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Record W2663475253 · doi:10.1021/acs.nanolett.7b01857

Measuring Spatially Resolved Collective Ionic Transport on Lithium Battery Cathodes Using Atomic Force Microscopy

2017· article· en· W2663475253 on OpenAlexafffund
Aaron Mascaro, Zi Wang, Pierre Hovington, Yoichi Miyahara, Andrea Paolella, Vincent Gariépy, Zimin Feng, Tyler Enright, C. P. Aiken, Karim Zaghib, Kirk H. Bevan, Peter Grütter

Bibliographic record

VenueNano Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsHydro-QuébecMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsIonic bondingChemical physicsIonCathodeDensity functional theoryLithium (medication)Battery (electricity)Materials scienceAnodePhase (matter)NanotechnologyChemistryMolecular physicsComputational chemistryThermodynamicsPhysical chemistryPhysicsElectrode

Abstract

fetched live from OpenAlex

One of the main challenges in improving fast charging lithium-ion batteries is the development of suitable active materials for cathodes and anodes. Many materials suffer from unacceptable structural changes under high currents and/or low intrinsic conductivities. Experimental measurements are required to optimize these properties, but few techniques are able to spatially resolve ionic transport properties at small length scales. Here we demonstrate an atomic force microscope (AFM)-based technique to measure local ionic transport on LiFePO 4 to correlate with the structural and compositional analysis of the same region. By comparing the measured values with density functional theory (DFT) calculations, we demonstrate that Coulomb interactions between ions give rise to a collective activation energy for ionic transport that is dominated by large phase boundary hopping barriers. We successfully measure both the collective activation energy and the smaller single-ion bulk hopping barrier and obtain excellent agreement with values obtained from our DFT calculations.

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 categoriesMeta-epidemiology (narrow)
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 score0.035
Threshold uncertainty score1.000

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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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.

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

Citations37
Published2017
Admission routes2
Has abstractyes

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