Percolation in Carbon Nanotube-Reinforced Polymers for Strain-Sensing Applications: Computational Investigation on Carbon Nanotube Distribution, Curvature, and Aggregation
The present article investigates the possibility of simulating the electrical conductivity of carbon nanotube-reinforced polymer composites by numerical methods. Periodic representative volume elements are generated by randomly distributing perfectly conductive reinforcements in an insulating matrix...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/16/14/4959 |
| _version_ | 1797588514056962048 |
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| author | Alessandro Pontefisso Michele Zappalorto |
| author_facet | Alessandro Pontefisso Michele Zappalorto |
| author_sort | Alessandro Pontefisso |
| collection | DOAJ |
| description | The present article investigates the possibility of simulating the electrical conductivity of carbon nanotube-reinforced polymer composites by numerical methods. Periodic representative volume elements are generated by randomly distributing perfectly conductive reinforcements in an insulating matrix and are used to assemble an electrical network representative of the nanocomposite, where the nanotube–nanotube contacts are considered equivalent resistors modeled by means of Simmons’ equation. A comparison of the results with experimental data from the literature supports the conclusion that a random distribution of reinforcements is not suitable for simulating this class of materials since percolation thresholds and conductivity trends are different, with experimental percolation taking place before the expectations. Including nanotube curvature does not solve the issue, since it hinders percolation even further. In agreement with experimental observations, the investigation suggests that a suitable approach requires the inclusion of aggregation during the volume element generation to reduce the volume fraction required to reach percolation. Some solutions available in the literature to generate properly representative volume elements are thus listed. Concerning strain sensing, the results suggest that representative volume elements generated with random distributions overestimate the strain sensitivity of the actual composites. |
| first_indexed | 2024-03-11T00:53:08Z |
| format | Article |
| id | doaj.art-b99e16d16138428f999cf3b0fa7491cd |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-11T00:53:08Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-b99e16d16138428f999cf3b0fa7491cd2023-11-18T20:15:43ZengMDPI AGMaterials1996-19442023-07-011614495910.3390/ma16144959Percolation in Carbon Nanotube-Reinforced Polymers for Strain-Sensing Applications: Computational Investigation on Carbon Nanotube Distribution, Curvature, and AggregationAlessandro Pontefisso0Michele Zappalorto1Department of Management and Engineering, University of Padova, Stradella San Nicola 3, 36100 Vicenza, ItalyDepartment of Management and Engineering, University of Padova, Stradella San Nicola 3, 36100 Vicenza, ItalyThe present article investigates the possibility of simulating the electrical conductivity of carbon nanotube-reinforced polymer composites by numerical methods. Periodic representative volume elements are generated by randomly distributing perfectly conductive reinforcements in an insulating matrix and are used to assemble an electrical network representative of the nanocomposite, where the nanotube–nanotube contacts are considered equivalent resistors modeled by means of Simmons’ equation. A comparison of the results with experimental data from the literature supports the conclusion that a random distribution of reinforcements is not suitable for simulating this class of materials since percolation thresholds and conductivity trends are different, with experimental percolation taking place before the expectations. Including nanotube curvature does not solve the issue, since it hinders percolation even further. In agreement with experimental observations, the investigation suggests that a suitable approach requires the inclusion of aggregation during the volume element generation to reduce the volume fraction required to reach percolation. Some solutions available in the literature to generate properly representative volume elements are thus listed. Concerning strain sensing, the results suggest that representative volume elements generated with random distributions overestimate the strain sensitivity of the actual composites.https://www.mdpi.com/1996-1944/16/14/4959CNTcomputational analysispolymer compositesstructural health monitoring |
| spellingShingle | Alessandro Pontefisso Michele Zappalorto Percolation in Carbon Nanotube-Reinforced Polymers for Strain-Sensing Applications: Computational Investigation on Carbon Nanotube Distribution, Curvature, and Aggregation Materials CNT computational analysis polymer composites structural health monitoring |
| title | Percolation in Carbon Nanotube-Reinforced Polymers for Strain-Sensing Applications: Computational Investigation on Carbon Nanotube Distribution, Curvature, and Aggregation |
| title_full | Percolation in Carbon Nanotube-Reinforced Polymers for Strain-Sensing Applications: Computational Investigation on Carbon Nanotube Distribution, Curvature, and Aggregation |
| title_fullStr | Percolation in Carbon Nanotube-Reinforced Polymers for Strain-Sensing Applications: Computational Investigation on Carbon Nanotube Distribution, Curvature, and Aggregation |
| title_full_unstemmed | Percolation in Carbon Nanotube-Reinforced Polymers for Strain-Sensing Applications: Computational Investigation on Carbon Nanotube Distribution, Curvature, and Aggregation |
| title_short | Percolation in Carbon Nanotube-Reinforced Polymers for Strain-Sensing Applications: Computational Investigation on Carbon Nanotube Distribution, Curvature, and Aggregation |
| title_sort | percolation in carbon nanotube reinforced polymers for strain sensing applications computational investigation on carbon nanotube distribution curvature and aggregation |
| topic | CNT computational analysis polymer composites structural health monitoring |
| url | https://www.mdpi.com/1996-1944/16/14/4959 |
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