Synergetic Effect Between Plasma and UV for Toluene Conversion in Integrated Combined Plasma Photolysis Reactor with KrCl/KrBr/XeCl/Xe<sub>2</sub> Excilamp
Bibliographic record
Abstract
An integrated combined plasma photolysis (CPP) reactor, which simultaneously produced dielectric barrier discharge (DBD) plasma and excimer UV radiation, was employed to decompose toluene gas. The study of toluene degradation by the separate processes, realized in three reactors (DBD, UV, and CPP), showed that toluene conversion by CPP was higher than the sum of the conversions for DBD and UV alone, indicating a synergetic effect in CPP between plasma and UV occurred. The degradation performance of gaseous toluene in CPP using different excimer UV sources (XeCl*, KrCl*, KrBr*, Xe2*) was compared, and a regression analysis showed that CPP can enhance toluene conversion by about 50 % and energy yield by 90 %. Radiant spectra and efficiency of excimer UV sources were recorded in detail. Further, it was observed that carbon balance and CO2 selectivity were greatly increased in the CPP in comparison with DBD, accompanied by NO, CO, and O3 being effectively suppressed. Intermediate products in effluent gas using CPP treatment for toluene included formic acid, acetic acid, benzene, benzaldehyde, phenol, benzoic acid, etc. Based on by‐product analysis and excimer UV radiant efficiency, we concluded that toluene degradation is due to electron impact dissociation, excited species and free radical reaction from background gases dissociation, direct photolysis by excimer UV, and synergy between plasma and UV. Among these, electron impact, free radical reaction, and plasma‐UV synergy played critical roles on toluene conversion, while direct UV contribution seems to be minor.
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.
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".