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Microstructure Effects on Electrochemical Characteristics for Plasma Spray Deposited LiFePO4 Films

2014· article· en· W1542330442 on OpenAlexvenueno aff
B. C. Satishkumar, Xuan Zhou, Nick Moroz, Pravansu Mohanty, Bi Fu, Yaping Wang

Bibliographic record

VenueJournal of Coating Science and Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMicrostructureMaterials scienceElectrochemistryPlasmaThermal sprayingChemical engineeringMetallurgyNanotechnologyChemistryElectrodeCoating

Abstract

fetched live from OpenAlex

The electrochemical behavior of composite electrodes used in Li ion batteries is influenced by factors such as microstructural characteristics (e.g. particle size, crystallinity, porosity etc.) and composition. For optimal performance of electrodes these factors are of utmost concern and serve as motivation for research in this field. In this report, we investigated LiFePO4 films synthesized by a novel plasma spray deposition method, which has capability for direct deposition of LiFePO4 films with carbon. This enables electrode characterizations to be carried out at the film level, without recourse to steps involving powder material handling. In this report microstructure and electrochemical properties of LiFePO4 films were investigated to elucidate their unique characteristics. Our studies show that factors such as porosity and microstructure of the films affect the electrochemical properties. The mechanical compression and thermal annealing experiments are shown to affect the electrochemical characteristics of LiFePO4 films. We show that annealing treatment leads to a drastic improvement in impedance and charge-discharge capacities for the LiFePO4 films. These treatments could serve to improve the electrode properties of porous film based materials for Li ion batteries and help us develop new film based materials for energy storage applications.

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)0.001
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 score0.009
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.003
GPT teacher head0.222
Teacher spread0.218 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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