MétaCan
Menu
← Back to cohort
Record W4283574223 · doi:10.1149/1945-7111/ac797e

Preparation of Low Surface Area Si-Alloy Anodes for Li-Ion Cells by Ball Milling

2022· article· en· W4283574223 on OpenAlexafffund
Simeng Cao, Mohammad H. Tahmasebi, Shayne Gracious, Craig Bennett, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsAcadia UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAlloyBall millAnodeMetallurgyElectrolyteGraphiteSurface modificationAmorphous solidSpecific surface areaChemical engineeringElectrodeCatalysisCrystallographyChemistry

Abstract

fetched live from OpenAlex

A new and simple 2-step milling technique is utilized to produce Si–Ti–N alloys with significantly reduced surface area compared to conventional ball milling, while still attaining a full amorphous active Si phase. Surface area reductions of up to 100% were obtained by this method. Surprisingly, this did not result in significant differences in cycling stability compared to conventionally ball milled high surface area alloy materials. This is likely because cycling caused severe fracturing of the alloy surfaces, resulting in a high surface area, regardless of the initial surface area of the alloy. This suggests that, unlike other anode materials such as graphite, reducing the initial surface area of Si alloys does not translate into reduced electrolyte reactivity.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical Society→Same topicAdvancements in Battery Materials→French-language works237,207→