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Record W1485237282 · doi:10.1149/2.0731509jes

Combinatorial Investigations of Ni-Si Negative Electrode Materials for Li-Ion Batteries

2015· article· en· W1485237282 on OpenAlexafffund
Zhijia Du, T. D. Hatchard, R. A. Dunlap, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsAnodeAmorphous solidMaterials scienceElectrodeIonThin filmVoltageMetalInternal stressTransition metalAnalytical Chemistry (journal)Chemical engineeringMetallurgyNanotechnologyChemistryCrystallographyComposite materialElectrical engineeringPhysical chemistryCatalysisChromatography

Abstract

fetched live from OpenAlex

Sputtered thin films in the Ni-Si system (0 ≤ x ≤ 0.65 in Ni x Si 1-x ) were studied for use as anode materials in Li-ion cells. All compositions were found to be amorphous. The Ni in Ni-Si films was found to suppress the lithiation voltage, resulting in a reduction in capacity. The delithiation voltage was not affected. No capacity was observed when Ni content was more than 50 at% because at this composition the lithiation voltage was suppressed to 0 V. In contrast to previous models of capacity in transition metal-Si films, all Si atoms were found to be active in Ni-Si films at all compositions. Capacity reduction is only caused by a suppression of the Si lithiation voltage. We attribute this voltage suppression to internal stress in the thin film during lithiation from the presence of Ni.

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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.257
Teacher spread0.238 · 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

Citations58
Published2015
Admission routes2
Has abstractyes

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