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Record W4283787447 · doi:10.1002/aenm.202200850

Design Criteria for Silicon‐Based Anode Binders in Half and Full Cells

2022· article· en· W4283787447 on OpenAlexafffund
Li Deng, Yun Zheng, Xiaomei Zheng, Tyler Or, Qianyi Ma, Lanting Qian, Ya‐Ping Deng, Aiping Yu, Jun‐Tao Li, Zhongwei Chen

Bibliographic record

VenueAdvanced Energy Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAnodeMaterials scienceSiliconGraphiteLithium (medication)NanotechnologyVolume (thermodynamics)ElectrodePolymerEngineering physicsProcess engineeringComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

Abstract While silicon is considered one of the most promising anode materials for the next generation of high‐energy lithium‐ion batteries (LIBs), the industrialization of Si anodes is hampered by the anode's large volume change during the charging and discharging process. In comparison to the traditional graphite anode used in LIBs, the Si anode places more stringent demands on the binder, which must maintain intimate contact between the electrode components and the integrity of the ion and electron transport channels when subjected to frequent large volume changes. The purpose of this review is to cover the recent advances in binder design strategies by examining the molecular structure, chemical functionalities, physical and mechanical properties of the binder materials, as well as the working strategies involved. The challenges in the design of the innovative polymer binder for commercializing Si anodes are discussed, as well as the future development direction and application prospects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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.

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".

Quick stats

Citations158
Published2022
Admission routes2
Has abstractyes

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