Modified Mn/ZSM-5 for Non-Thermal Plasma Mineralization of VOCs and DFT Simulation Using a Novel Y-Type ZSM-5 Model
Bibliographic record
Abstract
Using a catalyst to mineralize volatile organic compounds (VOCs) in a Non-thermal Plasma (NTP) reactor is an effective method. In many kinds of catalysts for VOCs degradation, oxygen defect is a crucial factor affecting the catalytic activity. Three different methods (steaming, doping, plasma) were used to introduce possible oxygen defects into the Mn/ZSM-5 to prepare modified catalysts, which were evaluated in VOCs degradation activity using a Double Dielectric Barrier Discharge (DDBD) plasma device. Additionally, a novel Y-type ZSM-5 model was employed in the DFT simulation. The new Y-type ZSM-5 model used in this paper is a more realistic aperiodic model. It showed that introducing possible oxygen defects can substantially enhance degradation efficiency. Taking the catalyst with oxygen defects introduced by plasma as an example, the conversion (CO2 selectivity) of the methanol, acetone, and toluene could reach 100% (100%), 97.7% (99.1%), 91.2% (93.9%), respectively, at an initial concentration of 2000 ppm and specific input energy of 9 kJ/L. The results demonstrated that modification could significantly enhance the activity of the catalyst in decomposing VOCs at room temperature using non-thermal plasma catalysis. Theoretical simulation of density functional theory (DFT) revealed that the adsorption of adsorbate on the catalyst becomes easier after possible oxygen defects are introduced.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".