Characterizing Catalyst Function and Transformations in the Plasma Reduction of CO 2 on Atomic Layer Deposition-Synthesized Catalysts
Bibliographic record
Abstract
The enhancement of CO2 reduction in atmospheric-pressure, non-thermal plasma has been shown using a variety of catalyst systems with ranging composition, particle sizes, and morphologies. Improvements in CO2 conversion can be attained by choice of catalyst material. However, inhomogeneity in the material distribution arising from the synthesis affects the catalytically active surface area and dielectric environment that modulates the plasma properties near the catalyst. Atomic layer deposition (ALD) can be used to control the composition of ultra- thin layers on support materials. We used ALD to synthesize metal oxide catalyst coatings on high surface area supports. We found that TiO 2 achieved significantly higher yields of CO2 conversion (to CO and O2 ) at low reactor power compared to ZnO or Al2O3 , materials commonly used a support for other catalysts. We also observed an unexpected increase in the catalytic activity on ZnO with increasing power. The results here suggest that ALD can unambiguously isolate the catalytic effects of materials in plasma reactors.
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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 | 0.001 |
| 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 | 0.001 | 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".