Simultaneous reduction of Cr (VI) and degradation of azo dyes by F-Fe-codoped TiO<sub>2</sub>/SiO<sub>2</sub> photocatalysts under visible and solar irradiation
Bibliographic record
Abstract
A new TiO2/SiO2 photocatalyst codoped with fluorine and iron was synthesized using the sol–gel process and immobilized onto glass beads by coupling two methods of dip coating and heat attachment. The prepared samples were characterized by XRD, FTIR, DRS-UV–vis, FESEM, EDX, TEM, and N2 adsorption–desorption analysis. The effective parameters of pH, flow rate, and photoreactor vessels’ angle against solar irradiation on degradation performance were optimized. Subsequently, the photocatalytic activity of the prepared nanocomposites was investigated in an innovative fabricated photoreactor using a four-component pollutant mixture of Cr (VI), basic red 29, basic blue 41, and basic yellow 51 under visible and solar irradiation. Furthermore, the effectiveness of photoreactor performance was assessed under various outdoor climate conditions as natural irradiation sources. Consequently, UV–vis spectrophotometry results showed significant efficiency of photocatalytic removal of the pollutant mixture. TOC results of 74.39% and 78.04% for removal of organic content of the sample under visible and solar light, respectively, were also confirmed the great capability of the designed system for the simultaneous removal of some hazardous inorganic and organic contaminants under natural light sources.
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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) | 0.000 | 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".