MétaCan
Menu
Back to cohort
Record W4319788467 · doi:10.1002/slct.202204086

Binary Carbon Modification Promoting the Electrochemical Performance of Silicon Anode for Lithium‐Ion Batteries

2023· article· en· W4319788467 on OpenAlexaff
Yaxin Feng, Yang Zhang, Ye Song, Pingyun Li, Jie Liu

Bibliographic record

VenueChemistrySelect · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceAnodeSiliconChemical engineeringGraphiteElectrolyteSurface modificationElectrochemistryLithium (medication)Nanowire batteryElectrodeNanotechnologyComposite materialLithium vanadium phosphate batteryOptoelectronicsChemistry

Abstract

fetched live from OpenAlex

Abstract As the research on lithium‐ion battery cathode materials gradually breaks through the saturation, the anode materials for lithium‐ion batteries have received wide attention because of their promising future. Silicon electrodes, in particular, are attracting increasing attention. In this paper, the Si/G precursor was achieved by simple mechanical ball milling. Two silicon based composite electrode materials Si@TA and Si@TA‐G were synthesized by spontaneous polymerization of tannic acid on precursor surface in Tris buffer solution. For the poor electrical conductivity of silicon, graphite was added as a carbon source to avoid the direct contact between silicon and electrolyte during charge/discharge process by forming tannic acid coating on silicon surface.The Si@TA composites achieve a discharge specific capacity of 927.4 mAh g −1 at 100 mA g −1 current density after 50 cycles with a retention rate of 87.1 % while the reversible capacity of Si@TA‐G is 1249.8 mAh g −1 with a retention rate is 93.6 %. This unique composite method provides new insights into the modification of silicon anode materials.

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 score0.034
Threshold uncertainty score0.487

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueChemistrySelectSame topicAdvancements in Battery MaterialsFrench-language works237,207