Characterization of an LED Based Photoreactor to Degrade 4-Chlorophenol in an Aqueous Medium Using Coumarin (C-343) Sensitized TiO<sub>2</sub>
Bibliographic record
Abstract
A detailed performance evaluation of a simple high intensity LED based photoreactor exploiting a narrow wavelength range of the LED to match the spectrum of a dye in a photocatalysis system is reported. A dye sensitized (coumarin-343, lambda max = 446 nm) TiO 2 photocatalyst was used for the degradation of 4-chlorophenol (4-CP) in an aqueous medium using the 436 nm LED based photoreactor. The LED reactor performed competitively with a conventional multilamp reactor and sunlight in the degradation of 4-CP. Light intensities entering the reaction vessel were measured by conventional ferrioxalate actinometry. The results can be fitted by approximate first order kinetic behavior in this system. Hydroxyl radicals were detected by spin trapping EPR, and effects of OH radical quenchers on kinetics suggest that the reaction is initiated by these radicals or their equivalents. LEDs operating at competitive intensities offer a number of advantages to the photochemist or the environmental engineer via long life, efficient current to light conversion, narrow bandwidth, forward directed output, and direct current power for remote operation. Matching light source spectrum to chromophore is a key.
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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".