Intrinsic kinetic model with variable light intensity of the emerging pollutants degradation in a photocatalytic differential reactor with immobilized TIO <sub>2</sub> : Experiments and CFD
Bibliographic record
Abstract
Abstract Heterogeneous photocatalysis is an alternative to mineralizing emerging pollutants. The present study focuses on the kinetic model of salicylic acid photocatalytic degradation in an aqueous solution as a function of pollutant concentration and light intensity. The intrinsic kinetic model parameters were determined using a differential photocatalytic reactor with immobilized TiO 2 based on experiments and numerical simulations. Five degradation experiments with different light intensities were performed for a turbulent flow rate of 4.7 L/min and salicylic acid concentration of 20 mg/L. Light intensities used for the kinetic experiments were obtained by computational fluid dynamics (CFD) simulation using the radiation transfer equation that was pre‐validated. The TiO 2 immobilized method presents low leaching of the catalyst and good degradation efficiency. The intrinsic kinetic model showed to be first‐order both for the pollutant concentration and for the light intensity and can be applied in simulation to optimize and design photocatalytic reactors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".