Impact of Nickel Content on the Structure and Electrochemical CO<sub>2</sub> Reduction Performance of Nickel–Nitrogen–Carbon Catalysts Derived from Zeolitic Imidazolate Frameworks
Bibliographic record
Abstract
The electrochemical conversion of CO 2 affords a sustainable route to produce chemicals and fuels from renewable sources of electricity. Nickel–nitrogen–carbon (Ni–N–C) materials have shown promise in terms of activity and selectivity toward the electro-conversion of CO 2 into CO, a feedstock widely used in the chemical sector. Ni–N–C catalysts, postulated to comprise catalytically active atomically dispersed Ni–N x /C sites, are commonly prepared by pyrolyzing a mixture of transition metal-, nitrogen-, and carbon-containing precursors. Herein, we use a zeolitic imidazolate framework (ZIF-8)─a subclass of metal organic frameworks─as a platform for synthesizing Ni–N–C electrocatalysts. We systematically investigate the role of the Ni concentration impregnated into the ZIF-8 precursor structure during synthesis in the overall structure and performance of the resulting Ni–N–C catalysts for electrochemical CO 2 reduction. Our findings show that increased Ni contents in the catalyst precursor results in the formation of Ni-containing particles that increase the catalytic selectivity toward the competing hydrogen evolution reaction, whereas reduced Ni contents preferentially form atomically dispersed Ni–N x /C active sites dispersed in heterogeneous carbon structures consisting of carbon nanotubes and carbonaceous particles. As an optimized concentration of Ni in the precursor mixture, we demonstrate a CO 2 reduction selectivity toward CO of ca. 99% Faradaic efficiency at an applied potential of −0.68 V versus the reversible hydrogen electrode.
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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) | 0.001 | 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".