Silicene‐Based Quantum Dots Nanocomposite Coated Functional UV Protected Textiles With Antibacterial and Antioxidant Properties: A Versatile Solution for Healthcare and Everyday Protection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The predominant adverse health effects in care delivery result from hospital‐acquired (nosocomial) infections, which impose a substantial financial burden on global healthcare systems. Integrating contact‐killing antibacterial action, gas permeability, and antioxidant properties into textile coatings offers a transformative solution, significantly enhancing both medical and everyday protective applications. This study presents an innovative, pollution‐free physical compounding method for creating a fluorescent biopolymer composite embedded with silicene‐based heteroatom‐doped carbon quantum dots for the production of functional textiles. The resulting coated fabric shows superior ultraviolet (UV) protection behavior (UV A and UV B ), thermal stability, breathability, mechanical strength, and antioxidant capabilities as demonstrated by the 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) experiment (>78%) and 2,2'‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulphonic acid) ABTS assay (>90%). Rigorous testing against both gram positive and gram negative bacteria confirms that the coated fabric has excellent antibacterial activity. Results from time‐dependent antibacterial assays indicate that the nanocomposite can markedly inhibit bacterial proliferation within a few hours. Molecular dynamics modeling, in conjunction with experimental investigations, is employed to elucidate the intermolecular interactions influencing the components of the treated cotton fabrics. The ongoing research can result in the creation of cost‐effective smart textile substrates aimed at inhibiting microbial contamination in healthcare and medical applications, possibly rendering them commercially viable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it