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Record W3198034111 · doi:10.1063/5.0058704

Structure–property correlations for analysis of heterogeneous electrocatalysts

2021· article· en· W3198034111 on OpenAlexafffund
Elif Pınar Alsaç, Nataraju Bodappa, Alexander W. H. Whittingham, Yutong Liu, Adriana C. de Lazzari, Rodney D. L. Smith

Bibliographic record

VenueChemical Physics Reviews · 2021
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrocatalystProperty (philosophy)CatalysisYield (engineering)Reactivity (psychology)Materials scienceComputer scienceNanotechnologyBiochemical engineeringChemistryChemical physicsElectrochemistryPhysical chemistryElectrodeEngineering

Abstract

fetched live from OpenAlex

Heterogeneous electrocatalytic reactions are believed to occur at a minority of coordination sites through a series of elementary reactions that are balanced by minor equilibria. These features mask changes in reaction sites, making it challenging to directly identify and analyze reaction sites or intermediates while studying reaction mechanisms. Systematic perturbations of a reaction system often yield systematic changes in material properties and behavior. Correlations between measurable changes in parameters describing the structure and behavior, therefore, serve as powerful tools for distinguishing active reaction sites. This review explores structure–property correlations that have advanced understanding of behavior and reaction mechanisms in heterogeneous electrocatalysis. It covers correlations that have advanced understanding of the contributions of the local reaction environment to reactivity, of structure and bonding within solid-state materials, of geometric or mechanical strain in bonding environments, and of the impact of structural defects. Such correlations can assist researchers in developing next generation catalysts by establishing catalyst design principles and gaining control over reaction mechanisms.

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 score0.146
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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