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Record W2341440680 · doi:10.1149/ma2014-02/5/273

Compression of Si Alloy Electrodes to Increase Li-Ion Battery Energy Density

2014· article· en· W2341440680 on OpenAlexaff
Zhijia Du, R. A. Dunlap, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceElectrodeComposite materialVolume (thermodynamics)AnodeSlurryCoatingCalenderingChemistry

Abstract

fetched live from OpenAlex

Introduction Si is attractive for use in high energy density anode materials due to its high volumetric capacity of 2194 Ah/L (corresponding to Li15Si4) [1]. The volume expansion of Si can be diluted to improve cycle life by the use of active/inactive composite electrode materials [2, 3]. However, electrode processing conditions also plays an important role in performance improvement. For commercial energy cells, the electrode stack should have high energy density. Therefore, electrode compression or calendering is widely practiced in industry to increase energy density [4]. Si-based materials are typically hard and brittle and cannot be readily calendered. Therefore such coatings can have high porosities, which can result in low energy density. Here, we present a facile and viable method for the compression of Si alloy electrodes while maintaining their high volumetric capacity, low volume expansion and good cycling performance. Experimental Electrode slurries were made by mixing specific ratios of 3M L-20772 Si alloy [5] and LiPAA (Polyacrylic acid) solution in distilled water with/without the addition of SFG6L graphite (28% by weight). The slurries were coated on Cu foil using a 0.004 inch gap coating bar and dried at 120oC in air for 1 h. The electrode foils were then passed through a calender for calendering to ~20% prorosity. The electrode pore volume was the difference of the total coating volume minus the solids volume. Electrodes were assembled into 2325 size coin-type cells using 1M LiPF6 dissolved in EC:DEC:FEC (3:6:1 vol%) solution. Two Celgard separators and a lithium foil counter/reference electrode were used. Electrode thicknesses were measured to within ± 1μm with a Mitutoyo 293-340 precision micrometer. The morphology of electrodes was studied using the Phenom G2 pro desktop SEM. Results The porosity of an uncalendered Si alloy / LiPAA 91/9 w/w electrode is about 56%. When it is fully lithiated coin cell is disassembled, it was found the entire coating had expanded by 96% and the porosity was calculated to be 57%. Therefore the alloy does not expand into the available porosity in the coating. Instead, as shown in Figure 1, as the alloy expands by 96%, the pores expand by the same amount. The large volume fraction porosity in such electrodes makes their volumetric capacity relatively low (624 Ah/L). To increase the volumetric capacity, electrodes with various formulations were calendered to ~20% porosity. Figure 2 shows the cycling performance of some of these electrodes. Clearly, calendering has a detrimental effect on the cycling performance of Si alloy electrodes when graphite is not present. By adding graphite in the electrode, the cycling excellent cycling can result. Moreover the volumetric coating capacity is increased to 957 Ah/L while overall volume expansion is reduced to only 64%. In this manner high volumetric capacity, low volume expansion alloy electrodes with excellent cycling characteristics can be obtained. Mechanisms of volume expansion in alloy coatings and methods of improving volumetric capacity and lowering volume expansion will be discussed. References [1] M. N. Obrovac and L. Christensen, Electrochem. Solid-State Lett., 7, (2004) A93. [2] M.N. Obrovac, L. Christensen, Dinh Ba Le and J. R. Dahn, J. Electrochem. Soc., 154, (2007) A849. [3] O. Mao, R. L. Turner, I. A. Courtney, B. D. Fredericksen, M. I. Buckett, L. J. Krause and J. R. Dahn, Electrochem. Solid-State Lett., 2, (1999) 3. [4] T. Marks, S. Trussler, J. Smith, D. Xiong and J. R. Dahn, J. Electrochem. Soc., 158, (2011) A51. [5] L. Christensen, D. Ba Le, J. Singh and M.N. Obrovac, 3M Alloy Anode Materials, 27th International Battery Seminar & Exhibit, Ft. Lauderdale FL, March 15-18, 2010. http://multimedia.3m.com/mws/mediawebserver?mwsId=SSSSSufSevTsZxtUo8mv4x_1evUqevTSevTSevTSeSSSSSS--&fn=AnodeTechPaperPowerConf.pdf

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.221
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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