Varying TiO<sub>2</sub> Macroscopic Fiber Morphologies toward Tuning Their Photocatalytic Properties
Bibliographic record
Abstract
In a context of volatile organic compound photodecomposition, we have addressed TiO2-based macroscoscpic fiber generation. We have extruded hybrid sols of amorphous titania nanoparticles, latex nanoparticles, and nonionic surfactant (Tergitol) as structure-directing agents into a poly(vinyl alcohol) (PVA) solution bearing salts acting as a flocculating medium. The resulting nanocomposite TiO2/latex/PVA macroscopic fibers were thermally treated in air to open porosity by organic removal while generating the photocatalytically active anatase phase of TiO2 along with residual brookite. Considering the synthetic paths, we have varied both the diameter of the latex particles as well as their concentration within the starting sol. These parameters allow tuning both the voids created through the applied thermal treatment and the fiber final diameters. For gas-phase photocatalysis, we have shown that the fiber diameters, mesoscopic roughness, and macroscopic topological defects represent indeed important morphological parameters acting cooperatively toward both acetone degradation and its mineralization processes. Particularly, triggering the fiber morphological characteristics, we have increased their efficiency toward acetone degradation of around 550% when compared with previous work.
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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".