New aluminum mesh from recyclable material for immobilization of TiO<sub>2</sub> in heterogeneous photocatalysis
Bibliographic record
Abstract
Abstract This study presents a new aluminium mesh made out of soda can rings as a support for titanium dioxide (TiO2) in the degradation of the synthetic dyes Bordeaux Red (BR) and Tartrazine (TT). Three pre‐treatments including calcination and acidification steps were investigated in order to select the most efficient immobilization procedure for photocatalysis application. Raw and titania‐aluminum meshes were characterized by scanning electron microscopy, x‐ray diffraction, diffuse reflectance, and Fourier transform infrared spectroscopy. The material presented itself as a suitable alternative in the immobilization of titania for wastewater treatment. Preliminary tests selected H2O2/TiO2(suspension) oxidation systems under natural sunlight and germicidal lamps (UVC) exhibiting 97.2% and 99.5% of degradation in 180 minutes, respectively. Immobilized TiO2 systems reached high degradation rates (>99%) after 180 minutes in both UVC and solar light‐based processes. An experimental planning study was carried out for the processes in order to find the best operational conditions and pseudo‐first‐order model fit well the removal data (discolouration rates of in the order of 0.0274 and 0.0145 min−1 for UVC and solar light systems, respectively). Parameters such as TOC, COD, and turbidity, revealed a great improvement in the environmental quality of the water after the treatment and acute toxicity bioassays demonstrated a significant decrease in toxicity for both systems after the treatments. The TiO2‐meshes demonstrated high performance in the removal after five cycles of operation. Therefore, the new immobilization procedure demonstrated that the TiO2‐aluminum mesh is a feasible option for wastewater treatment and photocatalysis.
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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".