Experimental Assessment of Photon Fluence Rate Distributions in a Medium-Pressure UV Photoreactor
Bibliographic record
Abstract
The performance of a medium-pressure (MP) mercury lamp photoreactor is strongly influenced by the spatial photon fluence rate (PFR) distributions which are wavelength-dependent. To address this issue, PFR distributions in an MP lamp photoreactor were measured using a 360-degree response microfluorescent silica detector (MFSD). To accurately express the optical behavior in an MP photoreactor, PFR, MFSD response PFR (PFR MFSD ), and effective germicidal PFR (PFR GER ) were defined and compared. The measured axial and radial PFR MFSD values agreed well with the corresponding results from a simulation model (UVCalc). The PFR and PFR GER were obtained from the measured PFR MFSD by using correction factors calculated by the UVCalc. Under identical UV transmittance (254 nm) conditions (75% and 85%), the weighted average PFR GER values were 13.3–18.7% lower than the corresponding PFR values, indicating that PFR GER, rather than PFR should be used in MP photoreactor design to meet disinfection standards. Based on measured lamp output, medium absorption spectrum, MFSD response, and microbial DNA response spectrum, the detailed relationships between the PFR, PFR MFSD, and PFR GER were elucidated. This work proposes a new method for the accurate description of wavelength-dependent PFR distributions in MP photoreactors, thus providing an important tool for the optimal design of these systems.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".