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Record W2085816503 · doi:10.1149/2.083112jes

Alloy Negative Electrodes for High Energy Density Metal-Ion Cells

2011· article· en· W2085816503 on OpenAlexaff
Tuan T. Tran, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAlloyElectrodeElectrolyteMetalMaterials scienceAnodeIonVolume (thermodynamics)Analytical Chemistry (journal)ChemistryMetallurgyThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

The volume expansion behavior and volumetric energy densities of metal alloy negative electrodes (A x M) for metal-ion batteries based on A = Li, Na, K, Mg, Ca and Al are compared. It was found that each metal ion, A, occupies a characteristic volume in alloys which is almost insensitive to the host metal, M. All alloy systems, with the exception of K x M alloys, were found to have similar or greater volumetric energy density than conventional graphitic anodes in Li-ion cells. It is predicted that both the Mg x M and Al x M systems might achieve higher energy densities than possible with any Li-ion negative electrode known and operate at potentials at which organic electrolytes are known to be stable.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations102
Published2011
Admission routes1
Has abstractyes

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