Unlocking Methane Generation via Photo‐Thermal‐Coupled CO <sub>2</sub> Hydrogenation by Integrating FeNiCrMnCo Multicomponent Alloy with GaN Nanowires
Bibliographic record
Abstract
Abstract The exploration of a noble‐metal‐free photo‐thermal‐coupled catalytic architecture plays a vital role in solar‐driven conversion of carbon dioxide (CO 2 ) into high‐value fuels and chemicals. In this study, FeNiCrMnCo multicomponent alloy (MCA) is integrated with GaN nanowires (NW's) for photo‐thermal‐coupled catalytic CO 2 methanation. The MCA/GaN NW's nanohybrid demonstrates a considerable methane production rate of 199 mmol∙g −1 ∙h −1 with an impressive selectivity of 93% under white light irradiation of 3 W∙cm −2 at 290 °C by external heating. The turnover number approaches 20,160 mole CH 4 per mole of MCA over a continuous operation period of 120 h, showcasing remarkable stability. Mechanistic investigations reveal that the unique MCA provides a flexible platform for tailoring the electronic and catalytic properties to optimize the adsorption and activation of CO₂ and H₂, thus leading to efficient and selective CO₂ methanation. This study presents an industry‐friendly architecture for photo‐thermal‐coupled CO 2 hydrogenation into high‐value fuels and chemicals by coupling a noble‐metal‐free multicomponent alloy with GaN NWs, paving the way for advancements in sustainable energy conversion through CO 2 utilization.
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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".