Laser‐Constructed RuO <sub>2</sub> /Fe <sub>2</sub> O <sub>3</sub> Composites for Efficient Photothermal Catalytic CO <sub>2</sub> Methanation Reaction
Bibliographic record
Abstract
Abstract Photothermal catalytic methanation of carbon dioxide (CO 2 ) is a promising and sustainable method for carbon resource utilization. The efficiency of this process hinges on enhancing the photothermal conversion capacity and precisely designing the catalytically active sites. Here, a laser synthesis strategy is proposed to construct the RuO 2 /Fe 2 O 3 heterostructure by laser etching to build a grooved Fe 2 O 3 substrate and depositing RuO 2 nanoparticles. RuO 2 /Fe 2 O 3 combines the wide‐spectrum absorption capacity of Fe 2 O 3 substrates (reaching 209.2 °C under light irradiation with a light intensity of 1.83 W cm −2 ) and the catalytic activity of RuO 2 , enabling CO 2 to have the ability of selective hydrogenation. Combined with the catalyst characterization and the catalytic experimental results, RuO 2 /Fe 2 O 3 is confirmed to be a photothermal catalyst with excellent catalytic performance and stability for CO 2 methanation (CH 4 selectivity and yield are 96% and 795 µmol cm −2 h −1 , respectively). This work provides a versatile platform for designing multi‐functional catalytic systems by laser‐assisted strategy, opening new avenues for solar‐driven CO 2 valorization.
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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".