Visible light photocatalytic water remediation strategies using a novel black TiO2 based material optimized for in-flow applications
Bibliographic record
Abstract
Contaminated drinking water is a major health hazard in large urban areas as well as remote communities. Several pollutants detected in untreated wastewater are hormonal disruptors which are harmful to consumers as well as aquatic life. In this contribution, we present a novel material designed for visible light driven decontamination of water. This material is based on a glass fiber support loaded with black TiO2, a modified form of TiO2 with an expanded light absorption capacity without any toxic metal or non-metal dopants. The photocatalyst developed in our laboratories is ideal for flow as the active material remians fixed while there is continous passage of solution occuring under visible light irradiation. 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 is able to degrade these water contaminants with greater efficiency than conventional TiO2. Using this framework we envision that our findings can contribute to the production of inexpensive, large-scale solar or LED-based water decontamination systems which would be rapidly deployable 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 in-flow 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".