Ni-Decorated N-Doped Biomass-Derived Porous Carbon toward Efficient Electroreduction of CO<sub>2</sub> to CO
Bibliographic record
Abstract
Electrochemical CO 2 reduction reaction (CO 2 RR) holds great prospects for transforming CO 2 into useful chemicals. However, there is still a long way to go to develop effective and affordable electrochemical CO 2 RR catalysts. In this work, a nickel-decorated nitrogen-doped biomass-derived porous carbon (Ni@N-BPC) catalyst was developed. The catalyst displayed a high specific surface area and superior CO 2 RR capability. The Faraday efficiency of CO exceeds ninety percent, ranging from a broad potential of 400 mV (−0.7 to −1.1 V vs RHE), reaching approximately 98.41% at −0.9 V vs RHE. The reaction pathway of CO 2 conversion to CO on Ni@N-BPC was analyzed by the in situ Fourier transform infrared spectroscopy method. Based on theoretical calculations, the rate-determining step shifts from the *COOH creation to the *CO desorption step by Ni anchoring. Meanwhile, N doping modulates the electronic structure and facilitates charge transport, lowering the overpotential of the rate-determining step, thus facilitating the formation of CO. This work offers fresh perspectives on the development and utilization of biomass-derived carbon materials as CO 2 RR electrocatalysts.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".