Biofiltration of hydrophobic VOCs pretreated with UV photolysis and photocatalysis
Bibliographic record
Abstract
The effects of pretreatments on the biofiltration of gas phase α-pinene, a poorly water soluble Volatile Organic Compound (VOC), was evaluated in a controlled and long-term experimental investigation. Ultraviolet (UV) photolysis and photocatalysis were used and compared as pretreatment techniques. A control experiment involving biofiltration alone allowed for the direct evaluation of the coupled UV-biofiltration. α-Pinene contaminated streams with flow rates of 5?6.5 l/min and concentrations of up to 130 ppmv were passed through the systems. UV pretreatment on average converted between 20 and 50% of α-pinene into water soluble intermediates. When comparing the effectiveness of each pretreatment process, UV photocatalysis provided greater α-pinene conversion, especially at low retention times and high contaminant loading. The untreated α-pinene along with the by-products of UV photooxidation was then removed effectively in the biofiltration stage. The UV-biofiltration process offered 50?80% more α-pinene removal compared to the control biofilter. Regardless of their effectiveness at removing the contaminant, photolysis and photocatalysis pretreatments had similar synergistic impact on the performance of the downstream biofilter.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".