Insights into a Nitrogen-Doped Cobalt Oxide Catalyst for Enhanced Hydrogenation of CO<sub>2</sub> to C<sub>2+</sub> Hydrocarbons
Bibliographic record
Abstract
Hydrogenation of CO 2 to C 2+ hydrocarbons over non-noble metal catalysts is essential from environmental and economic aspects. However, increasing the selectivity of C 2+ hydrocarbons is still challenging for Co-based catalysts, as their predominant selectivity is toward CH 4 (>99%). Herein, this work provides insights into the mechanism of CO 2 hydrogenation over a N-doped Co 3 O 4 (CoN x O y ) catalyst with a higher CO 2 conversion (25%) and C 2+ hydrocarbon selectivity (42 C-mol%) compared to the Co 3 O 4 parent material (9% and 3 C-mol%, respectively). An increased concentration of oxygen vacancies and a decreased surface basicity strength in CoN x O y correlated with its enhanced catalytic performance. In situ diffuse reflectance infrared Fourier transform spectroscopy and density functional theory calculations revealed the evolution of reaction intermediates and the N-doping benefits on the CoN x O y catalyst for high activity toward C 2+ hydrocarbons. The findings were consistent with a CO 2 hydrogenation catalytic cycle, where CO and C 2+ hydrocarbons are mainly produced through carbonate and formate reaction pathways, respectively. Overall, we found that a relatively simple nitridation procedure can enhance the catalytic activity and selectivity of cobalt oxide toward higher hydrocarbons. This methodology could be extended to improve other transition metal-based catalysts for CO 2 conversion.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".