Bridging Thermal Catalysis and Electrocatalysis: Catalyzing CO<sub>2</sub> Conversion with Carbon‐Based Materials
Bibliographic record
Abstract
Abstract Understanding the differences between reactions driven by elevated temperature or electric potential remains challenging, largely due to materials incompatibilities between thermal catalytic and electrocatalytic environments. We show that Ni, N‐doped carbon (NiPACN), an electrocatalyst for the reduction of CO2 to CO (CO2R), can also selectively catalyze thermal CO2 to CO via the reverse water gas shift (RWGS) representing a direct analogy between catalytic phenomena across the two reaction environments. Advanced characterization techniques reveal that NiPACN likely facilitates RWGS on dispersed Ni sites in agreement with CO2R active site studies. Finally, we construct a generalized reaction driving‐force that includes temperature and potential and suggest that NiPACN could facilitate faster kinetics in CO2R relative to RWGS due to lower intrinsic barriers. This report motivates further studies that quantitatively link catalytic phenomena across disparate reaction environments.
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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 | 0.001 |
| 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".