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Record W3024099895 · doi:10.1149/ma2020-01502766mtgabs

First-Principles Study of the Polaron Formation Process in Energy Materials

2020· article· en· W3024099895 on OpenAlexaff
Shuaishuai Yuan, Kirk H. Bevan

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsPolaronChemical physicsMaterials scienceCondensed matter physicsRelaxation (psychology)Thermal conductionConductivityOxideLattice (music)ElectronNanotechnologyChemistryPhysical chemistryPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Polaron formation severely limits the conduction properties of many metal oxides utilized in electrochemical energy storage and conversion applications. Thus, to improve the charge performance of such energy devices, it is essential to understand the fundamental stages of polaron formation. In this work, we utilize the HSE06 hybrid functional to study the initial stage of the polaron formation process in a series of metal oxides. By separating out electronic and lattice energy contribution to the formation of electron polarons, we find that polaron formation barrier heights are directly correlated with an electronic relaxation delay, which is determined by the hybridization of the conduction band minimum. Our results indicate that the formation of polarons may be mitigated by suitably engineering the electronic structure of the material, through a delayed electronic relaxation mechanism. Overall, these results point towards a systematic bottom-up approach for engineering the conductivity and overall electrochemical rate performance of metal oxide energy materials.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.247
Teacher spread0.205 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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Same venueECS Meeting AbstractsSame topicZnO doping and propertiesFrench-language works237,207