pages. Techniques of semantic web if applied on mined result

will precisely retrieve information with semantics. Researchers also agreed on semantically

mining web contents. As there is no standard framework for semantic based web mining, there is

lot of scope for research in this direction.

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33

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murpdh; kfsph; fiyf;fy;Y}hp>

epyf;Nfhl;il.

fhg;gpaq;fspy; murpay; epiy

muR vd;w nrhy;ypid jkpo; mfuhjp “xU ehl;il my;yJ khepyj;ij

eph;tfpf;Fk; (mjpfhuq;fs; tiuaWf;fg;gl;l) mikg;G”1 vd tpsf;fk; jUfpwJ. jkpopy;

murpay; vd;gjid tlE}yhh; „uh[jh;k‟ vd;W toq;fpdh;. murdpd; midj;Jf;

flikfisAk; ,r;nrhy; Fwpf;Fk;. murpaiy „mh;j;j rh];jpuk;‟ vd;W tlE}yhh;

gpd;dhy; toq;fshapdh;.

murpay; vd;w nrhy;ypidj; jkpo; mfuhjp “ehL xd;wpid Ml;rp Ghpe;jpUf;Fk;

Kiw> Ml;rp GhptJ gw;wpa gy;NtW fl;rpfspd; nfhs;iffs; kw;Wk; eilKiwfs;”2 vd

tpsf;Fk;. NkYk; r%fj;jpw;fhd tsh;r;rpf;F murpay; mikg;G ngUk;gq;F tFf;fpwJ.

“r%fk; muRk; ,d;wp xU kdpjdhy; mikjpahfTk;> ghJfhg;ghfTk; thoKbahJ.

tsh;r;rp milaTk; KbahJ. ,e;j cz;ikia czh;j;Jk; tifapy; kdpjd;

,ay;ghfNt xh; murpay; caphp vd;Wk;> mtd; muR ,y;yhky; ,Ug;ghdhapd; xd;W

kdpjj;jd;ikf;F Nkyhdtdhf ,Uf;f Ntz;Lk;. my;yJ fPohdtdhf ,Uf;f Ntz;Lk;

vd;Wk; fpNuf;f mwpQh; mhp];lhl;by; $WtJ epidtpw; nfhs;syhk;”3

vdNt r%f mikg;gpy; rl;ljpl;lq;fis cUthf;fp mtw;iwr; nray;gLj;Jfpd;w

jiytd; vg;gb ,Uf;f Ntz;Lk; vd;Wk;> mtdJ Ml;rpr; rpwg;G Fwpj;Jk; fhg;gpaq;fs;

vLj;Jf;fhl;Lfpd;wd.

xU murd; ey;ytdhf ePjpapd; topr; nry;gtdhf ,Ue;jhy; mtd; rpwe;jtdhf

fUjg;gLfpd;whd;. murd; mr;rk; ePq;fp mwk; fhg;gtdhfTk;> Fiw jPh;j;J

Kiwnra;NthdhfTk; Fb jOtpf; NfhNyhr;RNthdhfTk;> rhd;Nwhiuj; Jiztdhff;

nfhz;NlhdhfTk; jpfo Ntz;Lnkd;gNj njhy;Nyhh; tFj;j Ml;rpf;fiy vd;W RUq;ff;

$wyhk;. ,ij>

“kz;zpy; tho;jU kd;Daph;fl; nfy;yhk; fz;Zk; MtpAk; Mk;ngUq; fhtyhd;” (Nrf;.ng.Guh.14) vd;fpwhh; Nrf;fpohh;. Ml;rpahsd; nghJkf;fis jk; kf;fshff; fUJkhW rq;fg; Gyth;fs; mwpTWj;jpAs;sij>

34

“fhty; Fotp nfhs;gthpd; xk;Gkjp” (Gw.eh.5)

vd;W Rl;Lfpd;wJ.

xU kd;dd; vg;gb ,Uf;f Ntz;Lk; vd;gijf; fk;gh>; ehl;il MSk; jiytdhfpa

murd; fhl;rpf;F vspatdha;j; jhAs;sk; nfhz;ltdha; ePjpnewp tOthjtdha; ,Uf;f

Ntz;Lk;.

“nra;td nra;jy; ahz;Lk; jPad rpe;jpahik ca;td Mf;fpj; jk;NkhL cah;td cte;J nra;Naha;” (fpl;fp.mu.gl.11) vd;W nra;aj; jf;f nray;fisr; nra;jy;> jPik gaf;Fk; nraiyr; nra;ahik> gpwh;

,fo;e;jNghJ mth;fsplj;Jg; gopnrhw;fisf; $whJ ,dpa nrhw;fisg; NgRjy;> nka;

NgRjy;> gpwh; nghUis tpUk;ghik> jd;;idf; f; filgpbg;ghiu ew;fjp milar; nra;J

jhKk; Nkk;gl tpsq;Fjy; Nghd;wit Ml;rpahsDf;F ,Uf;f Ntz;ba gz;GfshFk;

vd;fpwhh; fk;gh;.

xU ehl;L kd;dd; ve;e jPq;Fk; ,d;wpf; fhf;Fk; NghJ mq;Nf Fw;wq;fspd;

tha;g;Gk; FiwthfNt ,Uf;Fk;. ehl;L kd;dd; eLT epiyAld; jdpkdpjDf;Fhpa

gz;GfNsHL kw;wth;fSf;F vLj;Jf; fhl;lhf thoNtz;Lk;

,j;jd;ikNa murd; kf;fs; ey;Ywit tsh;f;f mbg;ilahfpd;wJ. ,g;gz;Ng

jiyikg; nghWg;Ngw;wpUf;Fk; kd;dNdhL kf;fis ,iae;J Nghfr; nra;fpd;wJ.

kf;fspd; Njitf;F Vw;g Njitg;gLk; Neuq;fspy; muridf; fhZk; epiyiaAk;

Vw;g;gLj;jpj; jUfpd;wJ. Nkw;Fwpj;j jd;ikfisg; ghz;badpd; muR nfhz;bUe;jikahy;

jhd;; fz;zfp ghz;baid Neubahfr; re;jpj;Jj; jd; Fiwfisf; $Wfpd;whs;. ghz;ba

kd;dDk; jhd; fhzte;j fz;zfpia mitf;F mioj;J>

“tUf kw;w ts; jUf <q; nfd” (rpyk;G.to.fh.45)

vdf;$wp Neubahf ciuahLfpd;whd;;. NkYk;>

“ePh; thh; fz;iz vk;Kd; te;Njha; ahiu NaheP klf;nfhb Naha;?” (rpyk;G.to.fh.48-49) vdj; jd;dplk; fz;fyq;fp te;j ngz;zplk; ghpTld; ghz;ba kd;dd; Nfl;fpd;whd;.

ghz;ba kd;dd; fhl;rpf;F vspatd; vd;gijAk; jd;Kd; te;J epd;w fz;zfpapd;

fUj;ij Vw;Wf;nfhz;ltdhf tpsq;fpaijAk;> NkYk; fz;zfp jd; Nfhgk;

jzpahJ>“Njuh kd;dh nrg;GtJ cilNad;”(rpyk;G.to.fh.50) vd;Wk;>“ew;wpwk; gluhf;

nfhw;if Nte;Nj”(rpyk;G.to.fh.66) vd;nwy;yhk; fLQ;nrhy; $wpa NghJk; ghz;bad;

nghWik fhf;fpd;whd;. ,Wjpahf>

“jho;e;j Filad; jsh;e;j nrq;Nfhyd;” (rpyk;G.to.fh.72)

35

vd;Wk;>

ahNdh murd; ahNd fs;td;;” (rpyk;G.to.fh.74)

vd;Wk;>

vd;Wk; $wp jd; jtw;iw Vw;Wf; nfhs;fpwhd;. Nrho ehl;ilr; Nrh;e;j ngz;nzhUj;jp

ghz;ba ehl;L kd;dDf;F ePjp jtwpaij Rl;bf;fhl;bJ rpe;jidf;FhpaJ. ,jdhy;

mf;fhy kf;fSf;F fUj;Jr; Rje;jpuk; ,Ue;jik Gyhfpd;wJ.

ghz;ba kd;dDk; jh;kk; ePjp jtwpajhy; “nfLf vd; MAs;” (rpyk;G.to.fh.77)

vd;W $wp caph; Jwf;fpd;whd;. mtdJ caph; Jwg;G xU rpwg;ghfNt fhl;Lfpwhh;

,sq;Nfhtbfs;. NkYk;> kJuhGhpj; nja;tj;jpd; $w;Wk; ghz;bad; ePjp NgZk; kd;dNd

vd;gij nka;g;gpf;fpd;wJ. nghw;nfhy;yidf; fhuzk; $whky; jd; jtiw czh;e;J

khz;l ghz;badpd; gz;G kjpg;gpw;Fhpajhfpd;wJ.

Nrud; nrq;Fl;LtDk; Fbkf;fspd; fhl;rpf;F vspatdhf kf;fisg; ghJfhf;Fk;

fhtydhfTk; tpsq;fpaikiar; rpyg;gjpfhuf; fhg;gpak; Fwpf;fpwJ. kitsk; fhzr;nrd;w

NruDk;> Ntz;khSk; nghJkf;fisr; re;jpj;J ciuahLfpd;wdh;.

kf;fs; kd;ddpd; fhtypy; gakpd;wp ,Ue;jdh;. vd;gijAk; nghUs;tapd; gphpthy;

jdpj;jpUf;Fk; kidtpah;fSk; kd;ddpd; ghJfhg;gpy; ,Ue;jikiaAk; fhg;gpaq;fspy;

mwpaKbfpd;wJ. kf;fs;> kd;did ,iwtdhf vz;zpajhy; fztd; khh;fs; jq;fspd;

FLk;gq;fis tpl;Lg; gphpe;jpUe;jhYk; kdcWjpAld; ve;jj; jPq;Fk; Vw;glhJ vd

vz;zpapUe;jdh;. >

“miur Ntyp ay;yJ jpahtJk; GiujPh; Ntyp ,y;nyd nkhope;J kd;wj; jpUj;jpr; nrd;wP ut;top . ,d;wt;; Ntyp fhthNjh?” (rpyk;G.fl;.fh.44-47) vd;fpwJ rpyg;gjpfhuf; fhg;gpak;. NkYk; ghz;ba kd;dd;>

“.cr;rpg; nghd;Kb xsptis cilj;jif Fiwe;j nrq;Nfhy; Fiwahff; nfhw;wJ” (rpyk;G.fl;.fh.52-53) vdj; jd; ifiaf; Fiwj;Jf; nfhz;L nrq;Nfhy; FiwahJ nfhw;ifg; ghz;bad; Ml;rp

nra;j nra;jpiar; rpyg;gjpfhuk; Fwpg;gpLfpwJ. ,r;nra;jp Fbkf;fspilNa ed;kjpg;igAk;

Vw;gLj;JfpwJ.

kd;dh;fs; ePjp jtwhky;; Ml;rp Ghpe;j fhuzj;jhy; jhd; mth;fs; kf;fshy;

tho;j;jg;gl;ldh;. mth;fs; ey;y fhhpaq;fisr; nra;Ak; Nghnjy;yhk; kf;fs; tho;j;jpdh;.

ehL eyk; ngwNtz;Lk; vd;W tzq;fpdh;. ,e;jpu tpoh elf;fg;Nghtij kf;fSf;F

36

Kuriwe;J njhptpf;fj; njhlq;fpdhd;. Kjypy; nry;tk; fhuzkhf midtuhYk;

tpUk;gg;gLk; goikahd fhtphpG+k;gl;bdk; tho;f vd;Wk; jdJ nja;tj;ij tzq;fpdhd;.

mjd;gpd; thdk; Kk;khhp nga;f> Nte;jd; nrq;Nfhydhf tho;f vd;nwy;yhk; tho;j;jpdhd;.

kUT+h;ghf;fk;> gl;bdg;ghf;fk; Mfpa ,lq;fspy; ,Uf;Fk; tPuh;fs;> fhty; G+jf;

Nfhapy; gypgPlj;jpd; Kd;Ng nrd;W> kd;dDf;fhf caph;j;jpahfk; nra;fpd;wdh;. mjhtJ

tPu kwth;fs; tpohg;gyp vLj;jdh; vd;w nra;jpAk; ,lk;ngw;Ws;sJ.

mwnewpg;gl;ljhf nrq;Nfhd;ik cilajhf cs;s murghuk; Fwpj;jr;

rpyg;gjpfhuj;jpy; nrq;Fl;Ltd;>

“kiotsq; fug;gpd; thd;Ng ur;rk; gpioAap nua;jpw; ngUk;Ng ur;rk; FbGu Tz;Lq; nfhLq;Nfh yQ;rp kd;gijf; fhf;Fk; ed;Fbg; gpwj;jy; Jd;g yy;yJ njhOjf tpy;” (rpyk;G.fhl;.fh.100-104) kiotsk; FiwAk; NghJk; gpiocaph; nra;Ak; NghJk; mQ;rp mQ;rp thOk; murdpd;

MSk; jpwidr; rhh;e;jJ vd;fpwhh;.

rpyg;gjpfhuj;jpy; gytplq;fspy; Ik;ngUq;FOitAk;> vz;Nguhaj;ijAk; xU

murpay; Rw;wkhff; Fwpg;gpLk; mbahh;f;F ey;yhh;>.fuzj;jpayth;>fUkfhuh;>fdf Rw;wk;>

filfhg;ghsh;> efu khe;jh;> espg;gilj; jiyth;> ahid tPuh;> ,Tsp kwth; vdg;

gl;baypLfpwhh;.

kzpNkfiyapy; murpay; :

kzpNkfiyf; fhg;gpaj;jpy; kzpNkfiyapd; mwr; nraiyf; Nfs;tpAw;w Nrho

kd;dd; jhNd Neubahfr; nrd;W NgRfpd;whd;. ,jid>

“CDil khf;fl; Faph;kUe; jpJntd fiwNthh hpy;yhr; rpiwNahh; Nfhl;lk;

mwNthhf; fhf;fpdd; murhs; Nte;njd;;” (kzp.rpiw.mw. fh154-162) vd;W grpahy; thLNthh;f;F czNt kUe;J vd;gij murDf;Fj; njspTgLj;Jfpd;whs;

kzpNkfiy. ,jid kd;dd; Vw;Wf; nfhz;L jhDk; cjtp nra;tjhff; $wpr;

rpiwNfhl;lj;ij mwf;Nfhl;lkhf khw;wpj; jUfpd;whd;. kd;dd; gy ntw;wpfs; ngw;whYk;>

nrq;Nfhydhf ,Uf;Tk; tPukwth;fs; tpohg;gyp vLj;jdh; vd;w nra;jp kzpNkfiyf;

fhg;gpaj;jpy;>

“KuRfbg; gpL KJFbg; gpwe;Njhd; .jpUtpio %J}h; tho;f nfdNwj;jp

thd Kk;khhp nghopf! kd;dtd; Nfhs;epiy jphpahf; NfhNyh dhFf” (kzp.tpoh.fh.31-34)

37

vd;W Fbkf;fs; kd;did tho;j;jpr; rpwg;G nra;jikiaf; $Wfpd;wd. kd;dDf;fhf

kf;fSk;> kf;fSf;fhf kd;dDk; ,Ue;jik Gydhfpd;wJ.

ngz;bh;; fw;Gk;> kf;fs; eyKk;> ePjpAk;> Neh;ikAk; cila Ml;rpr; rpwg;igNa

rhh;e;J kzpNkfiyf; fhg;gpak; ,Ue;jij>

“khjth; Nehd;Gk; klthh; fw;Gk; fhtyd; fhty; ,d;nwdpd; ,d;why;” (kzp.rpiw.nra;.fh.208-209) mwpa Kbfpd;wJ>

eLTepiy jtwhJ mDgt mwpTld; rpe;jpj;J ePjptoq;fpa fhpfhydpd; eiuKjpatd;

NfhyKk; jd;kfidf; Njh;fhypy; fplj;jpa NrhoidAk;>kzpNkfiy fhg;gpaj;jpy;

“kfid Kiw nra;j kd;dtd;” (kzp.rpiw.nra;.fh.210)

vd vLj;Jiuf;fpwJ. kd;dh;fspd; mwk;Ghpr; nray;fisr; rhd;Wfspd; top tpsq;FfpwJ

,e;jpu tpohit vg;gb elj;JtJ vd;gijg; gw;wp Ml;rpahsUk;> mikr;rh;fSk;>

nghJkf;fSk; xd;W $b KbT nra;fpd;wdh;. cyiff; fhf;fhky; iftpl;L eP kl;Lk;

cah;e;j cyfila tpUk;Gthahdhy; mJ ePjp md;W> caph;fs; vy;yhk; khz;Lk;

gbtpl;L ePkl;Lk; ,yhgj;ij tpUk;gpatdhf Mtha;. ,r;nray; jd; caph;gLk; Jd;gj;jpw;F

,uq;fhky;> gpwcaph;fspd; Jd;gj;ijg; Nghf;fpf; fhg;ghw;Wk; Gj;jUila mwnewpahfhJ.

mjw;F khwhdjhFk;. ,t;thW mikr;rh; murDf;F mwpTiu $Wtij>

“kd;;Daph; Kjy;td mwKkP jd;;why; kjpkh Nwhh;e;jid kd;dt ! ntd;Nw KJnkhop $w” (kzp.MG.kzp.fh.117-119) vd;W kzpNkfiy tpsf;fp epw;fpwJ.

murpay; newpKiwfisr; Rl;bf;fhl;b Ml;rpahsd; gz;Gfisf; fhg;gpaq;fs;

typAWj;Jfpd;wd.fhl;rpf;F vspatd;vDk; kjpg;G ,ul;ilf; fhg;gpaq;fspYk;

fhzg;gLfpd;wd. ePjp jtwpa kd;dplk; Neubahf thjhba ngz;> jq;fspd; Fiwfs;>

Njitfs; ndg; gytw;iwAk; murdplk; Nfl;Fk; JzpT Mfpatw;iwnay;yhk;

MuhAk;NghJ Ml;rpahshpd; rpwg;G czug;gLfpd;wJ. Fw;wq;fs;> jz;lidfs; tiuaiw

ngwhky; ,Ue;j nra;jpfisf; fhg;gpaq;fs; Rl;bfhl;Lfpd;wd. jd;id ehb

te;jth;fSf;F cjtp nra;J gpwh; eydpy; mf;fiwAlDk;> ghpTlDk; ele;J nfhz;l

fhg;gpafhy rKjhaj;ij ed;F czu Kbfpd;wJ.

mbf;Fwpg;G

1.f;hpahtpd; jkpo; mfuhjp g.38

2.eh;khjtpd; jkpo; mfuhjp g.44

3.mg;Jy; uFkhd; fk;gdpd; murpay; Nfhl;ghL g.11

38

39

rh.];nly;yh nfsut tpupTiuahsH> jkpo;j;Jiw> muR kfspH fiyf;fy;Y}hp> epyf;Nfhl;il.

njhd;ikrhH E}y;fspy; Ntw;Wikf; $Wfs;

cyfj;jpd; Kjd;ikahfTk;> njhd;ikahfTk; jkpo; nkhopapy; cyh tUfpd;w

E}y; njhy;fhg;gpaKk;> rq;f,yf;fpaKkhk;. ,t;tpU E}y;fSk; xd;iw xd;W rhHe;J

gilf;fg;gl;Ls;sd. ,uz;Lk; xd;NwhL xd;W neUq;fpaj; njhlh;Gilajhf ,Ue;jhYk;>

,uz;L E}y;fisAk; cw;WNehf;fp MuhAk; nghOJ rpw;rpy ,lq;fspy; NtWghLfs;

fhzj;jhd; nra;fpd;wd. mj;jifa NtWghLfis Muha;tNj ,jd; Nehf;fkhf

mikfpd;wJ. fsTf; fhyj;jpy; njhy;fhg;gpaj;jpy; kl;Lk; Rl;bAs;s epfo;Tfs;

rq;f,yf;fpaj;jpy; ,lk;ngwhj nra;jpfisg; gpd;tUkhW tpsf;ff; fhzyhk;

jiytDf;Fhpa Iak;

,aw;ifg;Gzh;r;rp Fwpj;J fhl;rp> Iak;>njspT vd;w %d;W gFjpfisj;

njhy;fhg;gpah; tpsf;Ffpd;whH. fhl;rp vd;gJ jiykf;fs; Kjd;Kjypy; re;jpj;Jf;

nfhs;Sk; epfo;T. Iak; vd;gJ jiytpiaj; jiytd; nja;tk; Nghd;w gythfg; ghtpj;J

,ts; kq;ifjhNdh vd re;Njfpj;J epw;gJ MFk;. njspT vd;gJ jiykf;fs;

cz;ikawpe;J Iaj;jpypUe;J tpLgl;l epiy vd;W njhy;fhg;gpah; tpsf;Ffpwhh;. ,tw;wpy;

Iak; vd;Dk; gFjpapy; jiytDf;F kl;LNk Iag;gLjy; cz;L vd;Wk; mtd;

jiytpia nja;tNkh gpwNth vd re;Njfpg;ghd; vd;Wk; mr;re;Njfk; rpwg;gpw;FhpaJ

vd;Wk; njhy;fhg;gpah; Rl;L;fpwhh;. ,jid njhy;fhg;gpak; nghUsjpfhuk; fstpay; 3 –

MtJ E}w;ghtpy; Fwpg;gpLfpd;whH. Iak; vd;gJ Njhw;wk; ngWtjpy; kl;LNk

fhl;lg;gLfpd;wJ. ,e;j Iaj;jpypUe;J tpLgLtjw;Fj; njspT ngWk; fUtpfshfj;

njhy;fhg;gpak; nghUsjpfhuk; fstpay; 4 –MtJ E}w;ghtpy; Fwpg;gpLfpd;whH.,t;tpU

E}w;ghf;fisAk; cw;W Nehf;Fifapy; jiykfdplj;jpy; Iak; rpwe;jjhf ,Uj;jy;

Ntz;Lk; vdg; nghUs; czh;j;jg;gLfpd;wJ. ,jid nka;g;gpf;Fk; tpjkhf njhy;fhg;gpak;

nghUsjpfhuk; nghUspaypy; 42 –MtJ E}w;ghTk; cWjpg;gLj;JfpwJ. ,Nj fUj;ijj;

jhq;fp

mzq;Fnfhy; Ma;kapy; nfhy;Nyh fdq;Fio

khjh;nfhy; khYnkd; ndQ;Nr (Fws;.1081)

vd;Dk; Fws; ntspg;gLj;JfpwJ.

jiykfDf;Ff; Fwpj;Jr; nrhd;d Iak; jiykfSf;F cz;lh vd rpe;jpj;jhy;

Iak; vd;gJ jiykfDf;F kl;LNk nghUe;Jk; vd mikfpwJ. njhy;fhg;gpah; $Wk;

Iak; rq;f ,yf;fpaj;jpy; ,y;iy. jiytd; jiytpaplj;jpy; xU rpwe;j fsT

epfo;tpw;Fj; jiytdplk; Iak; Njit vd;W njhy;fhg;gpah; $Wfpwhh;. Mdhy; rq;f

40

,yf;fpak; Iak; ,d;wpf; fsT epfo;r;rpfisg; gilj;J ehlfg;Nghf;fpy; mike;jpUg;gJ

njhy;fhg;gpaj;jpypUe;J NtWgLtjhf mikfpd;wJ. rq;f ,yf;fpaj;jpy; jiytdplj;Nj

Iak; Njhd;WtJ Nghd;w ghly;fs; ,y;yhky; ,Uf;ff; fhuzk; rq;fg;Gyth;fs; rpwe;j

fhjiy ve;j epyj;jpy; GFj;jpr; nrhd;dhy; mf;fhjy; rpwg;Gg;ngWk; vd mwpe;J

mtw;iwf; FwpQ;rpj; jpizapy; Nrh;j;Js;sdh;.

fstpy; jiytdpd; epfo;T

jiytd; jiytpaplj;jpy; fsT Kbe;j gpd;dH jpUkzk; eilngWtjw;fhd

fhuzq;fspy; xd;whfj; jiytdplk; ngha; NgRk; Kiw $wg;gLfpwJ.ngha;Ak; tOTk;

Njhd;wpa gpd;dh; vd;W njhy;fhg;gpak; nghUsjpfhuk; fw;gpaypy; 4:1- MtJ mbapy;

Fwpg;gpl;Ls;shH. ,y;tho;f;if ele;jgpd;G jiytd; jiytpia vdf;Fj; njhpahJ vd;W

ngha; $wp> jiytpaplj;J ,Ue;j epfo;it kwe;J Ntw;W tiuT Nkw;nfhz;ljhfr;

Rl;lg;gLfpd;wJ. mjhtJ jiytdplk; ngha;Ak; tOTk; Njhd;wpa gpd;dNu

nghpNahh;fshy; „fuzk;‟ vd;w Kiw mikf;fg;gl;Ls;sJ. ,q;qdk; njhy;fhg;gpak;

$WtJ Nghd;W jiytd; fstpdpy; jiytpia Vkhw;wpajhfNth jiytpiaj; jtph;j;J

NtW ngz;iz kzg;gjhfNth rq;f ,yf;fpaj;jpy; ghly;fs; ,lk;ngwtpy;iy. Mdhy;

jiytd; Vkhw;wptpLthNdh vd;gJ Nghd;w gak; fye;j czh;T epiyahdJ

jiytpaplKk;> NjhopaplKk; ,Ug;gJ Nghd;w ghly;fs; cs;sd. rq;f ,yf;fpaj;jpy;

FWe;njhif 25-MtJghlypy; jiytd; vd;idf; fstpy; $ba Ntisapy; vd;Dld;

ahUk; ,y;iy. fs;tdhfpa mtd; kl;Lk; jhd; ,Ue;jhd;. mtd; ngha; nrhd;dhy; ehd;

vd;d nra;aKbAk;? vd;W jiytp Gyk;GtJ Nghd;W fUj;J mikfpd;wJ. jiytd;

vd;id Vkhw;wpdhd; vd;w fUj;Jg; Gyg;gltpy;iy. Iq;FWE}w;wpy; 287-MtJghlypy;

jiytd; jiytpaplk; Fwpj;j ehspy; tiue;J nfhs;Ntd; vd cWjp $wpdhd;. Mdhy;

nrhd;d ehspy; jpUkzk; nra;atpy;iy. kPz;Lk; mj;jifa cWjpnkhopiaj; Njhopaplk;

$wpdhd;. mg;NghJ Njhop jiytdplk; Ke;ija epfo;it vLj;Jf; $wpj; jiytidg;

gopj;Jiuf;fpd;whs;. jiytpapd; epiyikia tpsf;fpj; jpUkzj;jpw;Fj; jiytid

tiuT flhTk; Nghf;F Njhopaplk; fhzKbfpd;wJ. jiytd; jiytpiaj; njhpahJ

vd;W nrhd;djhfg; nghUs; ,y;iy. mg;gbNa ,Ug;gpDk; kPz;Lk; jiytpapd; epidthfj;

Njhopia ehl Ntz;ba mtrpak; jiytDf;F ,y;iy vd;gJ Gyg;gLfpd;wd.

,jd;top rq;f,yf;fpag; ghly;fspy; jiytp> Njhop MfpNahuJ $w;wpd; thapyhf

gphpe;Jnrd;w jiytd; tUtjw;Ff; fhyjhkjk; Mfpd;wJ. mjid Kd;epWj;jp mtd;

jk;ik Vkhw;wp tpLthd; vd;fpw mr;rczh;T jiytpf;F Vw;gLfpd;wJ. mt;thW jiytd;

jd;id Vkhw;wptplf;$lhJ vd;gjw;fhd Kd;Vw;ghL nra;Ak; tpjKk; ntspg;gLj;Jfpd;wJ.

njhy;fhg;gpak; Rl;bAs;s „ngha;Ak; tOTk; Njhd;wpa gpd;dh;‟ vd;Dk; fUj;jpw;F Vw;gj;

jiytpia Kw;wpYk; Vkhw;wpa epfo;TfSk; Ntw;WtiuT nra;Jnfhz;l epfo;Tk;

jiytdplk; epfo;e;jjhfr; rq;f ,yf;fpaj;jpy; ghly;fs; ,lk;ngwtpy;iy.

41

fstpy; jiytdpd; guj;jik

jiytDf;Fj; jiytpAld; jpUkzk; eilngWk;Kd;G jiytDk; jiytpAk;

fsTf; fhyj;jpy; nfhs;Sk; guj;jik gw;wpj; njhy;fhg;gpah; tpjptFf;fpd;whh;. ,jidj;

njhy;fhg;gpak; nghUsjpfhuk; fstpay; 21MtJ E}w;ghtpy; 35-36 thpfs; jiytp

$Wtjhf mike;Js;sJ. ,tw;wpy; jd;Dld; fhjy;tho;Tk; jhd; my;yhj gpw ngz;zplk;

guj;jikAk; jiytd; nfhz;Ls;shd; vdj; jiytp tha;nkhopfpd;w fUj;Jg;

Gyg;gLfpd;wJ. mjhtJ fstpdpy; jiytDf;F guj;jikxOf;fk; cz;L vd;gijj;

jiytp $w;wpd; thapyhfj; njhy;fhg;gpak; Rl;bf;fhl;Lfpd;wJ.

rq;fg;ghly;fspy; jiytd; guj;ijaplk; cwTnfhz;l nra;jp gw;wpNah

guj;ijapw;gphpT nfhz;L kPz;lijr; Rl;Lk; nrhw;fNsh fhzg;gltpy;iy. guj;jik

vd;gij fw;Gj; njhlh;ghd E}w;ghf;fspy; njspTgLj;Jk; njy;fhg;gpak; fstpdpYk; $wf;

fhuzk; fstpy; jiytDf;Fg; guj;jik cz;L vdf; $WtJk; rq;f,yf;fpaj;jpy;

jiytdpd; guj;ijaw;gphpT Nghd;w ghly;fs; mikag;gltpy;iy vd;gJk;

NtWghl;Lf;Fhpajhf cs;sJ. ,jd;%yk; rq;f,yf;fpaf; fhyfl;lj;jpy; jiytd;

jiytpf;F fsTf; fhyj;jpy; Raf;fl;Lg;ghLfs; tFf;fg;gl;Ls;sd vd;gJ Gyg;gLfpd;wJ.

Mdhy; njhy;fhg;;gpauJ fhyj;jpy; Raf;fl;Lg;ghLfs; mw;w epiyiaf; fhzKbfpd;wJ.

fstpy; nrtpypapd; jpwk;

fsT tho;f;ifapy; $w;W epfo;j;JthH vd;W njhy;fhg;gpah; vq;Fk; jkJ E}ypy;

Rl;ltpy;iy. Mdhy; nrtpypj;jha;f;Fg; gjpd;%d;W #oy;fspy; $w;W epfOk; #oiyj;

njhy;fhg;gpak; nghUsjpfhuk; fstpaypy; 24-MtJ E}w;ghtpy; Fwpg;gpLfpd;whh;. vdpDk;

mg;ghly;fs; midj;Jk; Njhop> jiytd; $w;whfNt mikfpd;wd. xUghly;$l

nrtpyp$w;whf mikatpy;iy. rq;f ,yf;fpaq;fspYk; njhy;fhg;gpah; fhl;ba

nrtpypf;Fhpa gjpd;%d;W epiyfSk; nrtpyp$w;wpd; thapyhf mikatpy;iy. vdNt

njhy;fhg;gpaj;jpy; ew;wha;f;F fstpy; tiuaWf;fg;gl;l gjpd;%d;W epiyfSk;

rq;f,yf;fpaj;jpy; Neh;f;$w;whf mikahky; jiytd; kw;Wk; Njhop vd;w gpwh;$w;whfNt

mike;Js;sd vd;gJk; Gyg;gLfpd;wJ. ,it ,e;j ,UE}y;fspilNa cs;s

Ntw;Wikiaf; fhl;LfpwJ

Jizapd;wpf;$Ljy;

jiytDk; jiytpAk; ahUila cjtpAk; ,d;wpf; fsTtho;f;ifapy; jdpikapy;

$Ljy; rpwg;G vd;W njhy;fhg;gpah; Fwpg;gpLfpwhh;. ,jid njhy;fhg;gpak; nghUsjpfhuk;

fstpay; 29-MtJ E}w;ghthy; mwpaKbfpwJ. rq;f ,yf;fpaj;jpy; jiykf;fs; jhNk $b

fstpidr; rpwg;gpf;Fk; ghly;fs; FiwthfTk;> Njhop> ghzd; Nghd;Nwhh; cjtpAld; $b

kfpOk; ghly;fs; kpFjpahfTk; gilf;fg;gl;Ls;sd.

njhy;fhg;gpaj;jpy; fsT rpwg;gjw;F ahUila JizAkpd;wpj; jiykf;fs; $LjNy

rpwg;G vd;W tiuaWf;fpd;wJ. rq;f,yf;fpak; mjw;F khwhf ghq;fd;> ghq;fp KjypNahhpd;

42

Jizfspd; thapyhfNt fstpidr; rpwg;Gwr; nra;fpw epiyapid ntspg;gLj;Jfpd;wJ.

,q;qdk; njhy;fhg;gpaj;jpypUe;J rq;f ,yf;fpak; rw;W NtWgl;L cs;sijf;

fhzKbfpd;wJ. vdNt njhy;fhg;gpak; kw;Wk; rq;f ,yf;fpaf; fhyq;fis ehk;

fhZk;NghJ ,d;iwa fhyr; #oyhdJ ,U epiyfisAk; ikakpl;ljhfNt cs;sJ.

mjhtJ fsT tho;f;ifapy; <LgLk; jiytd; jiytp Mfpa ,UtUk; fhjy; Njhd;Wk;

tiuapYk; gpwuJ mjhtJ ghq;fd;> Njhop Nghd;NwhuJ cjtpia ehLfpd;wdH. gpd;dH

mtHfis tpLj;Jf; fsT tho;it (fhjy; tho;it) Nkw;nfhs;fpd;wdH. NkYk;

jpUkzj;jpw;F ,ilA+W Vw;gLk; NghJ kPz;Lk; mtHfsJ cjtpia ehLk; epiyiaf;

fhzKbfpd;wJ.

ghq;fd;

ghq;fw; $l;lk; vd;gJ ghq;fdJ Jizahy; jiytd; jiytpiaf; $LtJ MFk;.

ghq;fw; $l;lj;ij njhy;fhg;gpah; ,uz;L epiyfspy; $Wfpd;whh;.njhy;fhg;gpak;

nghUsjpfhuk; fstpaypy; 11:8-9 vd;Dk; mbfspYk;> 13-MtJ E}w;ghtpYk;; tpsf;fpr;

nry;fpd;whh;.

ghq;fidg; gw;wp tpsf;Fk; njhy;fhg;gpah; Fw;wk; fhl;ba thapy; ngl;gpDk; vd;w

thpapy; jiytdpd; Fw;wj;ij vLj;Jiug;gtd; ghq;fd; vd;w fUj;ijf; Fwpg;gpLfpd;whH.

,tw;iwf; fhZk;NghJ ghq;fd; jiytdpd; fhjy;> Nrhh;T> tUj;jk;> mjdhy; Vw;gl;l

Neha; Nghd;wtw;iw vLj;Jf; $Wgtdhf tpsf;Ffpd;whd;. mjhtJ Kjypy; ghq;fd;

NgRgtdhfTk;> mjw;Fj; jiytd; $w;W epfo;JgtdhfTk; mike;Js;sij

mwpaKbfpd;wJ.,f;fUj;ijf; nfhz;L rq;f ,yf;fpaj;ij MuhAk;NghJ cs;sg; Gzh;r;rp

mstpNyh nka;AW GzHr;rpapd; gpd;dNuh fyf;fk; va;jpa jiykfNd ghq;fd; Jizia

ehLtjhfg; ghly;fs; gilf;fg;gl;Ls;sd.

ghq;fw; $l;lj;jpy; ghq;fd; Kjypy; Ngrpajhfr; rq;f ,yf;fpaj;jpy; ve;jg;

ghly;fSk; ,y;iy. khwhfj; jiytd; $w;Wf;Fg; ghq;fd; gjpy; $WtjhfNt ghly;fs;

cs;sd. jiytd; $w;Wf;Fg; ghq;fd; Kjypy; fbe;J nfhs;gtdhfTk; gpd;dH

cld;gLgtdhfTk; ghly;fs; mikfpd;wd.FWe;njhif 280-MtJ ghlypy; „,bf;Fk;

NfspH Ek;Fiwahf‟ vd;W jiytd; $WtjpypUe;Jjhd; mwpaKbfpwJ. ghq;fd;

,bj;Jiuj;jikf;Fg; ghly;fs; rhd;Wfs; ,y;iy.

rq;f ,yf;fpaq;fspy; fhjy; kPJ cs;s jiytdpd; Fw;wq;fis vLj;Jf; $WtNj

ghq;fdpd; Nehf;fkhf ,Uf;fpd;wJ. mt;thW vLj;Jiug;gJ ,yf;fpaq;fisr; Ritgl

gilg;gjw;Fj; jilahf mikAk;. vdNtjhd; ghq;fdpd; $w;W njhlf;fj;jpy; cs;s

ghly;fs; rq;f ,yf;fpj;jpy; kpFjpahff; fhzg;gltpy;iy.

rq;f ,yf;fpag; ghlypd; njhlf;fj;jpy; ghq;fw;$l;lk; gw;wpa nra;jpfis

,yf;fpaq;fspy; nghpJk; fhz ,ayhikf;Fj; jiytd; ghq;fd; MfpNahhpd; ,t;Tzh;T

Kuz;gl;l fUj;Jf;fs; (gjpy;$Wjy;> fbjy;> cld;gly;) fhuzkhf ,Uf;fyhk;. ghq;fw;

$l;lj;ij MuhAkplj;J njhy;fhg;gpak; ghq;fd; Fw;wk; $wpatopapy; jiytd; gjpy; $w;W

43

epfo;j;Jtjhff; $WtJk;> rq;f ,yf;fpak; jiytd; $w;Wf;Fg; ghq;fd; gjpy;

$WtjhfTk; mikfpd;wJ. ghq;fd; Kjypy; $w;W epfo;j;jpajhf rq;f,yf;fpaj;jpy;

ghly;fs; ,y;iy vd;gJ mwpaKbfpd;wJ. jiykf;fspd; fsT tho;f;ifapy; ghq;fd;

Kf;fpa ,lj;ijg; ngWfpd;whd;. vdpDk; njhy;fhg;gpak; ghq;fid Kjd;ikg;gLj;jpf;

fhl;Lfpd;wJ. Mdhy; rq;fg;ghly;fs; jiytDf;Fg; gjpy; $Wk; gilg;ghfg; ghq;fidg;

gilj;jpUf;fpd;wJ.

Njhopf;Fhpa kjpAlk;ghL

rq;f ,yf;fpaj;jpy; fsT tho;f;ifapy; Njhop jdpj;j ,lj;ijg; ngWfpd;whs;.

jiytpAk; NjhopAk; jdpj;jpUf;Fk; NghJ jiytd; te;J ciuahLtjhf fhl;rp

mikf;fg;gl;L mjd;%yk; jd; mwptpidg; gad;gLj;jpj; jiytDf;Fk; jiytpf;Fk;

fhjy; cs;sikiaj; Njhop jdJ kjpEl;gj;jhy; mwpe;J nfhs;SjiyNa „kjpAlk;ghL‟

vd;W njhy;fhg;gpak; nghUsjpfhuk; fstpay; 37-MtJ E}w;ghtpy; Rl;bf;fhl;LfpwhH.

,tw;wpy; kjpAlk;ghL vd;gJ %d;whtJ epiyahf Njhopf;F chpajhff; tpsf;FfpwhH.

njhy;fhg;gpak;> rq;f,yf;fpak; Mfpa ,uz;bYk; jiytd; jiytpiaf; fhl;bYk;

Njhopia Kd;dpWj;jpg; ghbAs;sjdhy; NjhopNa fsTtho;f;ifiar; rpwf;fr; nra;tijf;

fhzKbfpd;wJ. ,jdhy; fstpdpy; Njhop Kjd;ikg; ghj;jpukhfg; gilf;fg;gLfpd;whs;.

„kjpAlk;gLjy;‟ vd;gJ Njhopf;F xU nghpa epfo;thf tpsf;fg;gLfpwJ. mit

mt;thW tpsf;fg;gltpy;iynadpy; jiytd; jiytpauJ fhjy; rpwg;Gg; ngwhky;

Ngha;tpLk;. vdNtjhd; rpy fhjy;fisj; NjhopNa jd; kjpEl;gj;jhy; mwpfpd;whs;.

mq;qdk; mwpe;j fhjiyf; $l;bf; fw;gpf;Fk; jpwikAk; Njhopf;Fhpajhf gilj;J

„,UtUk; cs;top mtd;tuTzh;jy;‟ vd;W Njhop jhNd fsit mwptjhfj;

njhy;fhg;gpak; gilj;Js;sJ.

rq;f ,yf;fpak; Gyth;fshy; nghpa fly;Nghd;w fhjy; czh;Tfisg; gilj;Jf;

fhl;bAs;sik ehk; midtUk; mwpe;j cz;ik. ,e;j fhjy; tho;tpw;Fj; Njhop

Kjd;ikg;gLj;jg;gl;l NghjpYk; njhy;fhg;gpak; Njhopf;F tFj;j Kf;fpa epfo;thd

„,UtUk; cs;top mtd; tuTzh;jy;‟ vd;w fhl;rpmikg;G rq;f ,yf;fpaq;fspy; ew;.213>

Iq;.176 vd;w ,uz;L ghly;fspy; kl;Lk; rpj;jhpf;fg;gl;bUf;fpd;wJ.

njhy;fhg;gpak; Njhopf;Fhpa kjpAlk;ghl;by; „,UtUk; cs;top mtd;tuTzh;jy;‟

vd;gij kpifg;gLj;jp ntspg;gLj;jpapUf;fpd;wJ. Mdhy; rq;f ,yf;fpak; ,uz;L

ghly;fspy; kl;LNk gilj;jpUf;fpd;wJ. rq;f ,yf;fpak; Njhopf;Fhpa kjpAlk;ghl;by;

mjpfkhf Kf;fpaj;Jtk; nfhLf;fhky; ,Ue;jpUg;gJ Gydhfpd;wJ.

,q;qdkhf rq;f ,yf;fpak; njhy;fhg;gpak; ,uz;Lk; jiytDf;Fhpa Iak;>

Jizapd;wpf; $Ljy;> jiytdpd; guj;jik>nrtpypapd; jpwk;> Jizapd;wpf; $Ljy;>

ghq;fdpd; nray;ghL> Njhopf;Fhpa kjpAlk;ghL Kjypa Ntw;Wikf; $Wfisg;

ngw;wjhfj; jpfo;fpd;wd.

44

VERSATALITY OF CULTURE IN A PASSAGE TO INDIA

- Dr.S.S.SRINIVASAN

Department of English

Government Arts College for Women

Nilakotai.

Keeping a big nation as that of India with a population of varied culture, religion,

language, ethnicity and caste and creed constantly under its sway demanded very many

techniques from the imperial power. Hence, the British always sought to supplement their control

of the Indian empire through a web of hegemonic practices involving subtle strategies of cultural

manipulation. Knowledge of Indian culture, ideology, ethnology, ethnography, anthropology and

the geography of India helped the British colonizers to build up a powerful discourse. Very often

books of science, fiction, technology and even the Bible were used as epistemological techniques

for control. Books of literature also helped furnish the bourgeois epistemological knowledge for

colonialists. They were used to exploit the people intellectually. They were also used to shape the

style of thoughts of the colonized. Colonial literature mostly reflects the attitude of colonial

machinery and encompasses cruelty and violence. The literature misled natives by making them

look for a Utopia and not the real world of bourgeois evil practices. Colonial texts usually evaded

the issue of contributing to the decadence and decay of native values and ignored the skepticism

of the natives towards the colonial forces that were oppressing them. A Passage to India depicts

both the deterioration and the formation of relationships between Anglo-Indians and natives.

Forster demonstrates how these repeated misunderstandings become hardened into

cultural stereotypes and are often used to justify the uselessness of attempts to bridge the cultural

gulfs.Foster ends his novel A Passage to India with the reconciliation of Aziz and Fielding. The

final message of the novel is that though Aziz and fielding want to be friends, racial difference

prevents their friendship. Even if the final lines of the novel are pessimistic, Forster does leave

open the possibility of a cross-cultural friendship between Fielding and Aziz to a certain moment

in the future.

Forster's message changes throughout the course of the novel. At the beginning of the novel,

characters such as Fielding and Aziz are the evidence of Forster's belief that with goodwill,

intelligence and respect, all individuals can be front one another. But, in the final scenes, the

natural landscape of India itself seems to rise up and divide Aziz and Fielding from each other.

Forster suggests that though men may be well-intentioned, circumstances such as cultural

difference and the interference of others can conspire to prevent their union.

45

In the eyes of westerners, India was viewed as a mysterious, but enchanting place, and it was

portrayed as a land of riches and a land of mystery. Such a view was especially harbored by Ms

Adela Quested, who came “to see the real India” and looked forward for an adventure in India.

Quested‟s “real India,” which involved in interacting with and appreciating the natives, served as

the antithesis to the India of the British expatriate, as symbolized in the Chandrapore Club, which

was in accessible to Indians, as members or even guests. This club was regarded as the symbol of

British aloofness among the populace of British India. However, there were some characters in

this story like Adela Quested and Mrs. Moore, who wanted to treat the natives equally.

Foster discusses the possibility of interaction between the East and the West. The concept

of the West on the East is that they are simply barbarians, uncultured and illiterates and meant to

be ruled and controlled. The West considers the East as Orients and literally an image of the

“Other”. The basic idea of orientalism is that east is what is not west but it is mysterious, holy,

dark, strange, attractive and barbaric. In their concept and perception, The East is unreasonable

and primitive people there are not themselves and therefore need to be ruled.

Right from the beginning till the end, “A Passage to India”, picturizes India as a holy

nation, known for its ageless history, ruled by the British people (the West). The meeting of Aziz

and Mrs. Moore displays the distinction between two countries and civilization, in general the

Eastern and the Western and the upper hand of one over the other. West cannot identify,

understand and agree with the perfection of the East. The British fail to agree with India and

Indians that they are perfect in their own style and tradition, on whom the West (British) is just

enforcing its power and strength in a brutal manner.

A Passage to India is set at the beginning of India‟s movement towards independence, in a

time when “Congress abandoned its policy of co-operation with the British Raj to follow

Ghandi‟s revolutionary call for non-violent revolution”. (Wolpert 301) By 1921, some 20,000

Indians were in prison (Wolpert 303). Interestingly, Forster‟s novel seems, to a great extent, to

have little sense of this instability, and as much as several characters may provide a voice of

dissent, the position of the British Raj in India is at no point challenged per se. This may be due to

the fact that though Forster‟s interest may have been in cultural India, the recognition of a

Colonial Other, on which the narrative tensions are based, depends upon a perspective from one

side of the divide. Novels such as Raja Rao‟s Kanthapura, which describes the independence

movement from the perspective of Indian villages, present the agents of the British Raj as the

Other. Indeed, there is a very little personal presentation of the British at all, presented as they are

46

as a remote power:

And from the bamboo cluster the voices of women are heard, and high up there, on

the top of the hill, the Sahib is seen with his cane and his pipe, and his big heavy

coat, bending down to look at this gutter and that.

(Rao 55)

For Forster‟s novels, which are confined to a British perspective, the Other will inevitably remain

the Indian. India is really ancient. They present caves as “dark caves”. Even when they open

towards the sun, very little light penetrates down the entrance - tunnel into the circular chamber.

This states that the country is still in darkness without light of freedom and selfhood. Cave

represents the country.

While Aziz talks to Mrs. Moore and Miss Quested, they converse about the Mughal

emperors, Babur, Humayun, Akbar and Aurangzeb.

“Akbar never repented of the new religion he invented instead of the Holy Koran.”

“But wasn‟t Akbar‟s new religion very fine?”

“Miss Quested, fine but foolish, you keep your religion, I mine.”

Here, Forster brings to light the mindset of the Indian who with the character of Aziz,

strongly condemns the imperial power of the rulers, because, the British people want to enforce

their culture and civilization, but through Aziz, Forster openly brings out the idea of the natives

that their own practice and ideology is far better than the ruler‟s (British) concept and practice.

The echo from the caves is like the voice of the oppressed native people. They try to

convey their emotion that no where the natives remain silent, definitely they will rage against

their difficulties. The reference to Chandrapore as a place of immense heat dramatises the

consequences or the impact of the rulers over the natives.

The novel talks about the possibility of friendship between an Englishman and an Indian,

which will bring the possibility of friendship between the East and the West. The rulers do not

have any real sympathy for the sufferings of the people meant to be ruled. They simply oppress

the natives with their language, showy culture and influencing tactics. They never concentrate on

empowering and enhancing the lives of the natives, instead they wish and plan to loot the wealth,

enduring knowledge, etc.

When Aziz and Fielding meet one another again in the last section of the novel, there is

no joyous reunion between the friends. Even when the misunderstanding is cleared, the

conversation goes badly and the meeting ends with a complete separation between the two men.

47

Aziz is paradoxically the instrument of reconciliation between himself and the British people.

In the last section, Aziz and Fielding start talking frankly and intimately about politics.

They realize that their characters and way of life have changed radically for them to be able to

continue as close friends. They have never been closer than now; and they speak more as an

Englishman and an Indian than as Fielding and Aziz; both are angry and excited. Aziz begins to

shout, Fielding mocks at him and Aziz is enraged. They bring their horses nearer to embrace each

other, but the horses swerve apart. This suggests that sub-human India is hostile to inter-racial

friendships and therefore their union is transitory.

Conclusion

A Passage to India can be roughly divided into three long sections, which correspond, in

Forster‟s viewpoint, to the three seasons of the Indian year-Mosque (the cool weather), Caves (the

hot weather), and Temple (the rains). A Passage to India is an emotional and deeply personal

story of love and class -struggle in 1928 of India. Adela Quested travels to India to visit her

fiancee who was the city Magistrate of fictional city of Chandapore. She is on an adventure

accompanied by his mother, Mrs Moore, an elderly woman who is appalled at the treatment of

Indians by the British who rule and occupy Chandapore. Both women befriended with a young

Indian man; Dr. Aziz, who overstepping the accepted boundaries between the classes, invited the

women on a picnic excursion to the Marabar Cave. In a strange turn of events, the young doctor is

accused of attempting to rape Miss Quested. What actually did happen in the Marabar Cave

remains the central riddle of this lush engrossing novel.

REFERENCES

Conrad, Joseph, Heart of Darkness, (Great Britain: Penguin, 1975)

Forster, E.M., A Passage to India, (St Ives: Penguin, 2005)

Rao, Raja, Kanthapura, (Delhi: Oxford University Press, 2006)

Wolpert, Stanley, A New History of India, (New York: Oxford University Press, 2004)

Duff, David (2000) Modern Genre Theory. London: Longman.

48

Removal of Pb(II), Cu(II) and Ni(II) ions from aqueous solution using NaOH treated Guazuma Ulmifolia

seeds

P. Raj kumar

aDepartment of Chemistry, Government Arts College for Women, Nilakottai – 624 208, Tamilnadu, India

Abstract

The aim of the present work is to develop inexpensive and effective adsorbent from the natural waste such as Guazuma

Ulmifolia seeds, by treating this material with sodium hydroxide (NaOH). The surface modified Guazuma Ulmifolia

seeds by base NaoH (SMGU-B) was utilized as an effective adsorbent for the removal of metal ions such as Pb(II),

Cu(II) and Ni(II) from the aqueous solution. Batch adsorption studies were carried out to evaluate the effects of

parameters, such as the solution pH, adsorbent dose, contact time, initial metal ion concentration and temperature. With

an increase in the pH, the adsorption of metal ions increases and reaches equilibrium at pH 5.0. An increase in the

percentage removal of metal ions is seen with an increase in the adsorption dosage. Kinetic results show that the metal

ion-adsorbent system can be described by the pseudo-second order equation which provides best correlation of the

experimental data. The adsorption of metal ions onto adsorbent was found to be controlled by surface diffusion in the

earlier stages followed by pore diffusion in the later stages. The applicability of the Freundlich and Langmuir

adsorption isotherm equations to the metal ion-adsorbent system indicates that the monolayer adsorption and

heterogeneous surface conditions exist under the studied experimental conditions. An increase in the temperature shows a

decrease in the removal of the metal ions from the aqueous solution. Desorption studies show the possibility of regeneration

and recovery of the metal ions from the adsorbent.

Keywords: Adsorption, SMGU-B, Pb(II), Cu(II), kinetics, isotherms.

Author to whom all correspondence should be addressed: e-mail: krprajkumar@gmail.com; Phone: +91 89035 29517

1. Introduction

Water pollution has reached worrying proportions worldwide. In recent years, increase in population, urbanization and

industrialization is bringing about a serious threat to all natural water resources. The increasing uses of synthetic

49

organic substances have brought serious and adverse effects on freshwater bodies. These pollutants find their way into

aquatic ecosystems, such as rivers, ponds and lakes, and pose a risk to the health of human beings and also destruct the

above ecosystem. Water pollution in many regions has increased due to the discharge of industrial waste, especially

that containing heavy metals into water bodies. Heavy metal is a general collective term, which applies to the group of

metals and metalloids, with an atomic density greater than 4000 kg m-3

, or 5 times more than water (Garbarino et al.).

They are found naturally as components of the earth‟s crust. The most toxic forms of these heavy metals are their ionic

species in their most stable oxidation states; eg. Pb(II), Cu(II) and Ni(II) ions in which, they react with the body‟s bio

molecules to form extremely stable biotoxic compounds, which are difficult to dissociate and non-biodegradable in

nature (Duruibe et al. 2007). It is very difficult for anyone to avoid exposure to the harmful heavy metals in day to day

life. Effluents from various industries such as mining, metal plating, tanneries (Ozcimen et al. 2009), battery, silver

refineries, paints and pigment industries (Kadirvelu et al. 2001; Ajmal et al. 2000), contain heavy metals in huge

proportions. Heavy metal accumulation in the cells and tissues of living organisms results in a variety of diseases and

disorders (Namasivayam et al. 1995).

Lead occurs in +2 and +4 oxidation states. Pb(II) is more common and reactive form of lead. The presence of lead in

the environment arises from both natural and anthropogenic sources. Lead is one of the more persistent metals in

industrial effluents and it was estimated to have a soil retention time of 150 to 5000 years (Sobolev et al. 2008). It is used

in many industries, such as metal plating, explosive manufacturing, acid battery manufacturing, printing, finishing,

photographic materials, tetra ethyl lead manufacturing, and glass and ceramic industries (Majumdar et al. 2010; Martins

et al. 2006). Even at very low concentration, lead shows serious biological effects in living organisms. Exposure to lead

can damage the nervous system, reproductive system and kidneys especially in children (Jianmin Chen et al. 2012), and

long term exposure may induce sterility, abortion etc., (Gercel et al. 2007). Direct ingestion of lead leads to increased

lead levels in the blood (Swarup et al. 2005). According to the Bureau of Indian Standards (BIS), the permissible limit of

lead in drinking water is 0.01 mg/L (BIS 1994). In recent years, there has been an increased concern over the content

of lead in drinking and natural water. The concentration of lead in the industrial effluents must be reduced to 0.1mg/L as

per the regulations of the EPA before being discharged into the environment (Friberg. 1997).

Copper is one of the most widely used heavy metals and is discharged into the environment from mining wastes, plating

baths, fertilizer industry, paints and pigments (Ajmal et al. 1998), while melting , grinding or the

50

cutting of copper may produce fumes or dust. Copper may also be found as a contaminant in food, especially mushrooms,

nuts, shellfish and chocolate (Yu et al. 1998). Copper exists in various forms in the environment such as Cu(I), and

Cu(II) states. Among these Cu(II) is commonly found in natural water and the free Cu(II) ion is potentially very toxic to

aquatic life, both acutely and chronically. Copper entering in water eventually collects in the sediments of rivers,

lakes, and estuaries. Cu(II) is a micronutrient in agriculture, and therefore, when accumulated in surface water is a

threat to aquatic life and human beings (Meenakshi Goyal et al. 2001). Copper is an essential micronutrient to human

life, but high levels can be harmful to health. The maximum acceptable limit of copper in drinking water is less than

3000µg/L. Excessive intake of copper ions [Cu (II)] by humans leads to severe mucosal irritation, hepatitis and renal

damage, widespread capillary damage, gastrointestinal irritation, nervous disorder followed by depression, and possible

necrotic changes in the liver and kidney (Ofomaja et al. 2009), anemia and lung cancer, etc., (Richard et al. 1996; Wang

et al. 2001).

Nickel is one of the heavy metal pollutants released into the environment by nickel alloy production plants, welding,

electroplating, nickel-cadmium batteries, etc. It is primarily found in combined forms with oxygen or sulphur as oxides

or sulphides that occur naturally in the earth‟s crust. Nickel combined with other elements is present in all soils, in

meteorites, and is emitted from volcanoes. As for most metals, the toxicity of nickel is dependent on the route of

exposure and the solubility of the nickel compound (Coogan et al. 1989). The most common ailment arising from

nickel or its compounds is an allergic dermatitis known as nickel itch, which occurs when the skin is moist. Metallic

nickel and compounds of nickel are teratogenic and carcinogenic to mammals. Consumption of these bio accumulated

nickel plants by human beings can cause cancer, respiratory failure, birth defects and heart disorders (Duda-Chodak et al.

2008). Nickel causes some morphological transformations in numerous cellular systems and chromosomal aberrations

(Coen et al. 2001).

Due to the increase of population and industrialization, it is required to ensure the quality of water continuously. The

increasing levels of toxic metals discharged from various industries into the environment as effluents pose a serious

threat to human health and the environment. Thus, effective removal of undesirable metal ions from wastewater is still

a challenging task for environmental scientists. Numerous technologies for the removal of heavy metals from wastewater

have been developed. The conventional methods available are chemical precipitation (Esalah et al. 2000),

electrocoagulation (Subramanyan Vasudevan et al. 2012), solvent extraction, ion-

51

exchange, adsorption, membrane separation (Canet et al. 2002), ultra filtration (Bayhan et al. 2001), evaporation, reverse

osmosis (Staison, 1979), oxidation, and electro flotation (Zouboulis et al. 1997).

Except adsorption technique, other conventional methods are successfully applied to wastewater, which contains a high

concentration of heavy metal ions. But at low concentrations, it is difficult to achieve the permissible level of heavy metal

ions removal from wastewater (Oo et al. 2009). Due to their less efficiency, expensiveness, and high energy consumption,

conventional methods are commercially unattractive (Bailey et al. 1999). Adsorption has advantages over other

conventional treatment methods, in terms of low cost, insensitivity to toxic pollutants, and greater flexibility in the design

and operation. In addition, adsorption does not result in the formation of harmful substances as by products (Crini. 2006).

Removal of heavy metals from wastewater by the adsorption technique, using various commercial adsorbents has been

studied extensively. Numerous materials have been used and investigated as adsorbents in wastewater treatment. Among

them, activated alumina, silica gel, clay, zeolites and activated carbon have been found to play a vital role. In view of

the low cost of such adsorbents, it may not be essential to regenerate the spent materials. Thus, a number of industrial

wastes and plant material have been investigated as adsorbents with or without treatment for the removal of pollutants

from wastewater.

The aim of the present work is to prepare and explore the potential use of surface modified Guazuma Ulmifolia seeds

for removing the metal ions such as Pb(II), Cu(II) and Ni(II) from aqueous solution. To investigate the effects of

operating parameters on the adsorption behavior, the batch experimental studies were carried out by varying the

solution pH, adsorbent dose, initial metal ion concentration, contact time and temperature. The FT-IR and SEM analysis

were also carried out to study the physical-chemical properties of the adsorbent before and after adsorption with metal

ions. Adsorption isotherms, kinetics and thermodynamics were studied and the results are interpreted by fitting the

experimental data to the different model equations.

2. Experimental

2.1 Preparation of Adsorbent

52

The raw Guazuma Ulmifolia seeds were collected from Anna University Campus, Guindy, Chennai, Tamil Nadu,

India. They are thoroughly washed with distilled water to remove dust, mud and other impurities. They are sun

dried for a week. One part of the raw Guazuma Ulmifolia seeds was immersed in two parts by weight of 10N sodium

hydroxide solution for about 24 hrs. After treatment this was washed with distilled water until the filtrate reached a

neutral pH. The resultant material was dried in a hot air oven at 105°C, powdered and sieved in 200 µm mesh

particle size. This adsorbent was labelled as SMGU-B (Surface Modified Guazuma Ulmifolia seeds with Base)

and used for further adsorption studies.

2.2 Preparation of Metal ion solutions

All the chemicals used were of AR grade, and procured from Merck, India. Stock solutions of metal ions, such as Pb(II),

Cu(II) and Ni(II) were prepared by dissolving the calculated quantity of Pb(NO3)2, CuSO4.5H2O and NiSO4.6H2O

respectively, in double distilled water. The prepared solutions are diluted with double distilled water to obtain various

desired concentrations. The pH adjustments of the solutions were carried out with 0.1 M NaOH or 0.1 M HCl as

required.

2.3 Analysis

The concentration of metal ions in the solution before and after equilibrium was determined, by using AA6300 Atomic

absorption spectrometer (AAS) (Shimadzu, Japan). The pH of the solutions was measured with a Hanna pH meter using a

combined glass electrode. The FT-IR analysis of the adsorbents was carried out using KBr pellets in the spectral range

of 4000 to 650 cm-1

(PE IR SPECTRUM ASCII PEDS 1.60 spectrometer). The surface morphology of the adsorbents

was analyzed using a Leo Gemini 1530 scanning electron microscope (SEM), at an accelerating voltage of 15 kV with a

working distance of 10 µm.

2.4 Batch adsorption experiments

Batch adsorption experiments were carried out by shaking a known quantity of the adsorbent (SMGU-B), with the

selected metal ion solution in a conical flask, at 120 rpm, using an orbital shaker (Orbitek, Sciegenics Biotech) for about

70 minutes, then, the metal ion solutions were centrifuged for about 5 minutes, and the residual concentration of the

metal ions in the solution was measured with AAS. Each determination was repeated thrice and the results obtained are

the averages of these values. The data obtained in the batch studies were used to calculate the percentage removal of

the metal ions by a mass balance relationship:

53

Co Ce % Re moval

Co

100

(1)

Where Co and Ce are the initial and equilibrium concentrations (mg/L) of the metal ion solutions respectively.

2.4.1 Effect of solution pH on metal ion adsorption

The effect of pH on the removal of metal ions with SMGU-B as adsorbent was evaluated with 100 mL solution of 50

mg/L metal ion concentration in the pH range of 2.0 to 7.0 at 30 oC, for 70 min. Experiments were not performed at

higher pH values due to hydrolysis, and the precipitation of the metal ions. After the attainment of equilibrium, the

samples were centrifuged, and the supernatant was analyzed to determine the residual metal ion concentration.

2.4.2 Effect of adsorbent dose on metal ion adsorption

Batch adsorption experiments was carried out with the adsorbent (1g/L to 6 g/L of SMGU-B) in a 100 mL solution

containing 50 mg/L of metal ion concentration at pH 5.0, for a contact time of 70 min at 30 oC. Flasks were shaken on a

rotary shaker for 60 min, to ensure that the equilibrium is reached. After the attainment of equilibrium, the samples are

centrifuged, and the supernatant was analyzed to determine the residual metal ion concentration.

2.4.3 Kinetic studies on metal ion adsorption

Batch adsorption experiments were carried out with SMGU-B by varying the contact time (10 to 120 min) of the

adsorbents in the metal ion solution, while keeping the other parameters such as pH, adsorbent dosage and initial metal

ion concentration as constant at 5.0, 3g/L and 50 mg/L respectively. The samples were then shaken and withdrawn at

predetermined time intervals and centrifuged. The supernatants were analyzed for metal ion concentration.

The amount of metal ions adsorbed onto the adsorbent at various time intervals was calculated, using the following

relationship:

(Co Ct ) V

qt m

mg/g (2)

where qt is the amount of metal ion adsorbed onto the adsorbent at any time t (mg/g), C t is the concentration of the metal

ion solution at any time t (mg/L), V is the volume of the metal ion solution (L) and m is the mass of the adsorbent used

(g). The results obtained from this study were utilized to investigate the adsorption mechanism, using the pseudo-first

order equation (Lagergren et al. 1898), pseudo-second order equation (Ho et al. 1999), Elovich

54

kinetic model (Ho et al. 2002), intraparticle diffusion model (Weber et al. 1963) and Boyd kinetic plot (Boyd et al. 1947).

2.4.4 Effect of initial concentration of metal ion and adsorption isotherms

Batch adsorption experiments were carried out by contacting 3 g/L of the SMGU-B adsorbent with 100 mL of the metal

ion solution of different initial concentrations (25-125 mg/L) at pH of 5.0 and at a temperature of 30 oC. The contents

were shaken for 70 min at a speed of 120 rpm and then filtered. The filtrates were analyzed for residual metal ion

concentration, using AAS.

The amount of metal ions adsorbed onto the adsorbent at equilibrium, qe (mg/g) was calculated by the following

relationship:

(Co Ce ) V

qe m

mg/g (3)

Where, Co and Ce (mg/L) are the initial and equilibrium metal ion concentrations respectively. Adsorption isotherm

models such as Langmuir and Freundlich (Langmuir. 1918; Freundlich. 1906; Ulis Soukand et al. 2002) were used to

fit the experimental data. It is important to find the best fitting isotherm model to evaluate the efficiency of the prepared

adsorbent and to develop a suitable batch adsorber design.

2.4.5 Effect of temperature and thermodynamic Studies

Batch adsorption experiments were performed with the SMGU-B at different temperatures of 303, 313, 323 and 333 K

for fixed initial metal ion concentration, adsorbent dose and pH. The experimental data obtained from these studies

were used to calculate the thermodynamic parameters, such as Gibbs free energy (OGo), enthalpy change (OH

o) and

entropy change (OSo), so as to confirm whether the adsorption process is exothermic or endothermic in nature.

2.5 Desorption studies

The disposal of the exhausted adsorbent is an issue of environmental concern. To make the adsorption process more

economical, it is necessary to regenerate the spent carbon for further use. Desorption of metal ions from the spent

adsorbent SMGU-B were carried out with varying concentrations (0.2 - 0.40 M) of hydrochloric acid (HCl) for spent

SMGU-B. The solution pH plays a vital role on the recovery of metal ions from the spent adsorbents at the metal ion

concentration of 50 mg/L.

3. Results and Discussion

55

3.1 Characterization of the adsorbent

3.1.1 FT-IR Analysis

Fig 1. FT-IR spectrum of (a) SMGU-B, (b) Pb(II), (c) Cu(II) and (d) Ni(II) ions loaded SMGU-

B.

The FT-IR spectrum of SMGU-B carbon and Pb (II), Cu (II) and Ni (II) loaded-SMGU-B are shown in Figure 1 (a), (b),

(c) and (d). The band at 3427 cm-1

may be due to the stretching vibrations of water and alcoholic groups. The presence of

aromatic ring is evident by its –CH stretching vibration at 3098 cm-1

and ring vibration at 1602 and 1456 cm-1

. C=O

stretching vibration due to carbonyl group is noted at 1621 cm-1

.

The peaks obtained at 1363, 1377, 1312 and 1435 cm-1

are due to –CH2 bending vibrations. The fine structure in the low

energy region of the high energy band was due to hydrogen bonding. A comparison of Figure 4.3(a) with the metal ions

loaded FT-IR spectra of SMGU-B (Figure 1 (b), (c) and (d)) shows, shifting of alcoholic stretching vibrations from 3427

cm-1

to 3430 cm-1

, 3392 cm-1

and 3387 cm-1

, and C=O stretching vibrations from 1621 cm-1

to 1615 cm-1

, 1594 cm-1

and

1591 cm-1

. Further it shows the disappearance of the peak at 1236 cm-1

which is assigned

56

to the ester (–COO). From the above observations it can be concluded that the metal ions (Pb (II), Cu (II) and Ni

(II)) are adsorbed onto the active sites of base treated Guazuma Ulmifolia seeds (SMGU-B) carbon.

3.1.2 SEM Analysis

Fig 2. (a) SEM image of SMGU-B, (b) SEM image of Pb(II) ions loaded SMGU-B, (c) SEM image of Cu(II)

ions loaded SMGU-B, (d) SEM image of Ni(II) ions loaded SMGU-B

Similarly, the scanning electron microscopic (SEM) images of the SMGU-B and Pb(II), Cu(II) and Ni(II) loaded

SMGU-B are shown in Figure 2 (a), (b), (c) and (d). SMGU-B was prepared by chemically treating Guazuma

Ulmifolia seeds with sodium hydroxide for surface modification. From Figure 2 (a), an irregular and pores of various

pore sizes could be observed. On the basis of this fact, it can be concluded that the adsorbent has an adequate morphology

for metal ion adsorption. After metal adsorption, the porous surface on the SMGU-B gets filled up by the metal ions.

Figures 2 (b), (c) and (d) show that Pb(II), Cu(II) and Ni(II) ions are heavily loaded in SMGU-B, and the adsorption of

each metal ion occurs on the inner walls of SMGU-B surface which may also be concluded from the morphological study

of every metal loaded SMGU-B.

3.2 Effect of solution pH on metal ion adsorption

10

Fig 3. Effect of solution pH on metal ion removal by SMGU-B (Initial metal ion concentration = 50 mg/L, adsorbent

dose = 3 g/L, time = 70 min (SMGU-B) and temperature = 30oC)

The solution pH acts as a controlling parameter of the adsorption process. The pH of a solution has an effect on the

surface charge of the adsorbent, the degree of ionization, and speciation of the adsorbate, during the process. The

influence of the solution pH on the percentage removal of the metal ions such as Pb(II), Cu(II) and Ni(II) was studied,

using SMGU-B as adsorbent in the pH range of 2.0 to 7.0. The experimental results are shown in Figure 3. The

percentage removal of metal ions investigated was found to increase with an increase in the solution pH up to 5.0, and

then it decreased with the increase in the solution pH. It could be seen from Figure 3, that at a lower pH, the surface of

the adsorbent was surrounded by hydronium ions. The adsorbent surface becomes more positively charged, so that the

attraction between the adsorbent and the metal ions get reduced. As the pH increases, the adsorbent surface becomes less

positively charged, and this facilitates higher metal ion removal. Beyond a certain pH, the charge on both the adsorbent

surface and the metal species becomes negative, and hence the adsorption process decreases significantly.

3.3. Effect of adsorbent dose on metal ion adsorption

The extent of adsorption depends on the available sites over the adsorbent. The adsorbent dose determines the number

of active sites, and acts as an important parameter in adsorption for a given initial metal ion concentration. Figure 4 show

the effect of the SMGU-B dose on the removal of metal ions. The maximum percentage removal was observed as Pb (II)

is 98.12%, Cu(II) is 96.38% and Ni(II) is 94.05% at a dose of 3g/L. Beyond this dosage, adsorption was almost

constant, which may be due to the reduction in the concentration gradient. This is explained by the fact that

increasing the mass of adsorbent increases the effective number of active sites available for the

11

adsorption of metal ions. The optimum SMGU-B dose was fixed as 3g/L and it was applied to further experimental

studies.

Fig 4. Effect of adsorbent dose for metal ion removal by SMGU-B (Initial metal ion concentration = 50 mg/L,

solution pH = 5.0, time = 70 min and temperature = 30 oC)

3.4 Kinetic Studies on metal ion adsorption

Equilibrium time is one of the most important parameters in the adsorption process. Kinetic studies of the adsorption

processes provide useful data regarding the efficiency of adsorption and the feasibility of scale-up operations. Figure 5

show the effect of contact time on the percentage removal of metal ions such as Pb (II), Cu (II) and Ni (II) from aqueous

solution by the SMGU-B. From the figure, it was identified that the removal of metal ions increases with the increase

of contact time, and that the equilibrium reached 70 minutes of contact time for all the metal ions examined in this study.

The data obtained in this study was utilized to elucidate the adsorption mechanism using the pseudo-first order, pseudo-second

order, Elovich, intraparticle diffusion and Boyd kinetic models.

12

Fig 5. Effect of contact time for metal ion removal by SMGU-B (Initial metal ion concentration = 25-125 mg/L,

solution pH = 5.0, SMGU-B dose = 3 g /L and temperature = 30 oC)

3.4.1 Pseudo-first order kinetic model

In adsorption equilibrium is established between the solution and the adsorbent, adsorption of metal ions from aqueous

solutions onto the adsorbent SMGU-B can be considered as a reversible process. The adsorption process can be

illustrated as follows

log(qe qt ) logqe

1 t 2.303

(4)

13

where

k k k C A (5)

kS

qt XAt and qe XAe

Where qe and qt are the amounts of metal ions adsorbed at equilibrium and at time t (mg/g) respectively, and k1 is

the pseudo-first order kinetic rate constant (min-1

).

Fig 6. Pseudo first order kinetic plots for the adsorption of (a) Pb(II), (b) Cu(II), (c)Ni(II) ions onto the SMGU-B.

The plot of log(qe − qt) versus t is shown in Figure 6, for various concentrations of metal ions Pb (II), Cu

(II) and Ni (II). The values of qe and k1 were calculated from the slope and intercept of the above plots respectively,

and they are listed Table 1(for Pb(II)), Table 2 (for Cu(II)), Table 3 (for Ni(II)).

14

3.4.2 Pseudo-second order kinetic model

Fig 7. Pseudo second order kinetic plots for the adsorption of (a) Pb(II), (b) Cu(II), (c) Ni(II) ions onto the

SMGU-B.

The pseudo-second order kinetic equation is based on the assumption that the adsorption rate is related to the square of

the number of unoccupied sites, and it follows second order chemisorption. The experimental data obtained was also

interpreted, using the following expression,

t

1

1 t

q k q2

q (6)

t 2 e e

where, k2 is the pseudo-second order kinetic rate constant. The values of qe and k2 were calculated from the slope and

intercept of the linear plot of t/qt versus t, and are shown in Figure 7, for different concentrations of metal ions Pb(II),

Cu(II) and Ni(II), and are listed in Tables Table 1(for Pb(II)), Table 2 (for Cu(II)), Table 3 (for Ni(II)).

15

3.4.3 Elovich kinetic Model

Fig 8. Elovich kinetic plots for the adsorption of (a) Pb(II), (b) Cu(II), (c) Ni(II) ions onto the SMGU-B.

The Elovich kinetic model can be expressed as:

q 1

ln ( ) 1

ln t

(7)

t

where α is the initial adsorption rate in mg/(g.min), and β (g/mg) is the desorption constant related to the extent of the

surface coverage and activation energy for chemisorption. The adsorption kinetics of metal ions such as Pb(II), Cu(II)

and Ni(II) onto the adsorbent (SMGU-B) were also tested with Elovich kinetic model by plotting qt versus ln t, and the

results are shown in Figure 8, for different concentrations of metal ions such as Pb(II), Cu(II) and Ni(II). Both the kinetic

constants (α and β) were estimated from the graph and are listed in Tables 1, 2 and 3.

16

The results indicate that the obtained R

2 values for the pseudo-first order equation were low, and the difference between

the calculated qe and experimental qe values were high. Therefore, the adsorption of metal ions onto SMGU-B does not

follow pseudo-first order kinetics. From Tables 1 to 3, it can be seen that the obtained R2 values for pseudo-second order

kinetic model are high (>0.97), which indicates the applicability of pseudo-second order kinetic model as a better option

to describe the adsorption of metal ions onto the adsorbent SMGU-B. The calculated qe values were also found to be

close to the experimental qe values for all metal ions studied, which confirms the applicability of the model. Therefore, it

can be concluded that the adsorption of metal ions onto the adsorbent follows the pseudo-second order kinetic model, and

assumes that chemisorption is the rate controlling step (Ho et al. 1999). The R2 values obtained for the Elovich kinetic

model were lower than those obtained for the pseudo-second order equation. The Elovich equation does not predict any

definite mechanism, but it is useful in describing adsorption onto highly heterogeneous adsorbents. This indicates that the

Elovich equation may also be used to predict the adsorption kinetics of metal ions onto adsorbent for the entire

adsorption period, since SMGU-B having heterogeneous surface active sites.

3.4.4 Intraparticle diffusion model

The intraparticle diffusion model is plotted in order to verify the effect of the mass transfer resistance on the adsorption

of metal ions such as Pb(II), Cu(II) and Ni(II) onto the SMGU-B. The Weber and Morris intraparticle diffusion model is

expressed as follows:

qt k t 2 C (12)

Where qt (mg/g) is the amount of metal ions adsorbed at time t, kp (mg/g.min1/2

) is the intraparticle diffusion rate

constant, and C is the intercept. The values of kp, C and R2 were calculated from the plot of qt versus t

1/2 (Figure 9) and

these values are listed in Tables 1 - 3. According to this model, if the plot of qt versus t1/2

passes through the origin, then

the adsorption of metal ions onto the adsorbents is controlled by intraparticle diffusion, while, if the data exhibit a multi-

linear curve line, then two or more steps influence the adsorption process.

In the present study, none of the plots passed through origin and the deviation from origin might be due to the difference

in mass transfer rate in the initial and final stages of adsorption. The plots show multi-linearity, indicating that two steps

are possible. As can be seen from Figures 9 of the first stage, the sharper portion may be considered as an external surface

adsorption or boundary layer diffusion. The second portion describes the slow

17

adsorption stage, where intraparticle diffusion is the rate-controlling step. The rate of metal ions uptake might be limited

by factors, such as the size of the adsorbate molecules, the concentration of the adsorbate and its affinity to the

adsorbent, the diffusion coefficient of the adsorbate in the bulk phase, the pore size distribution of the adsorbent, and the

degree of mixing. The results obtained from the intraparticle diffusion model show that intraparticle diffusion is not the

sole process that takes place, but boundary layer diffusion may also take place. This can be found by testing the kinetic

data with the Boyd kinetic model.

Fig 9. Intraparticle diffusion model plots for the adsorption of (a) Pb(II), (b) Cu(II), (c) Ni(II) ions onto the

SMGU-B.

18

3.4.5 Boyd kinetic model

In order to identify the actual slowest step in the adsorption of metal ions onto the adsorbent (SMGU-B), the

adsorption kinetic data were applied to the Boyd kinetic Equation. The Boyd kinetic equation is expressed as

follows: qt 1

6

qe 2

exp (Bt) F

(13)

Where qt (mg/g) and qe (mg/g) are the amounts of metal ions adsorbed at time t and at equilibrium, respectively, Bt is

the mathematical function of F, and F is the fraction of metal ions adsorbed at any time t. The Equation (13) can be

rearranged by taking the natural logarithm to obtain the following expression:

Bt 0.4977 ln (1 F ) (14)

The plot of Bt against time t is employed to test the linearity of the experimental values. If the plots are linear and pass

through origin, then the slowest step in the adsorption process is the intraparticle diffusion, and vice versa. From Figure

10, for different concentrations of metal ions such as Pb(II), Cu(II) and Ni(II), it can be observed that the plots are

linear but do not pass through the origin. This suggests that adsorption of metal ions onto the adsorbent SMGU-B is

controlled by film diffusion or external diffusion.

The B values obtained from the plots were used to determine the effective diffusion coefficient, D i (m2/s) using the

following expression:

2 D B i

r 2

(15)

where Di is the effective diffusion coefficient of metal ions in the adsorbent (SMGU-B) surface, and r is the radius of

the adsorbent particle, calculated by the sieve analysis and by assuming spherical particles. The D i values are listed in

Tables 1, 2 and 3 for SMGU-B.

19

Table 1 Kinetic parameters for the adsorption of Pb(II) ions on SMGU - B

Kinetic model Parameters Concentration of Pb(II) ion solution (mg/L)

25 50 75 100 125

Pseudo-first order

equation

k1 (min-1

) 0.0599 0.0714 0.0576 0.0783 0.0668

qe, cal (mg/g) 10.739 29.512 33.113 71.945 67.920

R2 0.918 0.896 0.930 0.896 0.911

Pseudo-second order

equation

k2 (g/mg.min)

x 10-3

2.866 3.623 2.272 1.715 1.294

qe ,cal (mg/g) 9.615 19.231 28.571 37.037 45.455

h (mg/g.min) 0.265 1.340 1.848 2.353 2.674

qe,exp (mg/g) 8.356 16.421 24.152 30.825 37.825

R2 0.993 0.992 0.992 0.993 0.993

Elovich kinetic

equation

α (mg/g.min) 2.660 4.477 5.749 6.669 6.755

β (g/mg) 0.585 0.285 0.189 0.141 0.109

R2 0.962 0.962 0.970 0.977 0.982

Intraparticle diffusion

model

kp

(mg/g.min1/2

)

0.582 1.196 1.805 2.399 3.106

C 3.168 5.798 7.899 9.659 10.360

R2 0.970 0.971 0.979 0.972 0.971

Boyd kinetic model B 0.061 0.072 0.058 0.079 0.068

Di

(x 10-13

m2/s)

10.309 12.168 9.802 13.351 11.492

R2 0.918 0.896 0.930 0.896 0.911

20

Table 2 Kinetic parameters for the adsorption of Cu(II) ions on SMGU - B

Kinetic model Parameters Concentration of Cu(II) ion solution (mg/L)

25 50 75 100 125

Pseudo-first order

equation

k1 (min-1

) 0.0553 0.0691 0.0806 0.0759 0.0783

qe, cal (mg/g) 9.638 29.040 56.624 67.453 90.365

R2 0.984 0.896 0.883 0.894 0.889

Pseudo-second order

equation

k2 (g/mg.min)

x 10-3

7.012 3.326 2.339 1.631 1.247

qe ,cal (mg/g) 9.709 19.231 27.778 37.037 45.455

h (mg/g.min) 0.661 1.230 1.805 2.237 2.577

qe,exp (mg/g) 8.289 16.187 23.421 30.492 36.844

R2 0.992 0.992 0.992 0.993 0.993

Elovich kinetic

equation

α (mg/g.min) 2.095 3.661 5.371 6.010 6.535

β (g/mg) 0.556 0.275 0.190 0.139 0.111

R2 0.962 0.970 0.974 0.980 0.982

Intraparticle diffusion

model

kp

(mg/g.min1/2

)

0.612 1.239 1.789 2.435 3.044

C 2.818 5.168 7.572 8.975 9.983

R2 0.967 0.975 0.975 0.974 0.973

Boyd kinetic model B 0.056 0.069 0.080 0.076 0.079

Di

(x 10-13

m2/s)

9.464 11.661 13.52 12.844 13.351

R2 0.932 0.896 0.883 0.894 0.889

21

Table 3 Kinetic parameters for the adsorption of Ni(II) ions on SMGU - B

Kinetic model Parameters Concentration of Ni(II) ion solution (mg/L)

25 50 75 100 125

Pseudo-first order

equation

k1 (min-1

) 0.0576 0.0645 0.0852 0.0783 0.0783

qe, cal (mg/g) 9.572 22.646 59.429 61.518 75.336

R2 0.947 0.946 0.922 0.933 0.936

Pseudo-second order

equation

k2 (g/mg.min)

x 10-3

9.894 4.262 2.362 1.742 1.378

qe ,cal (mg/g) 8.333 17.241 27.397 35.714 43.478

h (mg/g.min) 0.687 1.267 1.773 2.222 2.604

qe,exp (mg/g) 8.186 15.788 22.997 29.522 35.297

R2 0.993 0.994 0.994 0.994 0.995

Elovich kinetic

equation

α (mg/g.min) 2.140 3.439 4.572 5.501 6.201

β (g/mg) 0.561 0.273 0.181 0.139 0.113

R2 0.964 0.973 0.974 0.976 0.978

Intraparticle diffusion

model

kp

(mg/g.min1/2

)

0.602 1.229 1.852 2.418 2.953

C 2.872 5.057 6.924 8.519 9.729

R2 0.956 0.953 0.953 0.951 0.947

Boyd kinetic model B 0.059 0.066 0.086 0.078 0.079

Di

(x 10-13

m2/s)

9.971 11.154 14.534 13.182 13.351

R2 0.947 0.946 0.922 0.933 0.936

Dual nature of the curve is obtained due to the varying extent of adsorption in the initial and final stages of adsorption

(Figure 9). This can be attributed to the fact that in the initial stages, the adsorption was due to the boundary layer

diffusion effect, whereas in the later stages, it is due to the intraparticle diffusion effects. The intercept of the plot

reflects the boundary layer effect. The larger the intercept, the greater will be the contribution of the surface adsorption in

the rate controlling step. If the regression in the plot of qt versus t(1/2)

is linear, and passes through the origin, then

intraparticle diffusion is the sole rate-limiting step.

22

Fig 10. Boyd kinetic model plots for the adsorption of (a) Pb(II), (b) Cu(II), (c) Ni(II) ions onto the SMGU-

B.

However, linear plots at each concentration do not pass through origin. This deviation may perhaps be due to the

difference in the rate of mass transfer in the initial and final stages of adsorption. This indicates that there is some degree

of boundary layer control, and this further show that the intraparticle diffusion is not only the rate- limiting step, but also

the rate-controlling factor of adsorption or both may be operating simultaneously. This can be found by testing the kinetic

data with the Boyd kinetic plot. From Figure 10, it can be seen that the plots are linear but do not pass through the

origin, suggesting that the adsorption process is controlled by film diffusion.

23

3.5 Effect of initial metal ion concentration and adsorption isotherm studies

Figure 11, show the effect of the initial concentration of metal ions such as Pb (II), Cu (II) and Ni (II) onto the adsorbent

SMGU-B. It can be seen that the percentage removal decreases with an increase in the metal ion concentration. This is

due to the saturation of the active sites of the adsorbent, by the metal ions. Adsorption proceeds faster when a fixed

amount of adsorbent dose is used at low metal ion concentrations, due to the less number of metal ions present in the

solution. Increase in the initial metal ion concentration serves against the fixed amount of available sites on the adsorbent.

Hence, there is a decrease in the metal ion removal with an increase in the initial metal ion concentration.

Fig 11. Effect of initial metal ion concentrations on SMGU-B (adsorbent dose = 3 g/L, pH = 5.0, time

= 70 min, and temperature = 30

oC)

Equilibrium adsorption isotherm is important to understand the mechanism of adsorption. The equilibrium data for the

adsorption of metal ions such as Pb (II), Cu(II) and Ni(II) onto the adsorbent SMGU-B, was applied to the Langmuir

and Freundlich adsorption isotherms. Analysis of the equilibrium data by fitting them into chosen isotherm models is an

important step to find a suitable isotherm model that can be used for the design of an adsorption system.

3.5.1 Langmuir adsorption isotherm

Langmuir adsorption isotherm model assumes the presence of a finite number of binding sites, homogeneously

distributed over the adsorbent surface, providing the same affinity for adsorption of a monolayer, with no interaction

between the adsorbed molecules. This model predicts the maximum monolayer adsorption capacity of the adsorbent, and

also determines if the adsorption is favourable or not. This model is expressed as follows:

24

1

1 .

1

1

qe qm KL Ce qm

(16)

Where qe (mg/g) is the adsorption capacity at equilibrium, qm (mg/g) is the maximum monolayer adsorption capacity,

Ce (mg/L) is the equilibrium concentration of the metal ions and KL (L/g) is the Langmuir equilibrium constant. The

Langmuir isotherm constant parameters and the R2 values were calculated from the plot of 1/Ce versus 1/qe (Figure 12)

and are listed in Table 4.

Fig 12. The Langmuir adsorption isotherm for (a) Pb(II), (b) Cu(II) and (c) Ni(II) ions onto the SMGU-B.

The essential characteristic of the Langmuir isotherm can be expressed in terms of a dimensionless constant, or

separation factor RL, which is given by the following expression:

R 1

(17) L

1 K LCo

25

Where KL (L/g) is the Langmuir equilibrium constant and Co (mg/L) is the initial metal ion concentration. The RL values

indicate whether the type of adsorption isotherm is favorable (0<RL<1), unfavourable (RL >1), linear (RL =1), or

irreversible (RL =0).

3.5.2 Freundlich adsorption isotherm

The Freundlich adsorption isotherm model is developed by assuming a heterogeneous surface, with a non-uniform

distribution of the heat of adsorption over the surface. It is expressed as follows:

1 log q e log K F n

log Ce

(18)

where KF is the Freundlich constant ((mg/g)(L/mg)(1/n)

) related to the bonding energy and n (g/L) is a measure of the

deviation from the linearity of adsorption. The values of KF and n were calculated from the linear plot of log qe versus

log Ce (Figure 13) and the values are listed in Table 4. The n value indicates the degree of non-linearity between the

metal ion concentration and adsorption. If n = 1, then the adsorption is linear; if n < 1, then the adsorption is a chemical

process; if n > 1, then the adsorption is a physical process.

Fig 13. The Freundlich adsorption isotherm for (a) Pb(II), (b) Cu(II) and (c) Ni(II) ions onto the SMGU-B.

26

The experimental data from the study of the effect of the initial concentration of metal ions onto adsorbent SMGU-B

surfaces was fitted to the Langmuir and Freundlich adsorption isotherm models, and the graphical representations of

these models are shown in Figure 12 and 13. The Langmuir constants, qm (mg/g) and KL (L/mg) with the R2 values were

calculated from the plot of 1/qe versus 1/Ce at 30 oC and are listed in Table 4. The separation parameter RL values, were

found to be 0.0033, 0.0160 and 0.0295 for the concentration of 75 mg/L of metal ions, Pb(II), Cu(II) and Ni(II)

respectively, for SMGU-B. The observed RL values lie between 0 and 1 for all the metal ions studied, with the SMGU-

B, which indicates that the adsorption is favourable (Eagleton et al. 1966).

The Freundlich constants, KF ((mg/g)(L/mg)(1/n)

) and „n‟ values with the R2 values were derived from the plot of log qe

versus log Ce at 30 oC, and are listed in Table 4. The „n‟ values were found to be 3.185, 2.415 and 2.227 for metal ions

Pb(II), Cu(II) and Ni(II) respectively for the adsorbent SMGU-B. Since, the „n‟ values lie between 1 and 10, it indicates

that the adsorption of metal ions onto the adsorbents was physical adsorption (McKay et al. 1981).

Table 4 Adsorption isotherm constants for the removal of metal ions by SMGU - B

Isotherm Model Metal ion solution

Parameters Pb(II) Cu(II) Ni(II)

Langmuir qm (mg/g) 27.78 32.258 35.714

KL (L/mg) 4.000 0.816 0.438

R2 0.937 0.977 0.970

Freundlich KF

((mg/g)(L/mg)(1/n)

)

16.406 12.218 9.683

n (g/L) 3.185 2.415 2.227

R2 0.997 0.996 0.997

Based on the R2 values obtained, the order of the best fit of the adsorption isotherms studied with different metal ions,

Freundlich > Langmuir for the adsorbent SMGU-B. From the R2 values, the adsorption of metal ions onto the SMGU-

B is well represented by the Freundlich adsorption isotherm model than the Langmuir adsorption isotherm model, which

is based on the heterogeneous adsorption of metal ions by the adsorbents. From the above observation, the adsorption of

metal ions onto the adsorbent follows both monolayer and heterogeneous adsorption mechanism. The maximum

adsorption capacity of the adsorbent SMGU-B for metal ions is in the order of Ni(II) > Cu(II) >Pb(II).

27

3.6 Effect of temperature and thermodynamic studies

The effect of temperature on the percentage removal of metal ions by the adsorbent SMGU-B was investigated at four

different temperatures, i.e., 303, 313, 323 and 333 K. Figure 14 show that the adsorption of metal ions onto the adsorbent

was highly dependent on temperature. The maximum adsorption of metal ions by the adsorbent was observed at 303 K.

A sharp decrease in the removal of metal ions was observed with an increase of temperature. It was mainly due to the

decrease in surface activity, which suggests that the adsorption between metal ions and the adsorbent is an exothermic

process.

Fig 14. Effect of temperature on the adsorption of metal ions by SMGU-B (Initial metal ion concentration = 25-

125 mg/L, SMGU-B dose = 3 g/L, time = 70 min and pH = 5.0)

The thermodynamic parameters, such as Gibbs free energy (OGo), enthalpy change (OH

o) and entropy change (OS

o) were

calculated from the following Equations:

28

K CAe

c C

(19)

e

Go RT ln Kc

So

Ho

(20)

log Kc 2.303R

2.303RT

(21)

where Kc is the equilibrium constant, Ce is the equilibrium of the metal ion concentration in solution (mg/L), CAe is the

amount of metal ions adsorbed on the adsorbent per liter of solution at equilibrium (mg/L), R is the gas constant (8.314

J/mol.K) and T is the temperature (K).

Fig 15. Thermodynamic plots for the adsorption of metal ions onto the SMGU-B

29

The values of OH

o and OS

o were determined from the slope and the intercept of the plot of log Kc versus 1/T (Figure

15). The thermodynamic properties are listed in Table 5 (for Pb(II)), Table 6 (for Cu(II)), Table 7 (for Ni(II)). Feasibility

and the nature of the adsorption process could be evaluated using the thermodynamic parameters. The negative value of

OGo shows that the adsorption process is feasible and spontaneous in the nature. The negative OH

o values for the

adsorption of metal ions such as Pb(II), Cu(II) and Ni(II) on SMGU-B indicate that the adsorption process is

exothermic in nature. The randomness at the adsorbent-solution interface during the adsorption of metal ions could be

described, using the change in entropy (OSo).

Table 5 Thermodynamic parameters for the adsorption of Pb(II) ions on SMGU-B

Initial

Concentration of

Pb(II) ions

(mg/L)

OHo (kJ/mol) OS

o (J/mol/K) OG

o (kJ/mol)

30oC 40

oC 50

oC 60

oC

25 -72.510 -197.024 -13.601 -9.850 -8.236 -7.707

50 -40.822 -102.744 -9.963 -8.325 -7.496 -6.857

75 -23.245 -52.233 -7.444 -6.833 -6.374 -5.859

100 -16.659 -34.484 -6.215 -5.857 -5.501 -5.185

125 -14.935 -30.731 -5.606 -5.375 -4.929 -4.731

Table 6 Thermodynamic parameters for the adsorption of Cu(II) ions on SMGU-B

Initial

Concentration of

Cu(II) ions

(mg/L)

OHo (kJ/mol) OS

o (J/mol/K) OG

o (kJ/mol)

30oC 40

oC 50

oC 60

oC

25 -44.134 -112.662 -10.298 -8.438 -7.663 -6.846

50 -30.980 -74.769 -8.462 -7.376 -6.853 -6.161

75 -17.763 -36.648 -6.679 -6.269 -5.906 -5.582

100 -15.017 -30.176 -5.866 -5.568 -5.290 -4.951

125 -10.969 -19.530 -5.053 -4.827 -4.709 -4.439

30

Table 7 Thermodynamic parameters for the adsorption of Ni(II) ions on SMGU-B

Initial OHo OS

o OG

o (kJ/mol)

Concentration (kJ/mol) (J/mol/K)

of Ni(II) ions 30oC 40

oC 50

oC 60

oC

(mg/L)

25

-33.584

-81.682

-8.992

-7.785

-7.186

-6.491

50 -20.085 -43.541 -6.936 -6.442 -6.003 -5.636

75 -17.669 -38.447 -6.078 -5.568 -5.162 -4.944

100 -12.889 -25.676 -5.079 -4.888 -4.598 -4.316

125 -11.080 -22.153 -4.338 -4.186 -3.915 -3.687

3.7 Desorption studies

Disposal of the spent adsorbent is an environmental problem. To make the adsorption process more economical, it is

necessary to regenerate the spent adsorbent for further use. Desorption of the metal ions from the spent SMGU-B was

carried out with varying concentrations of hydrochloric acid (HCl) (0.2- 0.35 M). The results of desorption studies are

reported in Table 8, for the spent SMGU-B. The results show that the percentage recovery of metal ions increases with

the concentration of HCl and reaches a constant value with 0.3 M HCl for the spent SMGU-B.

Table 8 Desorption of metal ions from the spent SMGU-B using HCl

Metal ions

(Co = 50

mg/L)

Removal

efficiency

(%)

Percentage recovery of metal ions

0.2 M 0.25 M 0.30 M 0.35 M

Pb(II)

98.12

67.58

76.86

88.41

90.17

Cu(II)

96.64

58.68

72.38

84.29

84.11

Ni(II)

94.01

54.47

68.75

82.14

82.78

31

4. Conclusion

The Guazuma Ulmifolia tree is widely distributed throughout India. The seeds of this tree have been found to be a

potential adsorbent for the removal of impurities from wastewater. The present investigation shows that base treated

Guazuma Ulmifolia seeds, can be utilized as an effective adsorbent for the treatment of wastewater, containing metal ions

like Pb(II), Cu(II) and Ni(II). The adsorbent SMGU-B was characterized by using the FT-IR and SEM analyses. The

effects of various parameters, such as solution pH, adsorbent dose, contact time, initial metal ion concentration and

temperature, on the adsorption of metal ions with SMGU-B was studied. Further, based on these studies, kinetics of

adsorption, adsorption isotherms, and the thermodynamics of the adsorption process were also determined. In addition,

desorption of the metal ions from the loaded adsorbent was also carried out to enumerate the reusability of the

adsorbents.

32

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