3D Conformal Modification of Electrospun Silk Nanofibers with Nanoscaled ZnO Deposition for Enhanced Photocatalytic Activity
Bibliographic record
Abstract
Nanostructured metal oxide materials have drawn great attention because of their enhanced semiconducting, electrical, optical, sensing, and chemical catalyzing properties. The application of metal oxides in biomedicine has recently emerged as a promising field, especially in the format of organic/metal oxide composites. However, the existing methods for fabricating organic/metal oxide materials revealed limitations on the precise control over the deposition of metal oxides and the maintenance of organic nanostructures. Here, we developed an approach for the fabrication of composite materials by depositing metal oxides on the nanostructured organic templates through atomic layer deposition (ALD). With this method, we fabricated a series of silk fibroin/ZnO composites at varied deposition temperatures. The results demonstrated that the ZnO layer had a 3D conformality and hexagonal wurtzite structure, and the deposition thickness was well controlled. The photocatalytic activity of silk/ZnO composites was confirmed by the photodegradation of Rh-B under UV exposure, and the efficiency was found to be temperature dependent. These results demonstrated the successful integration of organic materials with metal oxides through an easy and controllable approach for the development of multifunctional organic/metal oxide biomaterials.
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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".