Intrinsic Kinetic Study for Photocatalytic Degradation of Diclofenac under UV and Visible Light
Bibliographic record
Abstract
This study employs a semibatch, swirl-flow, monolithic type photocatalytic reactor to determine intrinsic kinetic parameters for the photocatalytic degradation of a common anti-inflammatory drug, diclofenac (DCF), in an immobilized system under both UV and visible radiation. The goal of this work was (a) to find a better reactor configuration that provides improved residence time distribution of fluid, (b) to obtain a suitable support system (such as fiberglass sheet) for the immobilization of various photocatalysts (Degussa P25 TiO 2 and modified TiO 2 (doped and dye-sensitized), (c) to compare degradation rates with Degussa P25 when doped and dye-sensitized photocatalysts is used particularly under visible radiation, and finally (d) to determine true kinetic parameters after correcting for external mass transfer resistance that exists when catalysts is immobilized as a function of various operating parameters such as flow rate, catalyst loading, pH, light intensity, initial concentration, and photocatalyst type. The objective of this study lies in obtaining true kinetic data independent of reactor types and, therefore, can be used for process scale-up and high-rate water treatment. It was observed experimentally that a better degradation rate can be achieved with dye-sensitized catalysts activated under visible light.
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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) | 0.001 | 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 | 0.001 | 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".