Design and characterization of monolaurin loaded electrospun shellac nanofibers with antimicrobial activity
Bibliographic record
Abstract
The aim of this study was to elucidate the optimized fabrication factors influencing the formation and properties of shellac (SHL) nanofibers loaded with an antimicrobial monolaurin (ML). The main and interaction effects of formulation and process parameters including SHL content (35%–40% w/w), ML content (1%–3% w/w), applied voltage (9–27 kV) and flow rate (0.4–1.2 ml/h) on the characteristic of nanofibers were investigated through a total of 19 experiments based on a full factorial design with three replicated center points. As a result, the SHL content was the major parameter affecting fiber diameter. Another response result revealed that the SHL content would be also the most significant negative impact on amount of beads. An increase in the concentration of SHL leaded to a reduction in the amount of beads. From the results of characterization study, it was proved that ML might be entrapped between the chains of SHL during the electrospinning process exhibiting an excellent encapsulation. According to the response surface area, small (~488 nm) and beadless (~0.48) fibers were obtained with the SHL and ML contents of 37.5% and 1.1% w/w respectively, at the applied voltage of 18 kV and the flow rate of 0.8 ml/h. In addition, the results of the kill-kinetic studies showed that SHL nanofibers loaded with ML exhibited an excellent antibacterial activity against Staphylococcus aureus, while Escherichia coli was less affected due to the hydrophilic structure of the its outer membrane. ML also exerted an antifungal activity by reducing the number of Candida albicans colonies. Based on their structural and antimicrobial properties, SHL nanofibers containing ML could be potentially used as a medicated dressing for wound treatment.
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) | 0.000 | 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".