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Record W2996088288 · doi:10.1515/pac-2019-1006

In-situ X-ray techniques for non-noble electrocatalysts

2019· article· en· W2996088288 on OpenAlex
Sung‐Fu Hung

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePure and Applied Chemistry · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Toronto
FundersMinistry of Science and Technology, Taiwan
KeywordsElectrocatalystX-ray absorption spectroscopyChemistryX-ray photoelectron spectroscopyCatalysisOxygen evolutionNoble metalNanotechnologyChemical engineeringAbsorption spectroscopyMaterials sciencePhysical chemistryElectrochemistryElectrodePhysics

Abstract

fetched live from OpenAlex

Abstract Electrocatalysis offers an alternative solution for the energy crisis because it lowers the activation energy of reaction to produce economic fuels more accessible. Non-noble electrocatalysts have shown their capabilities to practical catalytic applications as compared to noble ones, whose scarcity and high price limit the development. However, the puzzling catalytic processes in non-noble electrocatalysts hinder their advancement. In-situ techniques allow us to unveil the mystery of electrocatalysis and boost the catalytic performances. Recently, various in-situ X-ray techniques have been rapidly developed, so that the whole picture of electrocatalysis becomes clear and explicit. In this review, the in-situ X-ray techniques exploring the structural evolution and chemical-state variation during electrocatalysis are summarized for mainly oxygen evolution reaction (OER), hydrogen evolution reaction (HER), oxygen reduction reaction (ORR), and carbon dioxide reduction reaction (CO2RR). These approaches include X-ray Absorption Spectroscopy (XAS), X-ray diffraction (XRD), and X-ray Photoelectron Spectroscopy (XPS). The information seized from these in-situ X-ray techniques can effectively decipher the electrocatalysis and thus provide promising strategies for advancing the electrocatalysts. It is expected that this review could be conducive to understanding these in-situ X-ray approaches and, accordingly, the catalytic mechanism to better the electrocatalysis.

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.

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 score0.200
Threshold uncertainty score0.817

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 head0.003
GPT teacher head0.199
Teacher spread0.196 · 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