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Electrical characterization and sensing capabilities of self-assembly multi-scale multi-phase graphene-based composites.

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Title: Electrical characterization and sensing capabilities of self-assembly multi-scale multi-phase graphene-based composites.
Authors: Hostettler, Nathan1 (AUTHOR), Hubert, Pascal1 (AUTHOR) pascal.hubert@mcgill.ca
Source: Carbon. May2023, Vol. 208, p131-139. 9p.
Subjects: Unsaturated polyesters, Electric conductivity, Phase separation, Graphene, Low voltage systems
Abstract: The addition of graphene particles to a thermoset resin strongly enhances its electrical properties, opening the use of such system for damage and strain tracking. This increase is, however, proportional to the amount and purity of the graphene particles, usually at high costs. Using a high amount of filler leads to a decrease in processability as well as a possible drop in mechanical properties. In order to reduce the quantity needed for conduction with few-layer graphene (FLG), a self-assembly network of graphene particles in the polymeric structure was created using blends of unsaturated polyester and polycaprolactone, using FLG and fibreglass reinforcement. The DC and AC electrical conductivities were strongly enhanced by the coupled effect of phase separation and fibre preform, effectively reducing the percolation threshold. An impressive shift was measured from 8.2 wt% of graphene for the neat resin to 1.4 wt% of graphene for the optimized composition. These materials also showed strong strain sensing capabilities at low applied voltage. Therefore, these systems are reliable and cost effective candidates for self-sensing and damage tracking application. [Display omitted] [ABSTRACT FROM AUTHOR]
Copyright of Carbon is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 163470184
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Electrical characterization and sensing capabilities of self-assembly multi-scale multi-phase graphene-based composites.
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  Data: <searchLink fieldCode="AR" term="%22Hostettler%2C+Nathan%22">Hostettler, Nathan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hubert%2C+Pascal%22">Hubert, Pascal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pascal.hubert@mcgill.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22Carbon%22">Carbon</searchLink>. May2023, Vol. 208, p131-139. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Unsaturated+polyesters%22">Unsaturated polyesters</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+conductivity%22">Electric conductivity</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+separation%22">Phase separation</searchLink><br /><searchLink fieldCode="DE" term="%22Graphene%22">Graphene</searchLink><br /><searchLink fieldCode="DE" term="%22Low+voltage+systems%22">Low voltage systems</searchLink>
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  Data: The addition of graphene particles to a thermoset resin strongly enhances its electrical properties, opening the use of such system for damage and strain tracking. This increase is, however, proportional to the amount and purity of the graphene particles, usually at high costs. Using a high amount of filler leads to a decrease in processability as well as a possible drop in mechanical properties. In order to reduce the quantity needed for conduction with few-layer graphene (FLG), a self-assembly network of graphene particles in the polymeric structure was created using blends of unsaturated polyester and polycaprolactone, using FLG and fibreglass reinforcement. The DC and AC electrical conductivities were strongly enhanced by the coupled effect of phase separation and fibre preform, effectively reducing the percolation threshold. An impressive shift was measured from 8.2 wt% of graphene for the neat resin to 1.4 wt% of graphene for the optimized composition. These materials also showed strong strain sensing capabilities at low applied voltage. Therefore, these systems are reliable and cost effective candidates for self-sensing and damage tracking application. [Display omitted] [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Carbon is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.carbon.2023.03.005
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 131
    Subjects:
      – SubjectFull: Unsaturated polyesters
        Type: general
      – SubjectFull: Electric conductivity
        Type: general
      – SubjectFull: Phase separation
        Type: general
      – SubjectFull: Graphene
        Type: general
      – SubjectFull: Low voltage systems
        Type: general
    Titles:
      – TitleFull: Electrical characterization and sensing capabilities of self-assembly multi-scale multi-phase graphene-based composites.
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            NameFull: Hostettler, Nathan
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            NameFull: Hubert, Pascal
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            – D: 01
              M: 05
              Text: May2023
              Type: published
              Y: 2023
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              Value: 208
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            – TitleFull: Carbon
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