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Record W2142036122 · doi:10.1002/aenm.201301718

Very High Surface Capacity Observed Using Si Negative Electrodes Embedded in Copper Foam as 3D Current Collectors

2014· article· en· W2142036122 on OpenAlexafffund
Driss Mazouzi, David Reyter, Magali Gauthier, Philippe Moreau, Dominique Guyomard, Lionel Roué, Bernard Lestriez

Bibliographic record

VenueAdvanced Energy Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCurrent collectorFaraday efficiencyGravimetric analysisComposite materialCopperElectrodeGraphiteLithium (medication)Composite numberAnodeCarbon fibersMetallurgyElectrolyte

Abstract

fetched live from OpenAlex

Cu foam is evaluated as a replacement for metal foil current collectors to create 3D composite electrodes with the objective to produce Si‐based anodes with high loadings. The electrodes are prepared by casting the slurry into the porosity of the foam. With such a design, the loading and the surface capacity can reach values as high as 10 mg cm −2 and 10 mAh cm −2 . Compared to the common 2D design, the 3D copper framework shows a great advantage in the cycle life (more than 400 cycles at a Si loading of 10 mg cm −2 with commercial micrometric particles) and power performance. The thinness of the composite coating on the foam walls favors a better preservation of the electronic wiring upon cycling and fast lithium ion diffusion. A higher coulombic efficiency in half cells with lithium metal as the counter electrode is achieved by using carbon nanofibers (CNF) rather than carbon black (CB). The possibility to reach, in practice, higher surface capacity could allow a significant increase in both the volumetric and gravimetric energy densities by 23% and 19%, respectively, for the Cu foam‐silicon//LiFePO 4 stack compared to the graphite/LiFePO 4 stack of traditional design.

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 score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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

Citations72
Published2014
Admission routes2
Has abstractyes

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