Destruction of volatile organic compounds in air by a superimposed barrier discharge plasma reactor and activated carbon filter hybrid system
Bibliographic record
Abstract
The superimposed barrier discharge and activated carbon filter hybrid systems are used to remove toluene and trichloro-ethylene (TCE) from air streams. The superimposed barrier discharge consists of silent and surface discharges. Experiments are conducted for the gas flow rate from 1 to 10 L/min., applied power from 0 to 7 W and toluene and TCE initial concentration from 0 to 2,000 ppm for 60 Hz AC applied voltage conditions. Discharge byproducts are measured by FTIR, GC and TLV VOC detector. The results shows that: (1) toluene decomposition rate monotonically increases with increasing applied power; (2) approximately 90% of toluene is removed by plasma reactors alone and up to 98% is removed by hybrid systems; (3) TCE removal rate by hybrid system is 90% and up to 50% is removed by a discharge reactor alone; (4) the pressure drop of the reactor and carbon filter increase with increasing gas flow rate; (5) TCE decomposition to form CO/sub 2/, H/sub 2/O and Cl/sub 2/ and except CO/sub 2/ and H/sub 2/O these discharge byproducts are absorbed in activated carbon filters; (6) no COCl/sub 2/, HCl, CO, NO/sub x/ and O/sub 3/ are observed in a discharge byproducts for the present range of experiments; and (7) the energy yield for toluene decompositions is up to 30 g/kWh, and up to 15 g/kWh for TCE decompositions.
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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".