Synthesis and Characterization of Titanium Dioxide Hollow Nanofiber for Photocatalytic Degradation of Methylene Blue Dye
Bibliographic record
Abstract
Environmental crisis and water contamination have led to worldwide exploration for advanced technologies for wastewater treatment, and one of them is photocatalytic degradation. A one-dimensional hollow nanofiber with enhanced photocatalytic properties is considered a promising material to be applied in the field. Therefore, we synthesized titanium dioxide hollow nanofibers (THNF) with extended surface area, light-harvesting properties and an anatase–rutile heterojunction via a template synthesis method and followed by a calcination process. The effect of calcination temperature on the formation and properties of THNF were determined and the possible mechanism of THNF formation was proposed. THNF nanofibers produced at 600 °C consisted of a mixture of 24.2% anatase and 75.8% rutile, with a specific surface area of 81.2776 m2/g. The hollow nanofibers also outperformed the other catalysts in terms of photocatalytic degradation of MB dye, at 85.5%. The optimum catalyst loading, dye concentration, pH, and H2O2 concentration were determined at 0.75 g/L, 10 ppm, pH 11, and 10 mM, respectively. The highest degradation of methylene blue dye achieved was 95.2% after 4 h of UV irradiation.
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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 | 0.001 | 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".