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Record W2547701719 · doi:10.1021/acsami.6b12080

Carbon-Coated Silicon Nanowires on Carbon Fabric as Self-Supported Electrodes for Flexible Lithium-Ion Batteries

2016· article· en· W2547701719 on OpenAlexafffund
Xiaolei Wang, Ge Li, Min Ho Seo, Gregory Lui, Fathy M. Hassan, Kun Feng, Xingcheng Xiao, Zhongwei Chen

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooVehicle Technologies ProgramNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooGeneral Motors Corporation
KeywordsMaterials scienceLithium (medication)ElectrodeCarbon fibersSiliconNanotechnologySilicon nanowiresNanowireIonNanoarchitectures for lithium-ion batteriesElectrochemistryChemical engineeringOptoelectronicsComposite materialComposite numberOrganic chemistry

Abstract

fetched live from OpenAlex

A novel self-supported electrode with long cycling life and high mass loading was developed based on carbon-coated Si nanowires grown in situ on highly conductive and flexible carbon fabric substrates through a nickel-catalyzed one-pot atmospheric pressure chemical vapor deposition. The high-quality carbon coated Si nanowires resulted in high reversible specific capacity (∼3500 mA h g –1 at 100 mA g –1 ), while the three-dimensional electrode’s unique architecture leads to a significantly improved robustness and a high degree of electrode stability. An exceptionally long cyclability with a capacity retention of ∼66% over 500 cycles at 1.0 A g –1 was achieved. The controllable high mass loading enables an electrode with extremely high areal capacity of ∼5.0 mA h cm –2 . Such a scalable electrode fabrication technology and the high-performance electrodes hold great promise in future practical applications in high energy density lithium-ion batteries.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.238
Teacher spread0.228 · 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 teacher head, not a consensus.

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

Citations118
Published2016
Admission routes2
Has abstractyes

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