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Record W3081685353 · doi:10.1002/qua.26439

Can doping of transition metal oxide cathode materials increase achievable voltages with multivalent metals?

2020· article· en· W3081685353 on OpenAlexaff
Daniel Koch, Sergei Manzhos

Bibliographic record

VenueInternational Journal of Quantum Chemistry · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsDopingOxideTransition metalPolaronMetalChemical physicsMaterials scienceValence (chemistry)ElectrochemistryOxygenInorganic chemistryMetal K-edgeElectrodeDimerCathodeChemistryPhysical chemistryOptoelectronicsCatalysisElectron

Abstract

fetched live from OpenAlex

Abstract The use of substitutional p‐doping as a means to enhance the insertion energies of multivalent metals in transition metal oxides, and therefore the resulting voltages in an electrochemical cell, due to band structure modulation is investigated using first principles calculations. The investigations reveal the formation of n ‐hole polarons (with n > 1) in the form of oxygen dimers in p‐doped charge‐transfer insulating transition metal oxides, caused by localized p holes on oxide ions in agreement with previous findings. It is found that the oxygen dimer formation has an adverse effect on adsorption energetics compared to the single‐hole case without dimerization. On the other hand, strained systems or Mott insulators with qualitatively different valence band composition do not exhibit oxygen dimerization with multihole doping. The results demonstrate the advantages and limitations of transition metal oxide electrode p‐doping and show a path to possible strategies to overcome detrimental effects.

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 score0.025
Threshold uncertainty score0.603

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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