Innovative Black TiO2 Photocatalyst for Effective Water Remediation Under Visible Light Illumination Using Flow Systems
Bibliographic record
Abstract
Contaminated drinking water is a major health hazard in large urban areas as well as remote communities. Several pollutants detected in rivers and lakes are hormone disruptors that are harmful to consumers as well as aquatic life. In this contribution, we present a new material, synthesized using novel green technologies, designed for solar- or LED-driven degradation of pollutants. This material is based on a glass fiber support, loaded with black TiO2, a modified form of TiO2 with strong visible light absorption and without any toxic metal or non-metal dopants. This photocatalyst is fully compatible with flow applications. The effectiveness of the catalyst is demonstrated with crocin and 17β-estradiol, the former being a natural carotenoid used as a screening tool and the latter being a common hormonal disruptor. Our work shows that under visible light illumination, our supported black TiO2 can degrade these water contaminants with greater efficiency than conventional TiO2. We envision that our findings can contribute to the production of inexpensive, large-scale solar or LED-based water decontamination systems that could be rapidly deployed to sites in need. Operation of such systems would require minimal training and could be monitored remotely. In addition to the catalyst’s non-toxicity and inflow compatibility, the material also has a long shelf life and is easy and inexpensive to produce, making it an attractive candidate for developing water treatment devices.
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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 | 0.001 | 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".