Related Experiment Video
Updated: Jun 9, 2026
09:39
Procedure for Fabricating Biofunctional Nanofibers
Published on: September 10, 2012
AI-Driven Image Analysis for Nanofiber Characterization: From Diameter Measurement to Multiparameter Assessment
Serdar Tort1, Haticenur Negiz1, Emre Tunçel2
1Department of Pharmaceutical Technology, Faculty of Pharmacy, Gazi University, Ankara 06330, Türkiye.
ACS Omega
|June 8, 2026
Summary
This review explores computational methods for characterizing nanofibers, focusing on accurate fiber diameter measurement. It covers traditional tools and advanced artificial intelligence techniques for quality control and process optimization.
Area of Science:
- Materials Science
- Nanotechnology
- Computational Science
Background:
- Nanofibers possess high surface area and porosity, enabling diverse applications in pharmaceuticals, energy, electronics, and environmental remediation.
- Nanofiber properties are critically dependent on production parameters, necessitating precise characterization for optimization and quality control.
- Accurate measurement of nanofiber diameter is crucial for assessing functional performance and ensuring consistent manufacturing.
Purpose of the Study:
- To systematically review computational methodologies for nanofiber characterization, with a specific emphasis on fiber diameter measurement.
- To compare traditional and artificial intelligence-based approaches for nanofiber analysis.
- To discuss industry applications and future trends in nanofiber characterization.
Main Methods:
- Review of manual measurement techniques and open-source software (DiameterJ, GIFT, SIMpoly).
- Exploration of artificial intelligence strategies, including machine learning, deep learning, generative frameworks, and transformer models.
- Analysis of comparative studies between computational and traditional characterization methods.
Main Results:
- Traditional tools offer foundational measurement capabilities but have limitations in speed and automation.
- Artificial intelligence models demonstrate significant potential for accurate and efficient nanofiber diameter determination.
- Emerging AI methodologies show promise for advanced analysis and automated quality control in nanofiber production.
Conclusions:
- Computational methods, particularly AI-driven approaches, are essential for precise nanofiber characterization and process optimization.
- Automated quality control using AI can enhance smart manufacturing of nanofibers.
- Further research into advanced AI techniques will drive innovation in nanofiber applications.
