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Record W4399444807 · doi:10.1002/adfm.202403435

Advances in Electrocatalyst Design and Mechanism for Sulfide Oxidation Reaction in Hydrogen Sulfide Splitting

2024· article· en· W4399444807 on OpenAlexafffund
Yu Zhao, Zhiping Deng, Yao Li, Xiaolei Wang

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence FundUniversity of AlbertaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Canada
KeywordsElectrocatalystHydrogen sulfideMaterials scienceSulfideMechanism (biology)Water splittingReaction mechanismInorganic chemistryCatalysisPhysical chemistryMetallurgyChemistryElectrochemistrySulfurOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Sulfide oxidation reaction (SOR) is a semi‐reaction for the electrochemical decomposition of hydrogen sulfide. Combining SOR with the hydrogen evolution reaction (HER) allows for the simultaneous splitting of hydrogen sulfide (H 2 S) to produce green hydrogen, reducing energy input and environmental pollution. However, the phenomenon of sulfur passivation on the anode leads to catalyst deactivation, posing a bottleneck in developing efficient SOR. Electrocatalysts with decent performance and sulfur‐tolerance play a central role in the application of SOR. Here, recent progress on electrocatalysis mechanisms and catalyst design strategies of SOR are summarized. A summary of recent progress is made in materials such as alloys, metal oxides, metal sulfides, metal selenides, and heterostructures as SOR catalysts. Additionally, some valuable design and modulation strategies are highlighted and included. Finally, this review offers an outlook on the future directions in this emerging field.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.238
Teacher spread0.226 · 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 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

Citations51
Published2024
Admission routes2
Has abstractyes

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