Polyamide Nanofiber Composites for Organic Pollutant Removal andChemical Warfare Protection
Bibliographic record
Abstract
Polymer nanofiber composites for the treatment of hazardous compounds are of considerable scientific and technological interest.In this study, polyamide nanofiber for organic pollutant removal and chemical warfare protection is discussed.The effect of position of functional materials in nanofiber matrix on the photocatalytic activity was studied by comparing the Ag-TiO2-decorated nylon nanofiber composite (AT-sur-NF) and Ag-TiO2-embedded nylon nanofiber composite (AT-in-NF) [1].We find that AT-sur-NF shows better photocatalytic activity compared to the photocatalytic activity of AT-in-NF.Based on these results, nylon and meta-aramid nanofibers decorated by various functional nanomaterials were fabricated.The electrospun meta-aramid nanofiber composites exhibit poor chemical stability because of the salt molecules remaining between meta-aramid chains [2].The chemical stability of meta-aramid nanofiber composites were improved by removing salt molecules during washing and additional thermal treatment.The polyamide nanofiber composites were stacked to enhance mechanical properties and resistivity to chemical warfare agents (CWAs).By controlling the stacking of polyamide nanofiber composites, thickness, weight density, and cool/warm feeling are optimized.In addition, the assemblies exhibit enough resistivity to CWAs while still maintain water vapor transmission to allow evaporation of sweat on the skin.Further study on the thermal properties and microstructure of nylon nanofibers reveals that the chains rigidity and thermal stability increase with decreasing diameter of nylon nanofibers.
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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".