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Record W2083454363 · doi:10.1149/1.3239988

Comparison of Mechanically Milled and Sputter Deposited Tin–Cobalt–Carbon Alloys Using Small Angle Neutron Scattering

2009· article· en· W2083454363 on OpenAlexafffund
A. D. W. Todd, P. Ferguson, J. G. Barker, Michael D. Fleischauer, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2009
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsDalhousie UniversityNational Institute for Nanotechnology
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Standards and TechnologyDalhousie UniversityNational Science Foundation
KeywordsMaterials scienceSputteringSputter depositionSmall-angle neutron scatteringGrain sizeTinAmorphous solidMetallurgyNeutron scatteringLithium (medication)AlloyCarbon fibersDeposition (geology)CobaltSmall-angle scatteringScatteringThin filmComposite materialNanotechnologyCrystallographyOpticsChemistry

Abstract

fetched live from OpenAlex

Small angle neutron scattering (SANS) is used to compare nanostructured Sn–Co–C alloys produced by vertical axis mechanical attriting to those produced by magnetron sputter deposition. The attrited materials have grain sizes that vary with composition and are on the order of in size. The sputter deposited materials are either amorphous or have a grain size of approximately , depending on the composition. The SANS results are used to further understand the electrochemistry of these materials when used as negative electrodes for lithium-ion batteries and to understand why mechanically alloyed Sn–Co–C alloys are far from reaching their expected theoretical specific capacity while sputtered alloys achieve capacities much closer to the expected value.

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 score0.003
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
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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

Citations28
Published2009
Admission routes2
Has abstractyes

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