Electrospun Polycaprolactone Nanofibers as a Reaction Membrane for Lateral Flow Assay
Bibliographic record
Abstract
Electrospun polycaprolactone (PCL) nanofibers have emerged as a promising material in diverse biomedical applications due to their various favorable features. However, their application in the field of biosensors such as point-of-care lateral flow assays (LFA) has not been investigated. The present study demonstrates the use of electrospun PCL nanofibers as a reaction membrane for LFA. Electrospun PCL nanofibers were treated with NaOH solution for different concentrations and durations to achieve a desirable flow rate and optimum detection sensitivity in nucleic acid-based LFA. It was observed that the concentration of NaOH does not affect the physical properties of nanofibers, including average fiber diameter, average pore size and porosity. However, interestingly, a significant reduction of the water contact angle was observed due to the generation of hydroxyl and carboxyl groups on the nanofibers, which increased their hydrophilicity. The optimally treated nanofibers were able to detect synthetic Zika viral DNA (as a model analyte) sensitively with a detection limit of 0.5 nM. Collectively, the benefits such as low-cost of fabrication, ease of modification, porous nanofibrous structures and tunability of flow rate make PCL nanofibers a versatile alternative to nitrocellulose membrane in LFA applications. This material offers tremendous potential for a broad range of point-of-care applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".