A postgraduate experiment: a study of fabricating nanofibers by electrospinning
Bibliographic record
Abstract
Abstract The purpose of this work is to propose an experiment based on the fabrication of nanofibers by an electrospinning method that may become part of curricula at postgraduate level, for students of physics. This addition will not only increase the teaching capabilities of scientist and researchers at this level but also enhance the basic understanding of physics students of experimental setup and their approach to explore untouched areas in the field of nano-science in the future. This experiment gives a qualitative analysis of some physical parameters that affects the morphology and size of polyvinyl alcohol (PVA) nanofibers. The experiment has been performed on a trial basis, at the Department of Physics, University of Karachi. During the experiment we studied the effect of needle diameter and concentration of PVA on the size of nanofibers by SEM analysis. The statistical analysis of PVA nanofibers was performed by one-way analysis of variance by considering a P -value equal to 0.05. The analysis concluded that variation at the lower range of needle diameter has no significant effect on the size and morphology of nanofibers. Moreover, it is suggested, for future research, to consider the orientation of fiber alignment and control.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".