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Record W2502687813 · doi:10.1149/2.0961609jes

Structural and Electrochemical Investigation of Fe<sub>x</sub>Si<sub>1-x</sub>Thin Films in Li Cells

2016· article· en· W2502687813 on OpenAlexafffund
Zhijia Du, R. A. Dunlap, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsAmorphous solidThin filmMaterials scienceAlloyElectrochemistrySputteringPhase (matter)DiffractionAnalytical Chemistry (journal)CrystallographyX-ray crystallographyChemical engineeringMetallurgyNanotechnologyChemistryElectrodePhysical chemistryOptics

Abstract

fetched live from OpenAlex

Fe x Si 1-x thin films with 0 ≤ x ≤ 0.46 have been prepared by combinatorial sputtering and their electrochemical properties have been studied in Li half-cells. X-ray diffraction showed that all of the films had an amorphous structure. Mössbauer effect spectra suggest that Fe was diluted in amorphous Si for x ≤ 0.23 and inactive FeSi 2 was formed with further increase in x. For x ≤ 0.23, all Si in the thin films was active toward lithiation/delithiation. At higher Fe contents, the capacity of Fe-Si thin films dropped rapidly and no capacity was observed for x greater than 0.33. The voltage curve of Si to become depressed during lithiation with increasing Fe content, which may be the result of stress induced between the active Si phase and inactive phases in the alloy. For alloys with x > 0.23, the formation of inactive FeSi 2 removes active Si from the alloy and results in the lowering of the reversible capacity.

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 score0.002
Threshold uncertainty score0.004

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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations22
Published2016
Admission routes2
Has abstractyes

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