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SUPPLEMENTARY INFORMATIONARTICLE NUMBER: 16097 | DOI: 10.1038/NENERGY.2016.97

NATURE ENERGY | www.nature.com/natureenergy 1

Magnetically aligned graphite electrodes for high-rate performance Li-ion batteries

Supplementary information for

Magnetically aligned graphite electrodes for high

rate performance Li-ion batteries

Juliette Billaud1+, Florian Bouville2+, Tommaso Magrini2, Claire Villevieille1*, André R.

Studart2*

+ Authors have contributed equally to this work

* Corresponding authors: [email protected], [email protected]

1- Paul Scherrer Institut, Electrochemical Laboratory, 5232 Villigen PSI, Switzerland

2- Complex Materials, Department of Materials, ETH Zürich, 8093 Zürich, Switzerland

http://dx.doi.org/10.1038/nenergy.2016.97

2 NATURE ENERGY | www.nature.com/natureenergy

SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2016.97

Supplementary Figures

Supplementary Figure 1 | Comparison of the aligned and non-aligned anodes of this study (in

red and blue) with literature values for the areal capacity of anodes versus C-rate.

References: commercial graphite anodes1, Graphite SFG6 and SFG442,3, 3D Li4Ti5O124

Supplementary Figure 2 | Micrograph of graphite flakes (Alfa Aesar, Graphite flake, Natural, -

325 Mesh, 99.8%, metals basis) after alignment in water under a rotating magnetic field



SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2016.97

Supplementary Figure 3 | Setup used for casting of graphite suspensions followed by

magnetic alignment of flakes

Supplementary Figure 4 | Schematic drawing illustrating the effect of static and rotating

magnetic fields on the orientation of flakes.generated by a 400 mT Neodymium magnet. The

white arrow in the bottom left corner indicates the rotation plane of the magnet.




Supplementary Figure 5 | Effect of thickness reduction by calendering on the crystallographic

texture of the initially aligned sample. a. Comparison of the diffractogram obtained at different

thickness reductions on an electrode with aligned graphite platelets. The blue curve

corresponds to the pristine aligned electrode, whereas the red indicates the reference

sample. Peaks indexed with a * correspond to the copper current collector. b. Intensity of the

(002) peak in the aligned electrode after calendering divided by the intensity of the peak for a

reference electrode as a function of the relative decrease in thickness imposed by

calendering.




Supplementary Figure 6 | SEM micrograph of an aligned electrode after 50

lithiation/delithiation cycles. The current collector has been removed after cycling.

Supplementary Figure 7 | Galvanostatic cycle for high loading (9.1 mg/cm2) electrodes at

C/30 rate for the two first cycles and C rate for the remainder of the test.




Supplementary Figure 8 | Zeta potential of the graphite powder in water as a function of

the pH (Alfa Aesar, Graphite flake, Natural, -325 Mesh, 99.8%, metals basis).




Supplementary Figure 9 | Structural characterisation of electrodes. a. Pictures of the aligned

and reference electrodes generated after processing data obtained from the Focused Ion

Beam (FIB) / Scanning Electron Microscopy (SEM) images (structures are displayed at the

same scale). b. Example of diffusivity calculation indicating the boundary conditions assumed.




Supplementary Figure 10 | Streamline of the diffusive flux for two different concentration

gradient directions for the reference electrode and the aligned ones




Supplementary Table

Supplementary Table 1| Data used to calculate the tortuosity factor tensor.

Direction

Flux

(mol.m2/s)

C0

(mol/m3) L1 (m) ε Deff (m2/s) D0 (m2/s) τ

Aligned

x 1.59 10-5 10 1.93 10-5 0.32 9.55 10-11 3.00 10-10 3.15

y 2.07 10-6 10 3.67 10-5 0.32 2.38 10-11 3.00 10-10 12.66

z 1.21 10-5 10 2.07 10-5 0.32 7.84 10-11 3.00 10-10 3.84

Not

aligned

x 2.54 10-5 10 1.71 10-5 0.45 9.66 10-11 3.00 10-10 3.08

y 1.96 10-5 10 2.93 10-5 0.45 1.2710-10 3.00 10-10 2.34

z 6.86 10-6 10 1.40 10-5 0.45 2.13 10-11 3.00 10-10 13.95




Supplementary Notes

1. Comparison of the capacity of various anodes at different charging rates

We calculated the areal capacities of our electrodes and compared them in Supplementary

Fig. 1 with data reported in the literature. Several important aspects are illustrated in this

graph. First and most importantly, it shows that the alignment of flakes alone leads to a

remarkable increase in areal capacity, even if the graphite source used is not optimised for

batteries. Second, graphite flakes have been previously shown to result in high areal

capacities at high rate if the particles size is optimised for lithium insertion (see SFG6

compared to SFG44 grades). This implies that flake geometries (in addition to spherical

mesocarbon microbeads, MCMB-type) can also provide higher capacities that can potentially

rival commercial electrodes if the formulation is further optimised with current industrial know-

how. Finally, commercial graphite anodes exhibit the highest areal capacities, which reflects

the continuous incremental optimisation of these materials in industry. However, these high

capacities are only possible at low rate, as the highly-loaded electrodes (i.e. with an areal

capacities superior to 2 mAh/cm2) could not be cycled above C/33. The comparison between

the performance of experimental anodes with battery-grade graphite flakes (SFG6 and

SFG44) with commercial electrodes indicates that formulations that have been continuously

optimised in industry for decades can increase the area capacity by a factor of 3 as compared

to values reported for standard recipes in the open, academic literature. Such comparison

further emphasises the major breakthrough achieved in our study, since a similar 3-fold

increase in performance was achieved by solely aligning flakes in an orientation that

facilitates mass transport of Li ions through the electrode thickness. Although further work is

still needed, our approach clearly shows the great potential of controlling the architecture of

the electrode alone as a simple and effective means to significantly change the areal capacity

of industrially-relevant graphite anodes.

2. Effect of calendering on platelet alignment

Using X-ray diffraction, we followed the alignment of the platelets after calendering an aligned

electrode at various thicknesses. Changes in alignment of the flakes can be monitored

accurately by measuring the intensity of the (002) peak, as this peak intensity is

representative of the fraction of horizontally aligned platelets.X-ray diffraction measurements

were performed at room temperature with a PANalytical Empyrean diffractometer using Cu

Kα-radiation. The results are summarised in Supplementary Fig. 5. The intensity of the peak

does not increase until the thickness of the electrode has been decreased by 30%, with an

electrode thickness reduction of 45 µm in this case. This means that the aligned structure can

be compressed and densified by 30 % without any major misalignment of the platelets. The

increase in intensity observed if the thickness is reduced further indicates a progressive

reorganisation of the structure, while still preserving a preferred orientation. This is clearly

noticed in Supplementary Fig. 5 b if the results obtained for the aligned sample are compared

with those of a reference electrode as a function of thickness reduction.




Those preliminary results show that in our sub-optimised case, calendering can be performed

to a certain extent without strongly affecting the electrode alignment

Supplementary Methods

1. Calculation of the effective diffusivity tensor from FIB-Tomography data

1.1 Mesh production and optimisation

The stacks obtained from FIB-Tomography were segmented using the open source software

Fiji5 as described in the main text. The plug-in statistical region merging6 was used with a

number of merged regions of 25. Then a simple threshold was applied (Supplementary Fig. 9

a). The open source plug-in BoneJ7 was used to extract STL files from the binary stacks with

the isosurface function. The STL files represent an unscaled meshed surface, but usually with

a poorly controlled vertices quality and thus need to be cleaned, scaled and optimised before

use. The open source software Meshlab was employed to first remove the unconnected

regions by applying the function “Remove isolated pieces (wt. diameter)” with a diameter of

100 pixels. Then the function “Select self-intersecting faces” was used to clean the mesh

further. A scaling function was also applied to change the mesh size; in our case one voxel

equals to 60x60x60 nm3. The surface mesh was then imported into the open source software

Gmsh8 to produce a 3D mesh. The software native mesh optimising tools was used before

exporting them as NASTRAN files.

1.2. Effective diffusivity calculation

The procedure described here has been adapted directly from an example available in the

software COMSOL Multi-physics 5.2 library. The 3D meshes were imported directly as

NASTRAN files. An automatic face detection function was used to select the different entry

and exit faces (Supplementary Fig. 9 b). The material properties were selected to represent

lithium hexafluorophosphate (LiPF6) in 1:1 EC:DEC, which is the electrolyte composition we

used in our experimental measurements. A bulk diffusion coefficient 𝐷𝐷! = 3 ∙ 10!!" 𝑚𝑚!/𝑠𝑠 of

lithium in the electrolyte was assumed. A constant concentration of 𝐶𝐶! = 10 𝑚𝑚𝑚𝑚𝑚𝑚/𝑚𝑚! was

applied on one face and an external forced convection was applied on the other side, with a

mass transfer coefficient of 𝑘𝑘 = 1 𝑚𝑚/𝑠𝑠 and an external concentration equal to 𝐶𝐶!"# =

0 𝑚𝑚𝑚𝑚𝑚𝑚/𝑚𝑚!. The steady state is reached when the outward flux and concentration reaches a

constant level, named respectively 𝑗𝑗! and 𝑐𝑐!, as shown in Supplementary Fig. 9 b. Under such

condition, Fick’s first law can be written as:

𝑗𝑗! = −𝐷𝐷!"" ∙ ∇𝑐𝑐 (Eq. S1)




where 𝑗𝑗! is the outward flux (equal to 𝑗𝑗! = 𝑘𝑘 ∙ 𝑐𝑐!), 𝐷𝐷!"" is the effective diffusivity coefficient in

the considered direction and ∇𝑐𝑐 is the concentration gradient. Thus, 𝐷𝐷!"" is expressed as,:

𝐷𝐷!"" = 𝑗𝑗! ∙!!

!!!!!= 𝑘𝑘 ∙ 𝑐𝑐! ∙

!!!!!!!

(Eq. S2)

where 𝐿𝐿! is the thickness of the mesh in the considered direction.

Finally, 𝐷𝐷!"" can now be expressed as a function of the bulk diffusion coefficient 𝐷𝐷!:

𝐷𝐷!"" =!!∙ 𝐷𝐷! (Eq. S3)

with 𝜖𝜖 being the electrode porosity calculated from the mesh volume in COMSOL, and 𝜏𝜏 being

the tortuosity factor, which in this case is an adjustable parameter representing the electrode

morphology in the considered direction9. The data resulting from the analysis of the two

stacks shown in Supplementary Fig. 9 are summarised in Supplementary Table 1 and plotted

in Fig. 3 of the main manuscript.

1.3. Streamline of the diffusive flux

To further illustrate the modification of mass transport in our structure, we used the streamline

option of the Comsol Software to plot a number of lines representing the mass flux direction

for two cases, one where the concentration gradient was along the x direction, and one where

it was along the z direction, for both electrodes (cf. Supplementary Fig. 10). Indeed, the lines

are straight and parallel to each other when the tortuosity factor is low, and randomly oriented

when the tortuosity factor is high.




Supplementary References

1. Gallagher, K. G. et al. Optimizing Areal Capacities through Understanding the

Limitations of Lithium-Ion Electrodes. J. Electrochem. Soc. 163, A138–A149 (2016).

2. Buqa, H., Goers, D., Holzapfel, M., Spahr, M. E. & Novák, P. High Rate Capability of

Graphite Negative Electrodes for Lithium-Ion Batteries. J. Electrochem. Soc. 152,

A474 (2005).

3. Heß, M. & Novák, P. Shrinking annuli mechanism and stage-dependent rate capability

of thin-layer graphite electrodes for lithium-ion batteries. Electrochim. Acta 106, 149–

158 (2013).

4. Sun, K. et al. 3D printing of interdigitated Li-ion microbattery architectures. Adv. Mater.

25, 4539–43 (2013).

5. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat.

Methods 9, 676–82 (2012).

6. Nock, R. & Nielsen, F. Statistical region merging. IEEE Trans. Pattern Anal. Mach.

Intell. 26, 1452–1458 (2004).

7. Doube, M. et al. BoneJ: Free and extensible bone image analysis in ImageJ. Bone 47,

1076–1079 (2010).

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