Synergistic plasma and platinum catalysts interactions in CO2 reforming of propane at room temperature: The role of supports
Bibliographic record
Abstract
This study investigates the synergistic impact of plasma and catalyst in a hybrid non-thermal plasma system integrated with 1 wt% Pt catalysts supported on diverse catalyst supports (γ-Al 2 O 3 , SBA-15, HZSM-5, CeO 2 , TiO 2 , and multi-wall carbon nanotube (CNT)) at ambient conditions in dry reforming of propane (DRP). To optimize the reaction, the impact of in-situ plasma reduction time was investigated over the Pt/Al 2 O 3 catalyst at varying durations before the DRP reaction. The most favorable catalytic performance was observed at a reduction time of 60 min due to having the highest surface area (284 m 2 /g), which might result in smaller Pt particle size, and the effective reduction of oxidation states to Pt 0 . A comparison of reduction sources also revealed that the plasma-reduced catalyst outperformed the other, which was attributed to the agglomeration of Pt active sites during the thermal reduction process. The chemo-physical properties of different catalysts directly affected the plasma characteristics and catalytic performance. Pt/Al 2 O 3 exhibited the highest performance, attributed to having the smallest mean Pt particle size (0.94 nm) and highest discharge power (13.97 W), effective capacitance (1.41 nF), and charge transfer (0.34 μC) during DRP reaction, leading to a 13 % and 15 % increase in CO 2 and C 3 H 8 conversions, respectively, compared to plasma-only mode. The catalytic performance showed that the synergy of catalysts with higher basic sites and plasma characteristics, along with smaller Pt particle size, resulted in high reactant conversion and syngas selectivity. Binary HRTEM images also indicated the presence of more soft carbon (coke) in the pore channels of the spent Pt/HZSM-5 catalyst than in spent Pt/Al 2 O 3 .
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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".