Enhancement of Thermal Stability, Flame Retardancy, and Antimicrobial Properties of Cotton Fabrics Functionalized by Inorganic Nanocomposites
Bibliographic record
Abstract
In this investigation, various metal oxide (ZrO 2, MgO, TiO 2 ) nanoparticles were prepared using the hot-air spray pyrolysis method. The prepared nanoparticles were characterized using the X-ray diffraction (XRD) technique, scanning electron microscopy (SEM), and transmission electron microscopy. The colloidal silica (SiO 2 ) sol was prepared using the sol–gel method, mixed with various metal oxides (ZrO 2, MgO, TiO 2 ). Uncoated cotton fabrics were separately impregnated with the prepared nano(composite) sols followed by the pad-dry-cure method. The structural analysis of the coated and uncoated fabrics was performed using XRD. The surface morphology of the coated and uncoated cotton fabrics was analyzed using SEM. The elemental analysis using energy-dispersive spectroscopy confirmed the presence of nanoparticles along with cellulose on the surface of the fabric. The thermal stability and flame retardancy properties and residue of the coated and uncoated fabrics were studied. The coated cotton fabrics showed better antibacterial activity against Staphylococcus aureus and Escherichia coli . The biocompatibility performance of the coated fabrics was in the order TiO 2 /SiO 2 > MgO/SiO 2 > SiO 2 > ZrO 2 /SiO 2 .
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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".