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Record W4289520727 · doi:10.1002/smtd.202200827

Nanoscale Morphological Characterization of Coordinated Binder and Solid Electrolyte Interphase in Silicon‐Based Electrodes for Li‐Ion Batteries

2022· article· en· W4289520727 on OpenAlexafffund
Lucas Huet, Philippe Moreau, Nicolas Dupré, Thomas Devic, Lionel Roué, Bernard Lestriez

Bibliographic record

VenueSmall Methods · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersEuropean Regional Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsElectrodeMaterials scienceInterphaseElectrolyteSiliconScanning electron microscopeDielectric spectroscopyChemical engineeringNanoscopic scaleElectrochemistryFaraday efficiencyNanotechnologyComposite materialChemistryOptoelectronics

Abstract

fetched live from OpenAlex

Abstract The physical crosslinking of polymeric binders through coordination chemistry significantly improves the electrochemical performance of silicon‐based negative electrodes. Scanning electron microscopy coupled with energy dispersive X‐ray spectroscopy is used to probe the nanoscale morphology of such electrodes. This technique reveals the homogeneous coordination of carboxylated binder with Zn cations and its layering on the silicon surface. The solid electrolyte interphase (SEI) formed after the first cycle is denser with Zn‐coordinated binder and preferentially observed on binder‐depleted zones. The superiority of coordinated binders can be attributed to their capacity to better stabilize the electrode and the SEI layer due to improved mechanical properties. This results in a lower SEI impedance, a higher first cycle coulombic efficiency, and a 40% improvement of capacity retention after 50 cycles for highly loaded electrodes of over 6 mAh cm–2.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.026
GPT teacher head0.320
Teacher spread0.293 · 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".

Quick stats

Citations11
Published2022
Admission routes2
Has abstractyes

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