dna-polyfluorophore chemosensors for environmental remediation: vapor-phase identification of...

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DNA-polyuorophore chemosensors for environmental remediation: vapor-phase identication of petroleum products in contaminated soilWei Jiang, Shenliang Wang, Lik Hang Yuen, Hyukin Kwon, Toshikazu Ono and Eric T. Kool * Contamination of soil and groundwater by petroleum-based products is an extremely widespread and important environmental problem. Here we have tested a simple optical approach for detecting and identifying such industrial contaminants in soil samples, using a set of uorescent DNA-based chemosensors in pattern-based sensing. We used a set of diverse industrial volatile chemicals to screen and identify a set of ve short oligomeric DNA uorophores on PEGpolystyrene microbeads that could dierentiate the entire set after exposure to their vapors in air. We then tested this set of ve uorescent chemosensor compounds for their ability to respond with uorescence changes when exposed to headgas over soil samples contaminated with one of ten dierent samples of crude oil, petroleum distillates, fuels, lubricants and additives. Statistical analysis of the quantitative uorescence change data (as D(R,G,B) emission intensities) revealed that these ve chemosensors on beads could dierentiate all ten product mixtures at 1000 ppm in soil within 30 minutes. Tests of sensitivity with three of the contaminant mixtures showed that they could be detected and dierentiated in amounts at least as low as one part per million in soil. The results establish that DNA-polyuorophores may have practical utility in monitoring the extent and identity of environmental spills and leaks, while they occur and during their remediation. Introduction Contamination of soil and groundwater by petroleum-based products is an extremely important and widespread environ- mental problem. Such contamination arises from above-ground leaks in tanks and pipelines, from degradation of aging underground storage tanks, and from breaches that occur during production and shipping. 1,2 Petroleum-based mixtures can be acutely toxic to animals, plants and humans, 3 and long- term exposure may carry health risks as well. 4 The magnitude of the problem is large; for example, in the U.S. there are an esti- mated 80 000 underground tanks currently leaking into the surrounding soil, with ca. 6 000 more discovered each year. 5 The spreading plumesof organic contaminants put the associated groundwater at risk. 6 As a result of this widespread hazard, remediation of such spills is of high priority worldwide. 7,8 Analysis is important to determine their makeup and origin, and to monitor the margins of each spill as it is being cleaned or removed. Labo- ratory testing methods can be quite eective in determining chemical compositions of organic mixtures in soil samples, 9 but the analysis typically requires expensive instrumentation such as gas chromatography-mass spectrometry and trained techni- cians and analytical chemists to operate them. This adds time and cost to testing soil samples, and limits the ability to rapidly assess the progress of cleanup eorts. As a result, there is a need for analytical methods that can quickly assess levels of contaminants in soils in the eld, and dierentiate their compositions. Among the most promising approaches for characterizing vapors from samples are optical methods, using compounds or materials that respond to vapors of analytes with changes in reectance, absorbance or uores- cence. 1015 Optical methods are rapid and quantiable, and may be implemented with very small amounts of sensor compounds. Arrays of compounds can be used in pattern-based responses to dierentiate one analyte from another. 1015 However, although chromophore arrays have recently been used to analyze mixtures associated with foods 1618 and bacteria, 19,20 we know of few, if any, tests of such optical methods in petroleum-based contamination of soil. 21 This may be challenging for two reasons: rst, such contamination may be diluted by large volumes of soil; and second, dierentiating distinct petroleum- based products may be dicult because of the complexity of the mixtures, the similarity of many petroleum products, and the lack of chemical functionality of the main hydrocarbon components. Department of Chemistry, Stanford University, Stanford, California, 94305-5080, United States. E-mail: [email protected]; Fax: +1 650 725 0259; Tel: +1 650 724 4741 Electronic supplementary information (ESI) available: Synthesis and characterization data; details of experimental methods; color-change plots and scattering data. See DOI: 10.1039/c3sc50985k Cite this: Chem. Sci., 2013, 4, 3184 Received 12th April 2013 Accepted 22nd May 2013 DOI: 10.1039/c3sc50985k www.rsc.org/chemicalscience 3184 | Chem. Sci., 2013, 4, 31843190 This journal is ª The Royal Society of Chemistry 2013 Chemical Science EDGE ARTICLE Published on 22 May 2013. Downloaded by University of Windsor on 25/09/2013 11:33:17. View Article Online View Journal | View Issue

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Page 1: DNA-polyfluorophore chemosensors for environmental remediation: vapor-phase identification of petroleum products in contaminated soil

Chemical Science

EDGE ARTICLE

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Department of Chemistry, Stanford Unive

United States. E-mail: [email protected]; Fa

† Electronic supplementary informatiocharacterization data; details of experimscattering data. See DOI: 10.1039/c3sc509

Cite this: Chem. Sci., 2013, 4, 3184

Received 12th April 2013Accepted 22nd May 2013

DOI: 10.1039/c3sc50985k

www.rsc.org/chemicalscience

3184 | Chem. Sci., 2013, 4, 3184–319

DNA-polyfluorophore chemosensors for environmentalremediation: vapor-phase identification of petroleumproducts in contaminated soil†

Wei Jiang, Shenliang Wang, Lik Hang Yuen, Hyukin Kwon, Toshikazu Onoand Eric T. Kool*

Contamination of soil and groundwater by petroleum-based products is an extremely widespread and

important environmental problem. Here we have tested a simple optical approach for detecting and

identifying such industrial contaminants in soil samples, using a set of fluorescent DNA-based

chemosensors in pattern-based sensing. We used a set of diverse industrial volatile chemicals to screen and

identify a set of five short oligomeric DNA fluorophores on PEG–polystyrene microbeads that could

differentiate the entire set after exposure to their vapors in air. We then tested this set of five fluorescent

chemosensor compounds for their ability to respond with fluorescence changes when exposed to headgas

over soil samples contaminated with one of ten different samples of crude oil, petroleum distillates, fuels,

lubricants and additives. Statistical analysis of the quantitative fluorescence change data (as D(R,G,B)

emission intensities) revealed that these five chemosensors on beads could differentiate all ten product

mixtures at 1000 ppm in soil within 30 minutes. Tests of sensitivity with three of the contaminant mixtures

showed that they could be detected and differentiated in amounts at least as low as one part per million

in soil. The results establish that DNA-polyfluorophores may have practical utility in monitoring the extent

and identity of environmental spills and leaks, while they occur and during their remediation.

Introduction

Contamination of soil and groundwater by petroleum-basedproducts is an extremely important and widespread environ-mental problem. Such contamination arises from above-groundleaks in tanks and pipelines, from degradation of agingunderground storage tanks, and from breaches that occurduring production and shipping.1,2 Petroleum-based mixturescan be acutely toxic to animals, plants and humans,3 and long-term exposure may carry health risks as well.4 The magnitude ofthe problem is large; for example, in the U.S. there are an esti-mated 80 000 underground tanks currently leaking into thesurrounding soil, with ca. 6 000more discovered each year.5 Thespreading “plumes” of organic contaminants put the associatedgroundwater at risk.6

As a result of this widespread hazard, remediation of suchspills is of high priority worldwide.7,8 Analysis is important todetermine their makeup and origin, and to monitor themargins of each spill as it is being cleaned or removed. Labo-ratory testing methods can be quite effective in determining

rsity, Stanford, California, 94305-5080,

x: +1 650 725 0259; Tel: +1 650 724 4741

n (ESI) available: Synthesis andental methods; color-change plots and85k

0

chemical compositions of organic mixtures in soil samples,9 butthe analysis typically requires expensive instrumentation suchas gas chromatography-mass spectrometry and trained techni-cians and analytical chemists to operate them. This adds timeand cost to testing soil samples, and limits the ability to rapidlyassess the progress of cleanup efforts.

As a result, there is a need for analytical methods that canquickly assess levels of contaminants in soils in the eld, anddifferentiate their compositions. Among the most promisingapproaches for characterizing vapors from samples are opticalmethods, using compounds or materials that respond to vaporsof analytes with changes in reectance, absorbance or uores-cence.10–15 Optical methods are rapid and quantiable, and maybe implemented with very small amounts of sensor compounds.Arrays of compounds can be used in pattern-based responses todifferentiate one analyte from another.10–15 However, althoughchromophore arrays have recently been used to analyzemixtures associated with foods16–18 and bacteria,19,20 we know offew, if any, tests of such optical methods in petroleum-basedcontamination of soil.21 This may be challenging for tworeasons: rst, such contamination may be diluted by largevolumes of soil; and second, differentiating distinct petroleum-based products may be difficult because of the complexity of themixtures, the similarity of many petroleum products, and thelack of chemical functionality of the main hydrocarboncomponents.

This journal is ª The Royal Society of Chemistry 2013

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In previous studies we have investigated the use of DNA-polyuorophores as uorescent chemosensors.14,22,23 Ourstructural design involves the use of the DNA backbone as ascaffold to arrange multiple uorophores in close proximity.The DNA phosphodiester backbone offers the advantages ofrapid automated synthesis of thousands of possible opticallydistinct molecules from a set of monomer components, andserves to place the uorophores in direct physical contact, asthe nucleobases are in DNA. This proximity allows the uo-rophores to yield sequence-dependent emergent optical prop-erties, including several forms of energy/excitation transfer, thatdo not occur in the monomer components.24 Such poly-uorophore chemosensors have been studied for their ability torespond with uorescence changes to vapors of pure organiccompounds and to toxic gases.22,23 Several sequences have alsobeen used in combination to sense mixtures such as bacterialmetabolites in air.20 Despite these early examples, it remainedto be seen whether such compounds could respond in ameaningful way to hydrocarbon-based complex mixtures, whichcontain less chemical functionality than previous mixtures.Moreover, it was not known whether they would have sufficientsensitivity to respond to such mixtures diluted to a large extentsuch as might occur in contaminated soil.

Here we describe the study of such DNA polyuorophores(also termed oligodeoxyuorosides or ODFs, Fig. 1) as possibleoptical chemosensors for detection and differentiation of

Fig. 1 Chemosensor compounds in this study. (A) Monomer components ofoligodeoxyfluorosides (ODFs), consisting of fluorophore deoxyribosides andnonfluorescent spacers. (B) Structure of a representative ODF chemosensor(sequence YQFS) conjugated to a PEG–polystyrene bead. (C) Image of ODFs onbeads, taken by epifluorescence microscopy (excitation 340–380 nm).

This journal is ª The Royal Society of Chemistry 2013

closely related petroleum-based products in contaminated soil.The polyuorophores were conjugated to PEG–polystyrenebeads in a combinatorial library, and by screening a set ofdistinct petrochemical organic vapors we arrived at ve ODFsequences that gave strong and highly diverse responses. Wethen studied this set of ODFs for their pattern-based responseswith a set of ten petroleum-based products, including gasolines,crude oil, and oil-based additives. We used statistical methodsto analyze the quantitative uorescence responses, whichranged from quenching to lighting-up and wavelength shis.Experiments showed that these ve ODFs could successfullydifferentiate all ten products despite many similar hydrocarboncompositions, and they could detect and differentiate contam-inants at concentrations as low as one milligram per kilogramof soil (1 ppm) within 30 minutes.

Experimental methodsPhosphoramidite monomers and ODF library construction

The syntheses of the deoxyriboside monomers Y, E, V, Q, F, Kwere performed as previously reported.25–28 The spacer phos-phoramidite (S) and the 5,6-dihydro-dT-CE phosphoramidite(H) were purchased from Glen Research. The construction ofthe tetramer library was carried out by standard split-and-poolmethods on 130 mm amine-functionalized PEG–polystyrenebeads (NovaSyn TG amino resin, Novabiochem) to yield 4096ODF sequences. Individual beads were chemically tagged forsequencing by the method of Still.29

ODF oligomers on beads

The 20 selected sensor sequences from initial screening wereresynthesized on an ABI 394 DNA synthesizer with standardphosphoramidite chemistry. The synthesis was performed at 1micromole scale in a standard column containing both PEG–PSbeads and 30-phosphate CPG (Glen Research), so that sensorbeads and corresponding ODFs on standard CPG were made atthe same time. This allowed the characterization of the ODFs offof the CPG beads while making the corresponding sensors onPEG–PS beads in one synthesis. Cleavage of ODF oligomersfrom CPG beads were carried out with 0.05 M K2CO3 in meth-anol (room temperature, 8 hours). Then they were puried byreverse-phase HPLC and characterized by MALDI-MS andoptical spectra (see ESI†).

Library screening

To screen potential sensors of VOCs, PEG–PS beads of thelibrary were placed on a small microscope slide and enclosed ina sealed 5 mL quartz uorescence cuvette (QS 111, Hellma).Before screening, images were taken in air under an epiuor-escence microscope with 4� objective (lex ¼ 340–380 nm; lem >420 nm) with a Spot RT digital camera and Spot AdvancedImaging soware. Aer this, one drop of VOC liquid or 10 mg ofVOC solid was placed beside themicroscope slide in the cuvette,which was sealed to generate saturated vapors. Aer 30 minexposure at room temperature, uorescence images were takenagain. The images were analyzed with Adobe Photoshop by

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Table 1 Analytes in this study

Pure VOC analytesused in screening

Petrochemical mixturestested in soil

Benzene KeroseneXylenes Gasoline (87 octane)Naphthalene Gasoline (91 octane)Phenanthrene Gasoline (E85)Styrene Crude oil (Colorado sweet)Thiophene Diesel fueln-Decane Multi-purpose oil1,2-Dibromoethane Brake uid1,2-Dichlorobenzene Gasoline additiveMethyl-tert-butyl ether Gasoline octane boosterAcrylamideAcrylonitrile

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constructing 50% gray-based difference maps of the beads bymerging the inverse of “before” images with “aer” imagesusing 50% transparency (Fig. S1†). Beads that gave the strongestresponses were picked up with a ame-pulled pipette andtransferred into a capillary tube for decoding by release of thechemical tags, which were analyzed by electron-capture gaschromatography.29

Petroleum-based analytes

Analytes were STP Gas Treatment, Turbo 108 Octane Booster,Prestone High Temperature Synthetic Brake Fluid, 3-IN-ONEMulti-purpose Oil, Clean-Strip Kerosene heater fuel (HomeDepot), 87-octane gasoline (Valero), 91-octane gasoline (Shell),E85 gasoline (Propel) and diesel fuel (Valero). Crude oil wasColorado Sweet Crude (Eaton formation).

Sample preparation and screening

For the preparation of soil samples contaminated with petro-leum-based analytes, crude soil (Earthgro top soil, Home Depot,40 lb bag) was baked in an oven (ca. 200 �C) for 2 days to removeexcess water, then ltered with a sieve. The ne soil was thenplaced at room temperature open to air for 3 days before use.When doing a sensing experiment, a calculated amount of soilwas weighed and placed in a 100 or 1000mL round-bottom asksealed with a rubber septum. Aer 30 minutes' equilibration inthe ask, 5 mL of vapor above the soil was extracted by gas-tightsyringe and injected in a sealed 5 mL quartz Hellma QS 111cuvette containing sensor beads. The cuvette was kept at roomtemperature for 30 minutes and then a uorescence image wastaken as blank background. Then a calculated amount ofpetroleum analyte was added into a second weighed soil samplein a similar round-bottom ask by pipette and the sealed bottlewas shaken vigorously to evenly mix the sample. The samplebottle was equilibrated at room temperature for another 30minutes. 5 mL of the contaminant vapor was extracted andinjected in the sensor-containing cuvette which was pre-evacu-ated under high vacuum. Then the cuvette was vented with asyringe needle and another 10mL of the contaminant vapor wascontinuously injected via syringe pump during 30 minutes tokeep the concentration of vapor consistent. A uorescenceimage of the beads was taken 30 minutes aer exposure to thecontaminant vapor. Care was taken not to disturb the beadsbetween images.

Optical and statistical analysis

Fluorescence change data from bead images were analyzed withAdobe Photoshop by reading the RGBL (red, green, blue, luma)values of the subtracted images before and aer analyte expo-sure. Average RGBL values were determined from a 16 � 16pixel box in the center of each bead image. Quantitative colorchanges aer exposure to VOCs, expressed in DR, DG, DB andDL on a �256 unit scale, were calculated for each bead andaveraged (no less than three beads per analyte for a givensensor). Standard deviations and standard errors were deter-mined to evaluate the accuracy and reproducibility of theresponses. The statistical calculations, principal component

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analysis (PCA) and agglomerative hierarchical clustering (AHC)analysis, were performed with Addinso XLSTAT soware usingDR, DG and DB values as input.

ResultsDNA-polyuorophore design and composition

The DNA-polyuorophores in this study were composed ofvarious combinations of eight different monomer uorophorenucleosides (Y, E, V, F, Q, K) and spacer groups (S, H) (Fig. 1A).The goal in choosing the monomers was to yield a range ofcolors across the spectrum, to enable changes in the blue,green, and red channels. For simplicity and small size, welimited lengths to tetramers; this yields 4096 unique sequences,which are denoted by letters describing the monomers in 50 /30 direction, analogous to DNA. The library was constructed on130-micron PEG–polystyrene beads by standard split-and-mixmethodology, and sequences were encoded on the beads withchemical tags. The library of sequences could be readily imagedby epiuorescence microscopy (Fig. 1C); for imaging we used asingle excitation lter (340–380 nm) and observed all visibleuorescence in real time (RGB camera) with a 420 nm long-passlter. The library showed a broad range of uorescence prop-erties in air, with emission colors ranging from blue to white tored and varying greatly in brightness.

Selection of a set of ODFs for differentiation of industrialvapors

Our initial goal was to identify a preliminary set of ODFsequences that could respond broadly and with good intensityto a wide range of possible industrial organic contaminants,including many petroleum components and petrochemicals.Thus we chose a set of twelve organic volatile compounds,including petroleum components (alkanes, alkenes, aromatics)and halogenated hydrocarbons (Tables 1 and S2†). These werescreened one at a time with the ODF library by simply placing adrop next to beads in a chamber, yielding near-saturated vaporsin air. Changes in uorescence aer 30 min were detected bybefore/aer difference maps (see Fig. S1 in ESI†); beads thatgave the greatest changes were picked and sequenced. We

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arrived at a set of twenty ODF sequences that each respondedstrongly to at least one of the twelve compounds. These twentysequences on beads were re-synthesized individually andcharacterized by absorption and emission spectra and MALDI-mass spectrometry (Table S1 and Fig. S2†).

We then set out to narrow this group of twenty candidatechemosensors to a small minimal set by carrying out cross-screening experiments, measuring quantitative responses ofeach ODF to each of the twelve petrochemical compounds.Graphical maps of the cross-screening data for the 240combinations are given in Fig. S3 and S4,† and quantitativeR,G,B response data were recorded with statistical soware.We used Principal Component Analysis (PCA) and Agglomer-ative Hierarchical Clustering (AHC) to group the candidatechemosensors for their diversity of response and ability todifferentiate the analytes (Fig. 2, 3 and S5†). Using the clus-tering analysis we then chose varied sets of ve sensorsequences from dissimilar classes (Fig. 3 and S12†) andstatistically compared their ability to differentiate the indus-trial analytes. This resulted in a nal optimal set of ve ODFsequences that best differentiated the VOC compounds: YSES,EYYY, YQFS, FEYF, and SSEK (Fig. S7; see three-dimensionalscattering in Movie_S1†). Other sets of ve also successfully

Fig. 2 Scattering data for 12 pure VOCs by Principal Component Analysis (PCA).(A) Plot of data projected to the first two principal axes (F1 vs. F2). (B) Orthogonalplot (F1 vs. F3). SeeMovie_S1† for animated 3D representation of scattering alongthe three axes.

Fig. 3 Dendrograms of Agglomerative Hierarchical Clustering (AHC) analysis of20 chemosensor responses to the 12 VOC analytes. (A) Grouping of analytes bydissimilarity of responses; (B) grouping of sensors by dissimilarity of theirresponses to all twelve analytes.

This journal is ª The Royal Society of Chemistry 2013

differentiated the twelve analytes (Fig. S12†), but this set gavethe largest scattering of responses overall.

Differentiating petroleum-based mixtures in contaminatedsoil

The above experiments determined a set of ve chemosensorODFs that responded with diverse optical signatures to indi-vidual components of oil and to petroleum-derived compounds.We then used this set of chemosensors to analyze petroleum-based products in soil, representative of the varied types ofmixtures that might be found in samples taken during reme-diation of spills and leaks. To this end, we developed a simplestandardized approach, mixing measured amounts of a givenproduct in soil and allowing it to equilibrate in a glass ask withair above the sample. This headgas was then injected by slowow (30 min) through a quartz optical cell containing the ODFbeads (see Experimental section and Fig. S6 in ESI†). Onceagain, digital images of beads before (uncontaminated soilonly) and aer exposure to contaminated soil were subtracted,and changes were quantied as DR, DG, DB data.

The petroleum-based mixtures tested included 87- and 91-octane gasoline, E85 gasoline, diesel fuel, kerosene, crude oil,multipurpose oil, brake uid and two commercial gasoline

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Fig. 5 Dendrogram of hierarchical clustering analysis for three petroleumcontaminants at 1 ppm in soil, showing dissimilarity of analyte responses ascompared with uncontaminated soil. Three replicates of each analysis are shownto illustrate the reproducibility of the measurements.

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additives (Table 1). Experiments were initially carried out at1000 ppm in soil (i.e., 10 mg in 10 g soil), and all ten mixtureswere individually tested with the ve ODF sequences, with threereplicates each to allow for a measure of error. The DR, DG, DBdata are given in Fig. S7 and S8, and statistical plots are shownin Fig. S9.† Examination of the R,G,B changes showed that allthe mixtures gave similar overall responses, as expected sincenearly all of the mixtures are closely related in main composi-tion. Responses varied widely with ODF sequence; for example,YQFS yielded light-up responses for most analytes, while EYYYcommonly gave quenching responses (Fig. S8†). Overall, errorbars were small, showing good precision and repeatability, andthis allowed small differences among the analytes to be evalu-ated quantitatively. The PCA analysis of the multidimensionaldata showed good scattering of all ten analyte mixtures(Fig. S9†). Notably, the rst two principal component axes (F1and F2) accounted for only 67% of the scattering data, showinghigh dimensionality of the responses of the ve ODFs. Thusdata were better represented in 3D than in 2D plots; a 3D videois available (Movie_S2†). A dendrogram of the responsesgenerated by AHC analysis is shown in Fig. S9.† The mostsimilar responses were seen for crude oil and brake uid; nextmost similar were diesel fuel and one gasoline additive. Themost dissimilar responses (i.e., the most widely resolved) wereseen for the crude oil/multipurpose oil samples relative to therened gasolines. Importantly, measurements showed highrepeatability and precision: all repeats of measurements (takenwith sensors on different beads) grouped together with very lowdissimilarity.

Testing limits of detection in soil

Finally, we carried outmeasurements to evaluate the ability of theve chemosensors to detect such mixtures at lower concentra-tions in soil. To carry this out we selected three of the petroleumproducts andmeasured responses of the ve-sensor set at furthertenfold dilutions in soil: 10 ppm and nally 1 ppm. The threeanalytes tested in this way were kerosene, E85 gasoline, and 91-octane gasoline. These were againmeasured with three replicateseach to obtain a measure of the precision and repeatability. The

Fig. 4 Scattering of data for sensing petroleum contaminants at 1 ppm in soil byPrincipal Component Analysis (PCA). 2-D projection of data (F1 vs. F3) is shown.Data were obtained from five chemosensors (YSES, EYYY, YQFS, FEYF, SSEK).

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RGB response data for the 1000, 100, and 10 ppm contaminantsare given in Fig. S10;† comparison shows, as expected, that thehigher the concentration, the larger the responses are. However,the 10 ppm analytes were all readily detected with nonzeroresponses and were separated from each other (ESI†). We thenproceeded to analyze the 1 ppm data, which are plotted in Fig. 4.The results show that the rst three dimensions of the principalcomponent analysis yield clear separation of the three analytes.Clustering analysis (Fig. 5) shows that all three are easily resolvedfrom uncontaminated soil.

Thus the data show that the ve ODFs are able to detect andidentify three related petroleum contaminants in soil at levels atleast as low as one part per million.

Discussion

Our data show that a minimal set of ve DNA-like ODF uo-rophores on beads was able to differentiate all ten of thepetroleum-based contaminants in soil. Examination of thedendrograms of the AHC analysis from the uorescenceresponses (Fig. S9†) shows the relationships of similarity/dissimilarity aer the measurements. The data show thatrepetitions of the uorescence measurements in all casesgrouped a given product with another replicate of the sameproduct (see also Movie_S2†), giving high condence that thedistinct mixtures were well differentiated from one another in arepeatable way. Indeed, the level of discriminating ability issurprising, considering the closely related compositions ofsome of the analytes tested here. For example, kerosene, 91-octane gasoline, 87-octane gasoline, and diesel fuel are verysimilar in composition,30 yet these four mixtures were differ-entiated with high condence.

Although the compositions of all the commercial productswe tested are not publicly known, our statistical groupings allowus to categorize them by similarity to other mixtures. Oneexample of such an unknown sample is the gas treatment uid,which showed closest similarity to diesel fuel and 87-octanegasoline, suggesting that its base composition may be close tothese. It is not surprising that the less-rened oil samples and

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E85 fuel were strongly differentiated from the rened gasoline-like products, as the mixtures are no doubt chemically quitedistinct. However, it was unexpected how well-differentiated the91-octane gasoline was from the 87-octane gasoline and dieselfuel. It seems possible in this case that the chemosensors maybe responding to an additive in the high-octane blend that isnot present in the others. Overall, the results highlight the factthat pattern-based sensing is especially adept at comparingcomplex analytes, even when their compositions are not known.

The current set of ODF chemosensors was chosen based onits ability to differentiate a wide range of single chemicalcomponents that can be found in petroleum and other indus-trial spills. This approach is expected to yield a set of dyes thatcan respond with versatility to a wide range of different indus-trial spills, although more testing would be needed with non-petroleum mixtures to establish whether they would be aseffective as they were here. It seems possible that greaterseparating ability might be possible for specic analytemixtures if desired (for example, stronger differentiation ofdiesel from high-octane gasoline); this might be achieved byscreening a library of ODFs directly with specic analytemixtures rather than single compounds. Future experimentscould readily test this hypothesis.

The ODF uorophores studied here were highly sensitive tothe current petroleum contaminants. Our data showed thatthree of the contaminant mixtures were readily detected at1 ppm in soil (the equivalent of onemilligram per kilogram). Forreference, New York State standards for gasoline-contaminatedsoil places “Alternative Guidance Value” limits on most indi-vidual components (e.g., MTBE, xylene, naphthalene) at 0.1–1ppm.31 Since we detected total gasoline mixtures at 1 ppm, thecomponents were at much lower concentrations than theseguidance values. We attribute the high sensitivity to multiplepossible factors: rst, the use of uorescence allows for inher-ently high sensitivity and wide dynamic range of response.Second, the placement of the ODFs on PEG–polystyrene beadsalso likely enhances response by concentrating the nonpolarvolatiles near the uorophores; consistent with this notion, wedid observe evidence of bead swelling in the most concentratedsamples tested here. Third, the large aromatic surfaces in theODFs may interact favorably with many aromatic components inpetroleum. It would certainly be of interest in the future to testresponses of these sensor compounds to petroleum contami-nants at levels yet lower than 1 ppm in soil. However, in thepractical sense it becomes difficult (in laboratory experiments) toprepare samples of lower concentration than this, as the liquidsbecome difficult to measure and dispense at volumes below500 nanoliters, and soil samples in the laboratory becomeincreasingly unwieldy to handle in amounts above 500 g.Nevertheless, with more sophisticated mixing and dilutionstrategies (and higher-volume equipment) such experimentsmay be of interest in the future, as workers involved in reme-diation may wish to measure contaminant concentrations lowerthan 1 ppm at the margins of a spill.

The pattern-based approach to optical sensing offers anumber of practical advantages over other approaches foranalysis of complex mixtures. The use of uorescence changes

This journal is ª The Royal Society of Chemistry 2013

gives a large degree of data (here, a �256-unit scale at threedifferent wavelength ranges); this complexity in ve differentsensor compounds gives a large potential range of multidi-mensional responses (ca. 1040). As a result, one can analyze alarge number of distinct samples with a small number of sensormolecule structures. Second, as mentioned above, it is impor-tant to note that in this pattern-based approach there is no needto know a priori what the chemical composition is of a givenanalyte mixture. Indeed, one may never require this informa-tion, since differences can be distinguished reproducibly by thedistinct optical responses. On the other hand, if one does desireto know the chemical composition, one can identify specicmixtures simply by testing prior training samples to dene theexpected pattern in the chemosensors. A third benet of usingoptical pattern-based responses is that they can be extremelyeffective at differentiating closely related mixtures. Recentexamples of this include differentiating coffee17 and winesamples,16 identifying different classes of cultured bacteria,19,20

and differentiating petroleum mixtures in the current study. Anal advantage of this approach is its simple practical imple-mentation, using disposable chemosensors and measuringoptical changes in a simple way that could be readily automatedand taken to the eld. This may incur lower cost and requirepotentially less training than current methods; moreover, it alsoallows for portability, which is signicant because the usercould analyze fresher samples containing more volatiles thatmight otherwise be lost during storage and transport.

More work will be helpful in addressing practical issues inoptical sensing before ODFs can be employed in remediation ofauthentically contaminated soil samples. For example, devel-opment of methods for arraying the chemosensors on xedsurfaces would aid in array/pattern standardization relative tothe current use of beads randomly scattered on slides. Second,it will be of interest to assess specically how the headgas over asoil sample is optimally sampled and owed over the sensors,since this may well affect sensitivity. Future work will bedirected at these issues.

Acknowledgements

We acknowledge Eni S. p. A. and the U.S. National Institutes ofHealth (GM067201) for support. We also thank S. K. Edwardsand E. M. Harcourt for assistance with HPLC and uorescencemeasurements.

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