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Record W2736878781 · doi:10.14447/jnmes.v14i1.126

Design and Synthesis of In Situ VGCFs Improved LiFePO 4 Composite Cathode Materials

2010· article· en· W2736878781 on OpenAlexvenueno aff
Fei Deng, Xierong Zeng, Jizhao Zou, Jianfeng Huang, Xinbo Xiong, Xiaohua Li, Hongchao Sheng

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
Fundersnot available
KeywordsCathodeMaterials scienceComposite numberGraphiteMicrostructureChemical vapor depositionComposite materialPyrolysisChemical engineeringNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

One of the most important factors which currently limit the application of LiFePO4 cathode material in lithium-ion batteries is its low electronic conductivity. In order to enhance the electronic conductivity of LiFePO4 cathode material, in situ vapor-grown carbon fibers (VGCFs) improved LiFePO4 composite cathode materials were designed and synthesized in one step by microwave pyrolysis chemical vapor deposition (MCVD). The phase, microstructure and electrochemical performances of the composite cathode materials were investigated. Results show that network-like VGCFs formed during the MCVD process generally grow with an in situ growth mode on the graphite particles, which is extremely beneficial to improve the electronic conductivity of the composite cathode materials. The initial discharge capacity of the composite cathode materials, compared with the cathodes without in situ network-like VGCFs, increases from 109 mAhg-1 to 144 mAhg-1 at 0.5C rate, and the total electric resistance corresponding to the electron jumping varies from 538 Ω to 66 Ω.

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 score0.001
Threshold uncertainty score0.003

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 head0.011
GPT teacher head0.241
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 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

Citations2
Published2010
Admission routes1
Has abstractyes

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