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Record W2906141451 · doi:10.1149/ma2018-02/4/254

Thermally Treated Sodium Carboxymethyl Cellulose Binder for Si Alloy Anodes

2018· article· en· W2906141451 on OpenAlexaff
Zilai Yan, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials scienceCarboxymethyl cellulosePolyacrylonitrilePolyvinylidene fluorideChemical engineeringAlloyPolymerAnodeElectrodeComposite materialSodiumMetallurgyChemistry

Abstract

fetched live from OpenAlex

Binder is a key component for enabling good cycle life of Si and Si-based negative electrode materials in Li-ion cells, despite comprising only 2-10 wt% of the electrode coating. In previous research, thermal carbonization of polymer binders, e.g. polyvinylidene fluoride (PVDF),1 cyclized polyacrylonitrile (PAN),2 and poly(amide imide),3 was shown to enhance the cycling performance of high energy density alloy anodes greatly. For instance, sintered Si electrodes with PVDF binder can maintain a reversible capacity of 1150 mA h g-1 over 500 cycles at 1C rate.1 Among various binders, sodium carboxymethyl cellulose (Na-CMC) has some favorable characteristics: abundance, low cost, high water solubility, good thickening properties, and a high carboxmethyl group content, which is responsible for surface binding interactions with Si.4,5 To our best knowledge, the effect of heat treatment on Na-CMC binder for Si-alloy containing anodes has not been explored yet.6 Here, electrodes were composed of 60 wt% Si alloy, 28 wt% graphite, 2 wt% carbon black (CB), and 10 wt% Na-CMC. Some of electrodes were sintered under Ar flow by heating up to 300 or 600 °C at a rate at 10 ºC/min, and then held at 300 or 600 °C for three hours. Figure 1(a) shows the TGA curve of Na-CMC polymer in the temperature range of 25-800 ºC under Ar gas. After being heated at 600 ºC in Ar, Na-CMC has a mass residue of 38.6 wt.%. The pyrolysis product consists of amorphous carbon (42 wt.%) and Na2CO3 (58 wt.%). The cycling capacity retention of Si-alloy/graphite electrodes with Na-CMC binder is shown in Figure 1(b). The unheated electrodes suffer from significant capacity fade, having only 78% capacity left after 100 cycles. The capacity retention is improved when the thermal treatment is applied. After heating at 300 ºC, the capacity retention after 100 cycles was improved to 87%. A significant improvement of capacity retention results when 600 ºC thermal treatment is applied: over 95% capacity is retained. The formation of Na2CO3 in a network of amorphous carbon contributes to this high capacity retention. Reference F. M. Hassan, V. Chabot, A. R. Elsayed, X. Xiao, and Z. Chen, Nano Lett., 14, 277–283 (2014). F. M. Hassan, R. Batmaz, J. Li, X. Wang, X. Xiao, A. Yu, and Z. Chen, Nat. Commun., 6, 8597 (2015). H. S. Yang, S. H. Kim, A. G. Kannan, S. K. Kim, C. Park, and D. W. Kim, Langmuir, 32, 3300–3307 (2016). U. S. Vogl, P. K. Das, A. Z. Weber, M. Winter, R. Kostecki, and S. F. Lux, Langmuir, 30, 10299–10307 (2014). C. C. Nguyen, T. Yoon, D. M. Seo, P. Guduru, and B. L. Lucht, ACS Appl. Mater. Interfaces, 8, 12211–12220 (2016). R. Petibon, V. L. Chevrier, C. P. Aiken, D. S. Hall, S. R. Hyatt, R. Shunmugasundaram, and J. R. Dahn, J. Electrochem. Soc., 163, A1146–A1156 (2016). Figure 1 (a)TGA curves of Na-CMC collected at a heating rate of 5 ºC/min under Ar gas. (b) Capacity retention of three V6/SFG6L/CB/Na-CMC electrodes: untreated, heated at 300 ºC in Ar, and heated at 600 ºC in Ar. 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 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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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Citations1
Published2018
Admission routes1
Has abstractyes

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