The Impact of Helium and Nitrogen Plasmas on Electrospun Gelatin Nanofiber Scaffolds for Skin Tissue Engineering Applications
Bibliographic record
Abstract
This study explores the fabrication of tannic acid-crosslinked gelatin nanofibers via electrospinning, followed by helium and nitrogen plasma treatment to enhance their biofunctionality, which was assessed using fibroblast cells. The nanofibers were characterized using scanning electron microscopy, atomic force microscopy, attenuated total reflection Fourier transform infrared spectroscopy, X-ray diffraction, and water contact angle measurements before and after treatment. Helium and nitrogen gas plasma were employed to modify the nanofiber surfaces. Results indicated that helium and nitrogen plasma treatment significantly increased the hydrophilicity and biofunctionality of the nanofibers by 5.1° ± 0.6 and 15.6° ± 2.2, respectively, making them more suitable for human skin fibroblast applications. To investigate the impact of plasma treatment on gelatin, we employed a computational model using density functional theory with the B3LYP/6-31+G(d) method. This model represented gelatin as an amino acid chain composed of glycine, hydroxyproline, and proline, interacting with plasma particles. Vibrational analysis of these systems was used to interpret the vibrational spectra of untreated and plasma-treated gelatin. To further correlate with experimental findings, molecular dynamics simulations were performed on a system of three interacting gelatin chains. These simulations explored changes in amino acid bonding. The computational results align with experimental observations. Comprehensive analyses confirmed that these treatments improved hydrophilicity and biofunctionality, supporting the use of plasma-treated gelatin nanofibers in skin tissue engineering applications. Gelatin's natural biopolymer properties and the versatility of plasma surface modification techniques underscore its potential in regenerating cartilage, skin, circulatory tissues, and hamstrings.
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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 | 0.000 | 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".