ArticlePolymers2023
Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network.
Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Article
- AI-Driven Image Analysis for Nanofiber Characterization: From Diameter Measurement to Multiparameter Assessment.ACS omega · 2026Review
- Nanoarchitectonics of aptamer-based electrochemical sensor using electrospun carbon nanofibers and Au nanoparticles for cd (II) analysis.Scientific reports · 2026Article
- Article
- Article
- Cogni-e-SpinDB 1.0: Open Dataset of Electrospinning Parameter Configurations and Resultant Nanofiber Morphologies.Scientific data · 2026Article
- A predictive model for electrospun based Polyvinyl alcohol (PVA) nanofibers diameter using an artificial neural network.Scientific reports · 2025Article
- CNN-Optimized Electrospun TPE/PVDF Nanofiber Membranes for Enhanced Temperature and Pressure Sensing.Polymers · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Electrospinning technology enables the fabrication of electrospun nanofibers with exceptional properties, which are highly influenced by their diameter. This work focuses on the electrospinning of polyacrylonitrile (PAN) to obtain PAN nanofibers under different processing conditions. The morphology and size of the resulting PAN nanofibers were characterized using scanning electron microscopy (SEM), and the corresponding diameter data were measured using Nano Measure 1.2 software. The processing conditions and corresponding nanofiber diameter data were then inputted into an artificial neural network (ANN) to establish the relationship between the electrospinning process parameters (polymer concentration, applied voltage, collecting distance, and solution flow rate), and the diameter of PAN nanofibers. The results indicate that the polymer concentration has the greatest influence on the diameter of PAN nanofibers. The developed neural network prediction model provides guidance for the preparation of PAN nanofibers with specific dimensions.
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Registered trials
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