Experimental methods in chemical engineering: Electrospinning
Bibliographic record
Abstract
Abstract Electrospinning produces micro‐ and nanofibres by forcing a polymeric solution through a fine needle in an electric field that produces a filament that accumulates onto a collector plate. The fibre morphology depends on solution properties, distance between needle and plate, feed rate, and electric field intensity. Membranes, films, capsules, and multilayer fibres are possible by introducing multiple needles, changing the plate configuration, moving the plate, or intermittent feeding. Its versatility, simplicity, and low‐cost has accelerated the adoption of this technology for adsorbents, catalysts, membranes, gas separation, electronic devices, electrodes in supercapacitors, drug delivery, and nanomedicine/tissue engineering. Since 1997, Web of Science Core Collection has indexed over 40,000 articles with electrospinning as a keyword in the ‘Topic’ search field. Multidisciplinary materials science, polymer science, nanoscience nanotechnology, and applied physics are the scientific disciplines that publish the most articles related to electrospinning, while chemical engineering is ranked 10th. The main clusters of research are: (1) membranes, fibres, polymer morphology, and medical type applications (scaffolds, drug delivery, antimicrobial); (2) nanoparticles/composites, photocatalysis, and graphene; (3) membranes, ultrafiltration, desalination, and carbon nanotubes; and, (4) waste water treatment, adsorption, and heavy metals. Here we highlight research conducted over the past decades and remaining challenges, including developing industrial scale‐up guidance, replacing/reducing hazardous and costly solvents, and devising theoretical models to determine optimal operating parameters.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".