MétaCan
Menu
Back to cohort
Record W2745400883 · doi:10.1002/cssc.201701281

Effects of the Formulations of Silicon‐Based Composite Anodes on their Mechanical, Storage, and Electrochemical Properties

2017· article· en· W2745400883 on OpenAlexafffund
Birhanu Desalegn Assresahegn, Daniel Bélanger

Bibliographic record

VenueChemSusChem · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique MontréalUniversité du Québec à Montréal
KeywordsMaterials scienceAnodeSiliconGrapheneComposite numberCarbon blackElectrochemistryElectrodeChemical engineeringComposite materialCarbon nanotubeNanotechnologyChemistryMetallurgy

Abstract

fetched live from OpenAlex

In this work, the effects of the formulation of silicon-based composite anodes on their mechanical, storage, and electrochemical properties were investigated. The electrode formulation was changed through the use of hydrogenated or modified (through the covalent attachment of a binding additive such as polyacrylic acid) silicon and acetylene black or graphene sheets as conducting additives. A composite anode with a covalently grafted binder had the highest elongation without breakages and strong adhesion to the current collector. These mechanical properties depend significantly on the conductive carbon additive used and the use of graphene sheets instead of acetylene black can improve elongation and adhesion significantly. After 180 days of storage under ambient conditions, the electronic conductivity and discharge capacity of the modified silicon electrode showed much smaller decreases in these properties than those of the hydrogenated silicon composite electrode, indicating that the modification can result in passivation and a constant composition of the active material. Moreover, the composite Si anode has a high packing density. Consequently, thin-film electrodes with very high material loadings can be prepared without decreased electrochemical performance.

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 score0.003
Threshold uncertainty score0.303

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)0.0000.000

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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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

Citations15
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueChemSusChemSame topicAdvancements in Battery MaterialsFrench-language works237,207