Investigation and predictive multi-modeling of PVA/PVP-blended nanofiber diameter in electrospinning

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Polyvinyl alcohol (PVA)/polyvinylpyrrolidone (PVP)–blended nanofibers have garnered increasing interest in biomedical applications in tissue engineering. PVA-based biomaterials still have limitations that can restrict their use or performance, among which blending with PVP has shown promising results. The ultrafine nanofibers were obtained from PVA solutions, and different rough surface and fractal surface nanofibers resulted from adjusting the PVP concentration. In this study, the ultrathin PVA/PVP-blended nanofiber membranes were produced using electrospinning. The experimental results show the effects of varying the PVA/PVP ratio and solution concentration on nanofiber quality and diameter. It indicates that increasing the PVA/PVP ratio not only decreases the nanofiber diameter but also complicates the control over nanofiber quality, leading to uneven distribution and problematic film formation at higher PVP levels. The data obtained from the proposed models demonstrate high significance, as indicated by high F values and low p values, underscoring the role of PVP in nanofiber fabrication. Additionally, an artificial neural network (ANN) was developed, exhibiting strong predictive capability during the testing phase prior to the final fabrication stages. This study provides the importance of carefully adjusting the PVA/PVP ratio to maintain nanofiber uniformity and optimizing solution concentration to control fiber diameter effectively.

Original languageEnglish
Article number103425
Pages (from-to)2445-2453
Number of pages9
JournalInternational Journal of Advanced Manufacturing Technology
Volume136
Issue number5
DOIs
Publication statusPublished - 2025 Jan

Keywords

  • Artificial neural network
  • Electrospinning
  • Nanofibers
  • Polymers
  • Predictive multi-modeling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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