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Record W2035319239 · doi:10.1149/1.2724741

Tin–Transition Metal–Carbon Systems for Lithium-Ion Battery Negative Electrodes

2007· article· en· W2035319239 on OpenAlexafffund
A. D. W. Todd, R. E. Mar, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsAmorphous solidLithium (medication)TinMaterials scienceCarbidePrecipitationCarbon fibersAmorphous carbonTernary operationTransition metalBattery (electricity)Chemical engineeringElectrodeMetallurgyChemistryCrystallographyPhysical chemistryThermodynamicsOrganic chemistryComposite materialCatalysis

Abstract

fetched live from OpenAlex

Magnetron cosputter deposited ternary libraries of (, V and Co) ( and ) have been studied structurally and electrochemically using combinatorial and high-throughput methods. Each of the sputtered binary systems shows an amorphous composition range where the specific capacity for lithium decreases with M content. Adding carbon to the amorphous binaries, to make ternaries, causes the precipitation of crystalline Sn in the cases when or V, but not when . We believe this is because stable carbides of Ti and V exist but stable Co carbides do not. The sputtered system was found to have a large amorphous range and the initial amorphous atomic arrangement in certain compositions are stable over at least 27 charge-discharge cycles of . Crystalline Sn was found to precipitate in composition ranges having competitive specific capacity in the and libraries causing rearrangement of the atoms during cycling leading to poor capacity retention.

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)0.001
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 score0.287
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.234
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.

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

Citations115
Published2007
Admission routes2
Has abstractyes

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