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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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