Flexible Semiconducting Nanofibers Functionalized with ZnO for Enhanced and Sustainable Water Decontamination
Bibliographic record
Abstract
We present here a photoactive system for water decontamination consisting of ZnO nanocrystals supported on a flexible mat of electrospun semiconducting nanofibers. The nanofibers have a core-sheath structure with a polyacrylonitrile (PAN) core and a sheath made of polypyrrole (PPY), a low band gap p-type semiconductor. Under UVA irradiation, the heterojunction formed between PPY and ZnO, an n-type semiconductor, promotes the separation of the charge carriers photogenerated at the interface. This decreases the charge recombination rate and increases the photocatalytic efficiency compared to a system where the same ZnO particles are supported on insulating bare PAN nanofibers. The photocatalytic tests and photoelectrochemical characterization show that the photoexcited electrons are preferentially collected on the PPY sheath and react with the dissolved oxygen , while the holes in excess on the ZnO surface degrade the persistent pollutants. The nanofiber production is scalable and sustainable. While previous works immobilized photoactive metal oxide nanoparticles on insulating mats to help their collection after use, this is the first report showing that a flexible semiconducting supporting material can play an active role in the photocatalytic process and significantly enhance its efficiency. This approach paves the way for the design of new supported hybrid materials for photocatalytic applications. Figure 1
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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".