Identifying Activity and Selectivity Trends for the Electrosynthesis of Hydrogen Peroxide via Oxygen Reduction on Nickel–Nitrogen–Carbon Catalysts
Bibliographic record
Abstract
The electrocatalytic production of hydrogen peroxide (H 2 O 2 ) through the two-electron oxygen reduction reaction (ORR) requires cost-effective catalysts with high selectivity, activity, and stability. Herein we report the synthesis and electrocatalytic assessment of nickel–nitrogen–carbon (Ni–N–C) electrocatalysts to gain insight into ORR activity and selectivity toward the production of H 2 O 2 . The activity and selectivity of the catalysts depended on the amount of nickel added during synthesis as well as the pH of the electrolyte. The materials were found to be heterogeneous in nature, consisting of nitrogen-doped carbon structures containing Ni species, including Ni 3 S 2 and covered metallic Ni particles. The presence of Ni during synthesis was imperative for the ORR performance in acidic electrolytes but had minimal impact on the performance in alkaline electrolytes. By experimentally demonstrating that Ni 3 S 2, metallic Ni, and N-doped carbon species were not the source of activity, we postulate that atomically dispersed Ni–N x /C sites are responsible for the ORR performance in acidic electrolytes, with an activity of −0.3 mA cm –2 and a H 2 O 2 selectivity of 43% measured for the best Ni–N–C catalyst at 0.5 V vs RHE. This work highlights the potential and generates scientific insight into Ni–N–C catalysts to guide the design of improved performance metal–nitrogen–carbon catalysts based on inexpensive precursors and simplistic syntheses.
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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 | 0.000 |
| 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) | 0.001 | 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".