Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 9, 2026

Procedure for Fabricating Biofunctional Nanofibers
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
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Development of Orally Disintegrating Tablets from Solid Dispersions Containing Tolvaptan/Cyclodextrin Complexes

Turkish journal of pharmaceutical sciences·2026
Same author

Development and characterization of ivermectin loaded microneedle formulations using 3D printed molds.

Journal of pharmaceutical sciences·2025
Same author

Herbal drug delivery for ocular treatments-an updated review.

Experimental eye research·2025
Same author

Development and optimization of hydrogel-forming microneedles fabricated with 3d-printed molds for enhanced dermal diclofenac sodium delivery: a comprehensive in vitro, ex vivo, and in vivo study.

Drug delivery and translational research·2024
Same author

Reproducible machine learning research in mental workload classification using EEG.

Frontiers in neuroergonomics·2024
Same author

Performance Analysis of Lambda Architecture-Based Big-Data Systems on Air/Ground Surveillance Application with ADS-B Data.

Sensors (Basel, Switzerland)·2023

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.

More Related Videos

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy
12:58

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Published on: September 12, 2019

Related Experiment Videos

Last Updated: Jun 9, 2026

Procedure for Fabricating Biofunctional Nanofibers
09:39

Procedure for Fabricating Biofunctional Nanofibers

Published on: September 10, 2012

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy
12:58

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Published on: September 12, 2019

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.