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Record W3088866096 · doi:10.1149/1945-7111/abba91

Synthesis, Lithium Insertion and Thermal Stability of Si–Mo Alloys

2020· article· en· W3088866096 on OpenAlexafffund
Simeng Cao, Shayne Gracious, Craig Bennett, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsAcadia UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAlloyThermal stabilityBall millPhase (matter)Lithium (medication)Temperature cyclingElectrochemistryDiffractionX-ray crystallographyMetallurgyAnalytical Chemistry (journal)ThermalChemical engineeringElectrodeChemistryThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

Li insertion was investigated in SixMo100−x (90 ≥ x ≥ 70, Δx = 10) alloys prepared by mechanical ball milling. X-ray diffraction (XRD) and quantitative phase analysis were used to analyze phase compositions of these Si–Mo alloys, and how these phase compositions changed with milling times. The results of quantitative phase analysis showed that cr-Si converted into a-Si within 1 h during milling, and the Si–Mo reactions were nearly complete after only 4-h milling. During electrochemical cycling, the Si–Mo samples with high initial Mo contents and long milling times displayed good crystalline Li15Si4 suppression and stable cycling performance. In addition, thermal stability of some selected Si–Mo alloys was studied. The Si80Mo20 16 h alloy combines good thermal stability and a high volumetric capacity of about 1300–1400 Ah L−1 after heat treatment at 600 °C or 800 °C, which may allow the alloy to be further improved by carbon coating at high temperature.

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 score0.003

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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

Explore more

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