ArticleScientific reports2024
Machine learning to predict morphology, topography and mechanical properties of sustainable gelatin-based electrospun scaffolds.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Article
- Article
- FibreCastML: an open web platform for predicting electrospun nanofibre diameter distributions for biomedical applications.Frontiers in bioengineering and biotechnology · 2026Article
- A Review of Chitosan-Based Electrospun Nanofibers for Food Packaging: From Fabrication to Function and Modeling Insights.Nanomaterials (Basel, Switzerland) · 2025Review
- Mechanical Properties of Biodegradable Fibers and Fibrous Mats: A Comprehensive Review.Molecules (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrospinning is an outstanding manufacturing technique for producing nano-micro-scaled fibrous scaffolds comparable to biological tissues. However, the solvents used are normally hazardous for the health and the environment, which compromises the sustainability of the process and the industrial scaling. This novel study compares different machine learning models to predict how green solvents affect the morphology, topography and mechanical properties of gelatin-based scaffolds. Gelatin-based scaffolds were produced with different concentrations of distillate water (dH
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.