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Record W2920883350 · doi:10.1002/aenm.201803947

Dynamics of the Morphological Degradation of Si‐Based Anodes for Li‐Ion Batteries Characterized by In Situ Synchrotron X‐Ray Tomography

2019· article· en· W2920883350 on OpenAlexafffund
Victor Vanpeene, Julie Villanova, Andrew King, Bernard Lestriez, Éric Maire, Lionel Roué

Bibliographic record

VenueAdvanced Energy Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSynchrotronElectrodeAnodeSiliconIn situIonX-rayLithium (medication)TomographyCrackingDegradation (telecommunications)Composite materialChemical engineeringNanotechnologyOptoelectronicsOpticsElectronic engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract The alloying reaction of silicon with lithium in negative electrodes for lithium‐ion batteries causes brutal morphological changes that severely degrade their cyclability. In this study, the dynamics of their expansion and contraction, of their cracking in the bulk and of their debonding at the interface with the current collector are visualized by in situ synchrotron X‐ray computed tomography and quantified from appropriate 3D imaging analyses. Two electrodes made with same silicon material having reasonable particle size distribution from an applied point of view are compared: one fabricated according to a standard process and the other one prepared with a maturation step, which consists in storing the electrode in a humid atmosphere for a few days before drying and cell assembly. All morphological degradations are significantly restrained for the matured electrode, confirming the great efficiency of this maturation step to produce a more ductile and resilient electrode architecture, which is at the origin of the major improvement in their cyclability.

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 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 score0.021
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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".

Quick stats

Citations87
Published2019
Admission routes2
Has abstractyes

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