The impact of transition metal catalysts on macroscopic dielectric barrier discharge (DBD) characteristics in an ammonia synthesis plasma catalysis reactor
Bibliographic record
Abstract
Abstract When non-equilibrium, low-temperature plasmas and catalysts interact, they can exhibit synergistic behavior that enhances the chemical activity above what is possible with either process alone. Unlike thermal catalysis, in plasma-assisted catalysis the non-equilibrium state of the plasma produces reactive intermediates, such as excited species, that may play an important role in the catalytic process. There are two primary plasma-surface mechanisms that could produce this synergy: the effect of the plasma on the catalyst (e.g. enhanced adsorption/reaction of plasma-activated species, change of surface structure/morphology, hot spots, etc) and the effect of the catalyst on the plasma state. This work focuses on the latter. We use a laboratory-scale, packed bed, dielectric barrier discharge (DBD) reactor to observe the influence of multiple alumina ( ) supported, transition metal ammonia (NH 3 ) synthesis catalysts on the plasma electrical and optical properties. We find that while the rates of ammonia synthesis over the materials considered, including , , and , are different, the macroscopic properties of the DBD are statistically indistinguishable. These results support the argument that the observed synergy in our catalysis experiments is not due to the catalyst modifying the characteristics of the plasma itself, but rather arises from differences in how the plasma environment and plasma-generated species modify chemistry at the catalyst surface, although the specific mechanism is still an outstanding question.
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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".