Noble-Metal-Free Ni-N-C Catalyst for Efficient Electrochemical Synthesis of Hydrogen Peroxide from Oxygen Reduction Reaction Under Both Acidic and Alkaline Conditions
Bibliographic record
Abstract
H2O2 is a valuable, environmentally friendly oxidizing agent with a wide range of applications from the provision of clean water to the synthesis of valuable chemicals. The on-site electrocatalytic production of H2O2 would bring the chemical to uses beyond its present reach. The successful commercialization of electrochemical synthesis of H2O2 through 2e- oxygen reduction reaction (ORR) and the efficiency of the process depends greatly on the availability of cost-effective catalysts with high selectivity, activity, and stability. Among the catalysts, heat-treated metal-nitrogen-carbon materials (M-N-C) are noble-metal-free alternatives to the expensive state-of-the art platinum-based catalysts and have attracted massive attention due to their low-cost, high abundance, and efficient catalytic performance towards ORR. Herein, we report the synthesis and electrocatalytic assessment of a M-N-C catalyst for the synthesis of H2O2 through ORR in both acidic and alkaline media. The catalyst was based on nickel, and prepared by pyrolyzing a mixture of nickel (II) chloride and nitrogen/carbon precursors. This talk will focus on the impact of nickel content on the performance, with catalyst characterization correlated with electrochemical results to gain insight into electrochemical activity and selectivity towards H2O2.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".