Performance Evaluation of Photocatalytic Reactors for Air Purification Using Computational Fluid Dynamics (CFD)
Bibliographic record
Abstract
The performance of two photocatalytic reactors for air decontamination designated Photo-CREC-air reactors is analyzed using computational fluid dynamics (CFD). Simulations of the original Photo-CREC-air revealed that the occurrence of a dead volume renders ∼68% of the available photocatalyst surface area inactive, resulting in poor air−photocatalyst contact. Moreover, the square cross section of the reactor geometry introduces regions of low ultraviolet (UV) irradiation. These issues are successfully addressed in a modified Photo-CREC-air design, which presents a uniform flow distribution over the photocatalyst surface and, therefore, good air−photocatalyst contact. In addition, the redesigned reactor geometry results in uniform UV irradiation over the photocatalyst. Simulations of reactor operation in continuous mode, with acetone as a model pollutant, revealed that negligible conversions are attained in the original Photo-CREC-air design, whereas conversions of 7.8% are predicted by simulations of the modified reactor. A simulation considering 10 modified Photo-CREC-air reactors in series showed that acetone conversions of 61% could be achieved in such a system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".