A Systematic Study of the Reactive In Situ Synthesis of Self-Assembled Silver Nanoparticles on Cotton Yarn
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) have attracted considerable interest for various applications, including antiviral and antimicrobial treatments, textile nanocomposites, heat transfer and strain sensing textiles, flexible electronics, and smart textiles. Their unique properties, determined by their size, shape, and morphology, render them suitable for a wide range of uses, such as antimicrobial treatments, anticancer therapy, drug delivery, personal protective equipment (PPE), and catalysis. In this investigation, we present an in situ reactive technique for the self-assembly of AgNPs directly onto cotton yarn. A systematic investigation was undertaken to establish the influence of several synthesis parameters on the average size of AgNPs. The variables under consideration included the ambient vacuum conditions, the concentration of both Ag precursor and reducing agent, the growth temperature, and the duration of thermal treatment. By precisely optimizing these parameters, we successfully regulated the AgNPs size range between 10 and 50 nm on the cotton yarn. The findings of this study elucidate the methodology of the controlled synthesis of AgNPs on cotton yarn for potential advancements in smart textile technologies.
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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) | 0.000 | 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 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".