ArticleGels (Basel, Switzerland)2023
Exploring Gel-Point Identification in Epoxy Resin Using Rheology and Unsupervised Learning.
Article in Gels (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Article
- Rheological Evolution and Viscoelastic Transition of Ambient-Curing Epoxy-Urethane Reactive Polymer Composites.Polymers · 2026Article
- Reconfigurable Nanogap SERS for Multiscale Molecular Sensing on Curved Surfaces.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- A Theory-Guided Machine Learning and Molecular Dynamics Approach for Characterizing Fast-Curing Polyurethane Systems.Polymers · 2026Article
- Influence of Epichlorohydrin Concentration on the Physicochemical and Rheological Performance of Lignin/PVA Hydrogels.Polymers · 2025Article
- Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization.Gels (Basel, Switzerland) · 2025Review
- Physicochemical properties of surimi made from edible insects using washing and pH shift methods.Current research in food science · 2025Article
- Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels.Gels (Basel, Switzerland) · 2024Article
- Engineering innovations in medicine and biology: Revolutionizing patient care through mechanical solutions.Heliyon · 2024Review
- Uncovering Key Factors in Graphene Aerogel-Based Electrocatalysts for Sustainable Hydrogen Production: An Unsupervised Machine Learning Approach.Gels (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Any thermoset resin's processing properties and end-use performance are heavily influenced by the gel time. The complicated viscosity of resin as a function of temperature is investigated in this work, with a particular emphasis on identifying the gel point and comprehending polymerization. Rheology studies carried out using a plate-plate controlled stress rheometer under isothermal conditions were used to compare three experimental techniques for figuring out an epoxy resin's gel point. We also look at the basic modifications that take place during polymerization. We verify the reliability of the three strategies by including Principal Component Analysis (PCA), an unsupervised machine learning methodology. PCA assists in uncovering hidden connections between these methods and various affecting factors. PCA serves a dual role in our study, confirming method validity and identifying patterns. It sheds light on the intricate relationships between experimental techniques and material properties. This concise study expands our understanding of resin behavior and provides insights that are essential for optimizing resin-based processes in a variety of industrial applications.
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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.