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Record W2160621742 · doi:10.1038/srep01622

Hierarchical silicon nanowires-carbon textiles matrix as a binder-free anode for high-performance advanced lithium-ion batteries

2013· article· en· W2160621742 on OpenAlexfundno aff
Bin Liu, Xianfu Wang, Haitian Chen, Zhuoran Wang, Di Chen, Yi‐Bing Cheng, Chongwu Zhou, Guozhen Shen

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaProgram for New Century Excellent Talents in UniversityWuhan National Laboratory for OptoelectronicsNatural Science Foundation of Hubei ProvinceCollege of Family Physicians of Canada
KeywordsMaterials scienceAnodeSiliconLithium (medication)GraphiteNanotechnologyNanowireElectrodeCarbon fibersElectrochemistryChemical engineeringOptoelectronicsComposite materialComposite numberChemistry

Abstract

fetched live from OpenAlex

Toward the increasing demands of portable energy storage and electric vehicle applications, the widely used graphite anodes with significant drawbacks become more and more unsuitable. Herein, we report a novel scaffold of hierarchical silicon nanowires-carbon textiles anodes fabricated via a facile method. Further, complete lithium-ion batteries based on Si and commercial LiCoO2 materials were assembled to investigate their corresponding across-the-aboard performances, demonstrating their enhanced specific capacity (2950 mAh g(-1) at 0.2 C), good repeatability/rate capability (even >900 mAh g(-1) at high rate of 5 C), long cycling life, and excellent stability in various external conditions (curvature, temperature, and humidity). Above results light the way to principally replacing graphite anodes with silicon-based electrodes which was confirmed to have better comprehensive performances.

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)0.001
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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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

Citations153
Published2013
Admission routes1
Has abstractyes

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