Topic Editors

Institute of Polymer Science and Technology (ICTP-CSIC), Juan de la Cierva 3, 28006 Madrid, Spain
Departamento de Polimeros, Facultad de Ciencias Químicas, Universidad de Concepción, Edmundo Larenas 129, Concepción, Chile
Institute of Polymer Science and Technology (ICTP), CSIC, C/Juan de la Cierva, 3, 28006 Madrid, Spain

Advances in Rubbers, Elastomers and Resins for Leading Edge Technologies

Abstract submission deadline
31 August 2025
Manuscript submission deadline
31 October 2025
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897

Topic Information

Dear Colleagues,

Today, polymers are essential for technological progress and are used in a wide range of applications such as electronics, 3D printing, robotics, etc. Therefore, understanding pre- polymers and polymers that can be crosslinked such as rubbers, elastomers, and resins and their impact on the technological fields of interest is a major challenge.

According to Scopus (as of September 19, 2024), nearly 449,000 scientific articles have been published in the last 10 years devoted to the study of "Rubber or Elastomer", "Thermoset and Resin", or "Photocurable and Resin". Growth prospects are promising due to (1) the advent of nanotechnology, (2) the development of 3D printing, and (3) new characterization techniques in advanced engineering fields. All of the above allow for conceptualization and demonstration through proof of concepts. Undoubtedly, the combination of these tools will lead to unprecedented advances. Therefore, it is essential to understand, from a fundamental science point of view, the phenomena that govern the properties necessary for their application in advanced technologies.

In this sense, our proposal is focused on providing a space to make visible and highlight research in the following topics: rubber and elastomer sciences; thermosetting or photocurable resins; current processing techniques, addressed to 3D printing; the influence of nanomaterials on polymer composite properties.

Dr. Marianella Hernández Santana
Dr. Héctor Aguilar Bolados
Prof. Dr. Miguel Ángel López Manchado
Topic Editors

Keywords

  • rubbers and elastomers
  • thermosetting resins
  • photocurable resins
  • nanomaterials
  • polymer-based composites
  • 3D printing

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Journal of Composites Science
jcs
3.0 5.0 2017 18.5 Days CHF 1800 Submit
Materials
materials
3.1 5.8 2008 15.5 Days CHF 2600 Submit
Polymers
polymers
4.7 8.0 2009 14.5 Days CHF 2700 Submit

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Published Papers (1 paper)

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20 pages, 6610 KiB  
Review
Smart Polymer Composites for Electrical Heating: A Review
by Alexandr V. Shchegolkov, Aleksei V. Shchegolkov, Vladimir V. Kaminskii and Maxim A. Chumak
J. Compos. Sci. 2024, 8(12), 522; https://doi.org/10.3390/jcs8120522 (registering DOI) - 12 Dec 2024
Viewed by 583
Abstract
The paper presents an overview of conductive polymer composites based on thermosetting materials, thermoplastics, and elastomers modified with carbon nanotubes (CNTs). To impart conductive properties to polymers, metal, carbon-dispersed materials, or their combinations are used. The inclusion of dispersed materials in polymers is [...] Read more.
The paper presents an overview of conductive polymer composites based on thermosetting materials, thermoplastics, and elastomers modified with carbon nanotubes (CNTs). To impart conductive properties to polymers, metal, carbon-dispersed materials, or their combinations are used. The inclusion of dispersed materials in polymers is associated with their microstructural features, as well as with polymerization methods. Such polymerization methods as melt mixing, solution technology, and introduction of fillers into the liquid phase of the composite with subsequent polymerization due to the use of a catalyst are known. Polymer composites that are capable of conducting electric current and changing their properties under the influence of an electric field, i.e., having one or more functional purposes, are called “smart” or intelligent. One such application is electric heating elements with the function of adaptive energy consumption or the effect of self-regulation of temperature depending on the surrounding conditions. A wide variety of polymers and dispersed materials with conductive properties determines a wide range of functional capabilities of the composite, including a positive temperature coefficient of resistance (PTCR) required to control temperature properties. The most effective filler in a polymer for obtaining a composite with desired properties is carbon nanomaterials, in particular, CNT. This is due to the fact that CNTs are a nanosized material with a high bulk density at a low weight, which allows for high electrical conductivity. Calculation of model parameters of polymer composites containing carbon nanostructures can be carried out using neural networks and machine learning, which give a fundamentally new result. The article contains sections with an assessment of various types of polymer matrices based on thermosets, thermoplastics, and elastomers. To impart electrically conductive properties, various options for fillers based on Ag, Au, Cu, Ni, Fe, and CNTs are considered. Methods for introducing dispersed fillers into polymer matrices are presented. Functional composites with a positive temperature coefficient and methods for their regulation are considered. The mechanisms of various electrophysical processes in conductive composites are considered, taking into account the resulting electrical conductivity based on the tunnel effect and hopping conductivity. An analysis of electric heaters based on various polymer matrices and dispersed fillers is carried out, taking into account their operating modes. Thus, the conducted review of modern scientific and practical research in the field of obtaining electrically conductive composites based on various types of polymer matrices with nanosized additives allows us to assess the prospects for the formation of functional composites for electrical heating, taking into account the mechanisms of electrical conductivity and new technologies based on machine learning and neural networks. Full article
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