Hydrogen Recovery from H<sub>2</sub>S Electrochemical Oxidation: A DFT Study
Bibliographic record
Abstract
Electrolysis of hydrogen sulfide (H 2 S) offers a green and zero-emission process for producing hydrogen and treating pervasive and harmful H 2 S from oil and gas refineries. However, the development of such a technology requires an efficient and stable catalyst. Herein, we investigate the mechanism of the electrochemical H 2 S oxidation reaction (H 2 SOR) over various metal oxides and metal sulfides using DFT calculations. We demonstrate why RuO 2 has been widely reported as an active H 2 SOR catalyst. We also demonstrate that metal oxides are affected by sulfur poisoning and that their activity toward H 2 SOR is enhanced following sulfur coverage. By including surface coverage analysis for S-intermediates, we identify TiO 2 as a promising and durable catalyst for H 2 SOR with a 0.49 V calculated overpotential. We also investigated the H 2 SOR activity of pristine and doped metal sulfides. We show that even though doping appears to reduce the overpotential needed to drive H 2 SOR, the S-intermediates block the active sites and decrease the reactivity. This research paves the way for the creation of more effective catalysts by providing a computational understanding of H 2 S electrolysis over various catalysts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".