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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".