Smart Reinforced Composites Using Carbon and Carbon-Based Nanomaterials II

A special issue of Fibers (ISSN 2079-6439).

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 11089

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Innovation in Research and Engineering Solutions (IRES), Rue Koningin Astridlaan 59B, 1780 Wemmel, Belgium
Interests: carbon; nanomaterials; fibres; nanomechanical properties of materials; metals; alloys; polymers; ceramics; functionally graded materials for brakes; thruster and valve applications; thin films; elastomers; packaging polymers; polymers and composites; environmental friendly processes
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Dear Colleagues,

Current technological demands are increasingly stretching the properties of advanced composite materials to expand their applications to more severe or extreme conditions, while simultaneously seeking cost-effective production processes and final products. The aim is to demonstrate the influence of different surface enhancing and modification techniques on carbon nanotube (CNT) composite-based materials and fillers for high value and high performance applications. These materials are a route to further exploiting advanced materials, using enabling technologies for additional functionalities without compromising structural integrity. Carbon fiber (CF)-based materials have particular advantages due to their mechanical and electrical properties. The current generation of carbon fibers have been extensively used in a multitude of applications, taking advantage of their valuable properties to provide solutions for complex problems of materials science and technology; however the limits of the capability of the current technology are now being reached. Although the global use of fiber-based composites has significantly grown in the past decade, there are still expectations that they be used as an alternative (also with proper CNT modification, both in matrix and filler material) to metals in high value, and heavy engineering applications to provide lightweight multifunctionality, high structural integrity and enhanced safety.

This Special Issue will cover a large scope of research in the area of carbon nanotube (CNT) composite-based materials and fillers, and solicits contributions on,but not limited to, the keywords of the Special Issue.

Dr. Elias P. Koumoulos
Guest Editor

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Keywords

  • Carbon nanotube-based structures
  • Carbon nanofiber-based structures
  • Graphene and graphene oxide
  • Textile and woven-structure composites
  • Nano-enabled prepregs and modified resins
  • High performance fiber-based structures with multi-functionalities (i.e., enhanced mechanical properties, electrical conductivity, thermal stability, flexibility)
  • Smart composites
  • Additive manufacturing
  • Manufacturing: upscale and regulation
  • Life cycle assessment

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Published Papers (2 papers)

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Research

15 pages, 8432 KiB  
Article
Electrochemical Deposition of SiO2-Coatings on a Carbon Fiber
by Sergei Galyshev and Evgeniya Postnova
Fibers 2021, 9(5), 33; https://doi.org/10.3390/fib9050033 - 7 May 2021
Cited by 7 | Viewed by 3582
Abstract
Research on carbon fiber oxide coatings is primarily focused on metal matrix composites. Such coatings act as a diffusion barrier between a matrix and a fiber and, in addition, they can be weak boundaries that significantly increase the mechanical properties of metal matrix [...] Read more.
Research on carbon fiber oxide coatings is primarily focused on metal matrix composites. Such coatings act as a diffusion barrier between a matrix and a fiber and, in addition, they can be weak boundaries that significantly increase the mechanical properties of metal matrix composites. A simple and economical method of coating deposition is the sol-gel method. However, it does not allow for control of the thickness of the carbon fiber coating. To eliminate this limitation, a combined method is used that includes sol-gel technology and electrochemical deposition. The paper presents the results of studies on the production of SiO2 coatings on carbon fibers by the above method. The effect of current density, deposition time, salt concentration, pH of the reaction medium, TEOS/H2O molar ratio, and alcohol concentration in the reaction medium on the structure and thickness of the coatings was studied. Full article
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13 pages, 3102 KiB  
Article
Applying Machine Learning to Nanoindentation Data of (Nano-) Enhanced Composites
by Elias Koumoulos, George Konstantopoulos and Costas Charitidis
Fibers 2020, 8(1), 3; https://doi.org/10.3390/fib8010003 - 21 Dec 2019
Cited by 28 | Viewed by 6761
Abstract
Carbon fiber reinforced polymers (CFRPs) are continuously gaining attention in aerospace and space applications, and especially their multi-scale reinforcement with nanoadditives. Carbon nanotubes (CNTs), graphene, carbon nanofibers (CNFs), and their functionalized forms are often incorporated into interactive systems to engage specific changes in [...] Read more.
Carbon fiber reinforced polymers (CFRPs) are continuously gaining attention in aerospace and space applications, and especially their multi-scale reinforcement with nanoadditives. Carbon nanotubes (CNTs), graphene, carbon nanofibers (CNFs), and their functionalized forms are often incorporated into interactive systems to engage specific changes in the environment of application to a smart response. Structural integrity of these nanoscale reinforced composites is assessed with advanced characterization techniques, with the most prominent being nanoindentation testing. Nanoindentation is a well-established technique, which enables quantitative mapping of nanomechanical properties with the μm surficial and nm indentation resolution scale and high precision characterization. This feature enables the characterization of the interface in a statistical and quantitative manner and the correlation of (nano-) reinforcement to interface properties of CFRPs. Identification of reinforcement is performed with k-Nearest Neighbors and Support Vector Machine classification algorithms. Expertise is necessary to describe the physical problem and create representative training/testing datasets. Development of open source Machine Learning algorithms can have an influential impact on uniformity of nanometry data creation and management. The statistical character of nanoindentation is a key factor to supply information on heterogeneity of multiscale reinforced composites. Both the identification of (nano-) reinforcement and quality assessment of composites are provided by involving artificial intelligence. Full article
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