Physicochemical Study of Photocatalytic Activity of TiO<sub><b>2</b></sub>Supported Palygorskite Clay Mineral
Bibliographic record
Abstract
This study deals with the influence of physicochemical parameters, namely, the photocatalyst loading, dye concentration, and pH of polluted solutions, on the degradation efficiency of Orange G (OG) solutions containing TiO2nanoparticles supported on palygorskite clay mineral (TiO2-Pal). The TiO2photocatalyst attached to natural palygorskite fibers was elaborated by colloidal sol-gel route. It exhibits the anatase structure that is the most photoactive crystallographic form. The highest performances of supported photocatalyst on OG degradation were found using an optimum amount of TiO2-Pal around 0.8 g·L−1, which corresponds properly toca. 0.4 g·L−1of TiO2. This amount is interestingly lower than the 2.5 g·L−1generally reported when using pure unsupported TiO2powder. The photodegradation rate increases by decreasing OG initial concentration, and it was found significantly higher when the OG solution is either acidic ( pH<4 ) or basic ( pH≈11 ). For OG concentrations in the range 5×10-6– 5×10-4 M, the kinetic law of the OG degradation in presence of TiO2-Pal is similar to that reported for unsupported TiO2nanopowder. It follows a Langmuir-Hinshelwood model with a first-order reaction and an apparent rate constant of about 2.9×10-2 min−1.
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) | 0.000 | 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".