Fabrication and characterization of electrospun mats of Nylon 6/Silica nanocomposite fibers
Bibliographic record
Abstract
In this article, the electrospinning process for Silica nanoparticles reinforced Nylon 6 nanofiber composite mats is investigated. More specifically, the effect of Nylon 6/Formic Acid concentration and silica weight fraction on the solution viscosity and the properties of the end product is studied. Rheological measurements were conducted to investigate the solutions’ viscosity, and scanning electron microscope was used to characterize the morphology and dimensions of the nanofibers. Energy dispersion X-ray was used to prove that silica nanoparticles are well distributed within the nanofibers. Finally, surface roughness and porosity of the mats were measured. It was found that when Nylon 6/Formic Acid concentration increased from 15 to 20 wt%, solution viscosity increased by 0.63 Pa·s, which leads to the increase in average fiber diameter from 103 to 160 nm. Also, when silica increased by 6%, highest viscosity increase was 0.1 Pa·s, while average fiber diameter increased for around 5 nm. In addition, protuberances or small silica beads are observed when silica weight fraction is increased above a critical value. The porosity remains unchanged while surface roughness increased by increasing silica weight fraction and decreasing Nylon 6 concentration. This study outlines the successful fabrication of bead-free Silica reinforced Nylon 6 nanofibers and their mats via electrospinning. Good control over processing parameters results in tailorability of size, morphology, and surface roughness of the end products.
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.
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".