Fibrous TiO<sub>2</sub> Alternatives for Semiconductor-Based Catalysts for Photocatalytic Water Remediation Involving Organic Contaminants
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Water decontamination remains a challenge in several developed and developing countries. Affordable and efficient approaches are needed urgently. In this scenario, heterogeneous photocatalysts appear as one of the most promising alternatives. This justifies the extensive attention that semiconductors, such as TiO 2, have gained over the last decades. Several studies have evaluated their efficiency for environmental applications; however, most of these tests rely on the use of powder materials that have minimal to no applicability for large-scale applications. In this work, we investigated three fibrous TiO 2 photocatalysts, TiO 2 nanofibers (TNF), TiO 2 on glass wool (TGW), and TiO 2 in glass fiber filters (TGF). All materials have macroscopic structures that can be easily separated from solutions or that can work as fixed beds under flow conditions. We evaluated and compared their ability to bleach a surrogate dye molecule, crocin, under batch and flow conditions. Using black light (UVA/visible), our catalysts were able to bleach a minimum of 80% of the dye in batch experiments. Under continuous flow experiments, all catalysts could decrease dye absorption under shorter irradiation times: TGF, TNF, and TGW could, respectively, bleach 15, 18, and 43% of the dye with irradiation times as short as 35 s. Catalyst comparison was based on the selection of physical and chemical criteria relevant for application on water remediation. Their relative performance was ranked and applied in a radar plot. The features evaluated here had two distinct groups, chemical performance, which related to the dye degradation, and mechanical properties, which described their applicability in different systems. This comparative analysis gives insights into the selection of the right flow-compatible photocatalyst for water remediation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".