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Improving the strength and toughness of macroscale double networks by exploiting Poisson’s ratio mismatch

We propose a new concept that utilizes the difference in Poisson's ratio between component materials as a strengthening mechanism that increases the effectiveness of the sacrificial bond toughening mechanism in macroscale double-network (Macro-DN) materials. These Macro-DN composites consist of...

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Autores principales: Okumura, Tsuyoshi, Takahashi, Riku, Hagita, Katsumi, King, Daniel R., Gong, Jian Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225664/
https://www.ncbi.nlm.nih.gov/pubmed/34168253
http://dx.doi.org/10.1038/s41598-021-92773-0
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author Okumura, Tsuyoshi
Takahashi, Riku
Hagita, Katsumi
King, Daniel R.
Gong, Jian Ping
author_facet Okumura, Tsuyoshi
Takahashi, Riku
Hagita, Katsumi
King, Daniel R.
Gong, Jian Ping
author_sort Okumura, Tsuyoshi
collection PubMed
description We propose a new concept that utilizes the difference in Poisson's ratio between component materials as a strengthening mechanism that increases the effectiveness of the sacrificial bond toughening mechanism in macroscale double-network (Macro-DN) materials. These Macro-DN composites consist of a macroscopic skeleton imbedded within a soft elastic matrix. We varied the Poisson's ratio of the reinforcing skeleton by introducing auxetic or honeycomb functional structures that results in Poisson’s ratio mismatch between the skeleton and matrix. During uniaxial tensile experiments, high strength and toughness were achieved due to two events: (1) multiple internal bond fractures of the skeleton (like sacrificial bonds in classic DN gels) and (2) significant, biaxial deformation of the matrix imposed by the functional skeleton. The Macro-DN composite with auxetic skeleton exhibits up to 4.2 times higher stiffness and 4.4 times higher yield force than the sum of the component materials. The significant improvement in mechanical performance is correlated to the large mismatch in Poisson's ratio between component materials, and the enhancement is especially noticeable in the low-stretch regime. The strengthening mechanism reported here based on Poisson's ratio mismatch can be widely used for soft materials regardless of chemical composition and will improve the mechanical properties of elastomer and hydrogel systems.
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spelling pubmed-82256642021-07-02 Improving the strength and toughness of macroscale double networks by exploiting Poisson’s ratio mismatch Okumura, Tsuyoshi Takahashi, Riku Hagita, Katsumi King, Daniel R. Gong, Jian Ping Sci Rep Article We propose a new concept that utilizes the difference in Poisson's ratio between component materials as a strengthening mechanism that increases the effectiveness of the sacrificial bond toughening mechanism in macroscale double-network (Macro-DN) materials. These Macro-DN composites consist of a macroscopic skeleton imbedded within a soft elastic matrix. We varied the Poisson's ratio of the reinforcing skeleton by introducing auxetic or honeycomb functional structures that results in Poisson’s ratio mismatch between the skeleton and matrix. During uniaxial tensile experiments, high strength and toughness were achieved due to two events: (1) multiple internal bond fractures of the skeleton (like sacrificial bonds in classic DN gels) and (2) significant, biaxial deformation of the matrix imposed by the functional skeleton. The Macro-DN composite with auxetic skeleton exhibits up to 4.2 times higher stiffness and 4.4 times higher yield force than the sum of the component materials. The significant improvement in mechanical performance is correlated to the large mismatch in Poisson's ratio between component materials, and the enhancement is especially noticeable in the low-stretch regime. The strengthening mechanism reported here based on Poisson's ratio mismatch can be widely used for soft materials regardless of chemical composition and will improve the mechanical properties of elastomer and hydrogel systems. Nature Publishing Group UK 2021-06-24 /pmc/articles/PMC8225664/ /pubmed/34168253 http://dx.doi.org/10.1038/s41598-021-92773-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Okumura, Tsuyoshi
Takahashi, Riku
Hagita, Katsumi
King, Daniel R.
Gong, Jian Ping
Improving the strength and toughness of macroscale double networks by exploiting Poisson’s ratio mismatch
title Improving the strength and toughness of macroscale double networks by exploiting Poisson’s ratio mismatch
title_full Improving the strength and toughness of macroscale double networks by exploiting Poisson’s ratio mismatch
title_fullStr Improving the strength and toughness of macroscale double networks by exploiting Poisson’s ratio mismatch
title_full_unstemmed Improving the strength and toughness of macroscale double networks by exploiting Poisson’s ratio mismatch
title_short Improving the strength and toughness of macroscale double networks by exploiting Poisson’s ratio mismatch
title_sort improving the strength and toughness of macroscale double networks by exploiting poisson’s ratio mismatch
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225664/
https://www.ncbi.nlm.nih.gov/pubmed/34168253
http://dx.doi.org/10.1038/s41598-021-92773-0
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