Studying Reduction in Solid Oxide Fuel Cell Activity with Density Functional Theory− Effects of Hydrogen Sulfide Adsorption on Nickel Anode Surface
Bibliographic record
Abstract
To gain insight into the degree by which sulfur-based contaminants poison the solid oxide fuel cell (SOFC) anode, we examine adsorption and dissociation of consecutive molecules of hydrogen sulfide on a nickel (111) surface. Preferred adsorption sites, energies, transition states, and kinetic barriers are calculated for the resulting species, *SH x ( x = 0 − 2) and *H. Systematically larger amounts of adsorbed sulfur (0, 25, 50, 75, 100%) are calculated to determine the most energetically favorable sulfur surface coverage. The removal of existing sulfur surface atoms is studied to probe the irreversibility of the hydrogen sulfide adsorption reaction. The extent of molecular hydrogen adsorption at increasing surface sulfur coverages allows us to conclude that the presence of even 25% surface sulfur can reduce molecular hydrogen adsorption on the surface by half. Concurring with experimental data, our research demonstrates equilibrium coverage of 50% adsorbed sulfur on the surface. Due to the considerable exothermic nature of the hydrogen sulfide adsorption and dissociation reaction, partial irreversibility of the reaction is exhibited. This irreversibility proves challenging during attempts to remove surface sulfur and regain the original electrochemical activity.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".