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Record W4246959215 · doi:10.1149/ma2016-03/2/545

Electrochemical Studies of Carbon Nanotube-LiFePO<sub>4</sub> Nanocomposite Cathode for High-Capacity Lithium-Ion Batteries

2016· article· en· W4246959215 on OpenAlexaff
Xiangcheng Sun, Kun Feng, Zhongwei Chen, Bo Cui

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNanocompositeMaterials scienceCarbon nanotubeElectrochemistryLithium (medication)Cyclic voltammetryCathodeComposite numberChemical engineeringTransmission electron microscopyScanning electron microscopeNanoparticleNanotechnologyComposite materialElectrodeChemistry

Abstract

fetched live from OpenAlex

LiFePO4 nanoparticles were incorporated with multi-walled carbon nanotubes (CNTs) via a facile one-step polyol process at low temperature. CNTs were uniformly embedded into LiFePO4 particles and formed a conductive network to enhance the electrochemical performance of the CNT-LiFePO4 nanocomposite cathode. Structural composition and morphology of the composite were investigated in details by X-ray diffraction (XRD), scanning and transmission electron microscopies (SEM, TEM). The electrochemical properties were analyzed in the voltage of 2.0 and 4.0 V by charge/discharge testing and cyclic voltammetry. Primary experimental results showed that these CNT-LiFePO4 nanocomposite cathodes exhibited enhanced electrochemical performance with both high capacity and good reversibility which has been clearly demonstrated in the Fig. 1. Figure 1

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 score0.005

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 head0.020
GPT teacher head0.248
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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueECS Meeting Abstracts→Same topicAdvancements in Battery Materials→French-language works237,207→