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Record W4296962659 · doi:10.1002/anie.202211949

What X‐Ray Absorption Spectroscopy Can Tell Us About the Active State of Earth‐Abundant Electrocatalysts for the Oxygen Evolution Reaction**

2022· review· en· W4296962659 on OpenAlexfundno aff
Marcel Risch, Dulce M. Morales, Javier Villalobos, Denis Antipin

Bibliographic record

VenueAngewandte Chemie International Edition · 2022
Typereview
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
FundersH2020 European Research CouncilCanadian Institutes of Health ResearchHelmholtz-Zentrum Berlin für Materialien und EnergieCanadian Light SourceUniversity of SaskatchewanDeutsche ForschungsgemeinschaftNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsX-ray absorption spectroscopyElectrocatalystOxygen evolutionElectrochemistryAbsorption spectroscopyElectrolyteSpectroscopyAbsorption (acoustics)ChemistryMaterials scienceNanotechnologyInorganic chemistryChemical engineeringElectrodePhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Implementation of chemical energy storage for a sustainable energy supply requires the rational improvement of electrocatalyst materials, for which their nature under reaction conditions needs to be revealed. For a better understanding of earth-abundant metal oxides as electrocatalysts for the oxygen evolution reaction (OER), the combination of electrochemical (EC) methods and X-ray absorption spectroscopy (XAS) is very insightful, yet still holds untapped potential. Herein, we concisely introduce EC and XAS, providing the necessary framework to discuss changes that electrocatalytic materials undergo during preparation and storage, during immersion in an electrolyte, as well as during application of potentials, showing Mn oxides as examples. We conclude with a summary of how EC and XAS are currently combined to elucidate active states, as well as an outlook on opportunities to understand the mechanisms of electrocatalysis using combined operando EC-XAS experiments.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations54
Published2022
Admission routes1
Has abstractyes

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