Development and Simulation of Sulfur‐doped Graphene Supported Platinum with Exemplary Stability and Activity Towards Oxygen Reduction
Bibliographic record
Abstract
Sulfur‐doped graphene (SG) is prepared by a thermal shock/quench anneal process and investigated as a unique Pt nanoparticle support (Pt/SG) for the oxygen reduction reaction (ORR). Particularly, SG is found to induce highly favorable catalyst‐support interactions, resulting in excellent half‐cell based ORR activity of 139 mA mgPt −1 at 0.9 V vs RHE, significant improvements over commercial Pt/C (121 mA mgPt −1) and Pt‐graphene (Pt/G, 101 mA mgPt −1). Pt/SG also demonstrates unprecedented stability, maintaining 87% of its electrochemically active surface area following accelerated degradation testing. Furthermore, a majority of ORR activity is maintained, providing 108 mA mgPt −1, a remarkable 171% improvement over Pt/C (39.8 mA mgPt −1) and an 89% improvement over Pt/G (57.0 mA mgPt −1). Computational simulations highlight that the interactions between Pt and graphene are enhanced significantly by sulfur doping, leading to a tethering effect that can explain the outstanding electrochemical stability. Furthermore, sulfur dopants result in a downshift of the platinum d‐band center, explaining the excellent ORR activity and rendering SG as a new and highly promising class of catalyst supports for electrochemical energy technologies such as fuel cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".