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Record W2524254381 · doi:10.1149/ma2016-02/3/490

The Role of Polymer Binders in Alloy Anodes

2016· article· en· W2524254381 on OpenAlexaffabout
Timothy Hatchard, Paul Bissonnette, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAnodic Oxide Films and Nanostructures
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceAlloyAnodePolyimideElectrolyteElectrodePolymerComposite materialLayer (electronics)Chemistry

Abstract

fetched live from OpenAlex

Portable electronics and electric vehicle applications require high energy density cells with long cycle life. Much attention has been given to Si and Si-based alloy negative electrodes because of their high theoretical volumetric capacity. These benefits are tempered by the large volume changes during charge/discharge cycling that lead to electrode failure [1]. To deal with this problem, many researchers are developing advanced binders. It has been shown that good binders for alloy materials provide good adhesion to the active materials and to the current collector, and complete coverage of the alloy particles [2]. It is suspected that by completely covering the surface of the alloy particles, binders can form an "artificial SEI" layer to reduce electrolyte decomposition reactions [2]. Other studies have shown that conductive polymers can be used as excellent binders for alloy negative electrodes [3, 4]. Aromatic polyimides (PI) have been shown to work well as binders for alloy negative electrodes [5]. PI provides excellent surface coverage of the alloy particles and adheres strongly to the alloy and the current collector. However, PI has first cycle irreversible capacity. Recent work by Wilkes et al. [6] argues that this first cycle irreversible capacity is due to the carbonization of the polyimide during the first lithiation (charge) of the anode, during which the polyimide undergoes a 34 electron reduction. The reduction product is thought to be hydrogen containing hard carbon, which serves as a high performance conductive binder for subsequent cycles. Here, it will be shown that the thermal reduction of binders to form hydrogen-containing carbons also results in electrodes with excellent cycling performance. Figure 1 shows a comparison of cycle life for a Si-based alloy electrode employing a polyimide binder that has been cured at 300 °C to a similar electrode that has been heated to 600 °C before cell construction. The cells perform very similarly, indicating that binder reduction by electrochemical lithiation can be thought of as equivalent to a thermal reduction of the PI binder. The resulting products are similar to a low temperature, hydrogen-containing carbon that provides a conductive framework and continuous coating for the alloy particles and allows excellent charge/discharge cycling. Conductive polymers also exhibit this high irreversible capacity [4, 7]. We suspect that they are also undergoing carbonization during their first lithiation. If carbonization of the binder is key to good cycling properties, the utility of using expensive conductive binders is questionable, when other more inexpensive polymers exist that undergo reduction during lithiation or can be thermally decomposed to produce conductive species. Figure 2 shows the cycle life and coulombic efficiency of an electrode similar to that of Figure 1, except utilizing an inexpensive phenolic resin binder. This presentation will discuss the carbonization of polymer binders and their role as a binder or as part of the active material itself, in the form of a conductive matrix that keeps alloy particles in electrical contact while allowing moderate expansion/contraction during cycling. The authors thank 3M Canada Co. and NSERC for funding this work under the auspices of the Industrial Research Chairs Program. References. [1] M.N. Obrovac, L. Christensen, Dinh Ba Le, and J.R. Dahn, J. Electrochem. Soc. , 154 , A849 (2007). [2] M.N. Obrovac, V.L. Chevrier, Chemical Reviews , 114 (23) , 11444 (2014). [3] G. Liu , S. Xun , N. Vukmirovic , X. Song , P. Olalde-Velasco , H. Zheng, V. S. Battaglia , L. Wang and W. Yang, Adv. Mater. , 23 , 4679 (2011). [4] S.P. Xun, X. Song, V. Battaglia and G. Liu, J. Electrochem. Soc. , 160 (6) A849 (2013). [5] J.S. Kim, W. Choi, K.Y. Cho, D. Byun, J. Lim, J.K. Lee, Journal of Power Sources , 244 , 521 (2013). [6] B.N. Wilkes, Z. L. Brown, L.J. Krause, M. Triemert, and M.N. Obrovac, J. Electrochem. Soc. 163 (3), A364 (2016). [7] H. Wu, G. Yu, L. Pan, N. Liu, M.T. McDowell, Z. Bao, Y. Cui, Nature Communications , 4 , 1943 (2013). Figure 1

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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 teacher head, 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
Published2016
Admission routes2
Has abstractyes

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