Advances in Electrocatalyst Design and Mechanism for Sulfide Oxidation Reaction in Hydrogen Sulfide Splitting
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".