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Record W2022467457 · doi:10.1021/cm7027993

Surface Effects on the Physical and Electrochemical Properties of Thin LiFePO<sub>4</sub> Particles

2007· article· en· W2022467457 on OpenAlexaff
Karim Zaghib, A. Mauger, F. Gendron, C. Julien

Bibliographic record

VenueChemistry of Materials · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsMaterials scienceElectrochemistryNanoparticleChemical engineeringSurface modificationCoatingSurface layerLithium (medication)IonImpurityFerrite (magnet)CathodeLayer (electronics)Analytical Chemistry (journal)NanotechnologyComposite materialElectrodePhysical chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The structure of LiFePO 4 particles prepared by a new milling route has been investigated, with emphasis on surface effects found to be important for such small particles, whose sizes were distributed in the range 30–40 nm. The bulk and surface properties of the particles were investigated by a combination of XRD, TEM, FTIR, and magnetic measurements before and after application of a carbon coating intended to optimize the electrochemical performance of the powder used as a cathode element of a new generation of lithium-ion battery. Before the carbon coating was applied, the particles were well-crystallized in the bulk and free from any impurities, but they were surrounded with a disordered, ~8 Å thick surface layer in which the iron ions were in the Fe 3+ low-spin ( S = 1 / 2 ) configuration. Carbon coating at 750 °C reduced the disorder at the surface and switched the Fe 3+ ions in the surface layer to the high-spin ( S = 5 / 2 ) configuration. These results are discussed with respect to similar effects recently observed for ferrite nanoparticles used in spintronics and biological molecules such as proteins that contain heme Fe.

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 score0.001
Threshold uncertainty score0.462

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations148
Published2007
Admission routes1
Has abstractyes

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