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Record W3136987676 · doi:10.1021/acs.nanolett.1c00317

Manipulating Oxidation of Silicon with Fresh Surface Enabling Stable Battery Anode

2021· article· en· W3136987676 on OpenAlexfundno aff
Gaofeng Ge, Guocheng Li, Xiancheng Wang, Xiaoxue Chen, Lin Fu, Xiaoxiao Liu, Eryang Mao, Jing Liu, Xuelin Yang, Chenxi Qian, Yongming Sun

Bibliographic record

VenueNano Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDepartment of Science and Technology, Hubei Provincial People's GovernmentMinistry of Science and Technology of the People's Republic of China
KeywordsMaterials scienceAnodeSiliconPassivationChemical engineeringComposite numberNanocompositeElectrochemistryCoatingCarbon fibersElectrodeGraphiteOxideLayer (electronics)Particle (ecology)Lithium-ion batteryNanotechnologyBattery (electricity)Composite materialOptoelectronicsMetallurgyChemistry

Abstract

fetched live from OpenAlex

Silicon (Si)-based material is a promising anode material for next-generation lithium-ion batteries (LIBs). Herein, we report the fabrication of a silicon oxide–carbon (SiOx/C) nanocomposite through the reaction between silicon particles with fresh surface and H2O in a mild hydrothermal condition, as well as conducting carbon coating synchronously. We found that controllable oxidation could be realized for Si particles to produce uniform SiOx after the removal of the native passivation layer. The uniform oxidation and conductive coating offered the as-fabricated SiOx/C composite good stability at both particle and electrode level over electrochemical cycling. The as-fabricated SiOx/C composite delivered a high reversible capacity of 1133 mAh g–1 at 0.5 A g–1 with 89.1% capacity retention after 200 cycles. With 15 wt % SiOx/C composite, graphite-SiOx/C hybrid electrode displayed a high reversible specific capacity of 496 mAh g–1 and stable electrochemical cycling with a capacity retention of 90.1% for 100 cycles.

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 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 score0.192
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.218
Teacher spread0.203 · 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.

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

Citations65
Published2021
Admission routes1
Has abstractyes

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