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
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Bibliographic record
Abstract
The electrochemical conversion of CO2 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 CO2 into CO, a feedstock widely used in the chemical sector. Ni–N–C catalysts, postulated to comprise catalytically active atomically dispersed Ni–Nx/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 CO2 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–Nx/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 CO2 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it