Changing the Thermal Conductivity of the Percolation Network of Carbon Nanotubes Through Functionalization
Kapustin S. N.
1, Loginova A. S.
1, Tsykareva Yu. V.
11Lomonosov Northern (Arctic) Federal University, Arkhangelsk, Russia
Email: hare22@yandex.ru, yu.cykareva@narfu.ru
A study on the dependence of the thermal conductivity of percolation systems of carbon nanotubes (CNTs) on the type and degree of functionalization, number of defects has been carried out. The influence of the most commonly used -COOH, -OH and -CONH2 groups has been studied. A nonlinear dependence of conductivity on the number of functional groups has been detected. A small number of functional groups can improve conductivity, while a large number reduces it. We assume the existence of competing processes that increase thermal conductivity (changes in the geometry of CNTs, improved contact between them) and increase phonon scattering (the appearance of defects and scattering centers). The data can be used for manipulating the thermophysical properties of CNTs, as well as selecting the optimal degree of functionalization while developing composites and nanodevices. Keywords: carbon nanotubes, thermal conductivity, functionalization, percolation network.
- T.P. Dyachkova, A.G. Tkachev. Metody funktsionalizatsii i modifitsirobaniya uglerodnykh nanotrubok. Publishing house "Spectrum". M. (2013). 152 p. (in Russian)
- J. Jo, P. Saha, N. Kim, Ch. Ho, J. Kim. Mater. Des. 83, 777 (2015). https://doi.org/10.1016/j.matdes.2015.06.045
- M. Premalatha, A. Kingson Solomon Jeevaraj. J. Bionanosci. 12, 370 (2018). https://doi.org/10.1166/jbns.2018.1529
- Sh.-Y. Yang, Ch.-Ch. Ma, Ch.-Ch. Teng, Y.-W. Huang, Sh.-H. Liao, Y.-L. Huang, H.-W. Tien, T.-M. Lee, K.-Ch. Chiou. Carbon 48, 592 (2010). https://doi.org/10.1016/j.carbon.2009.08.047
- M. Premalatha, P. Vathi, S. Padmavathi, A. Kingson Solomon Jeevaraj. Sens. Lett. 18, 52 (2020). https://doi.org/10.1166/sl.2020.4188
- P. Ji, H. Sun, Y. Zhong, W. Feng. Chem. Eng. Sci. 81, 140 (2012). https://doi.org/10.1016/j.ces.2012.07.002
- S. Zhang, W. Chen, Y. Zhao, K. Yang, B. Du, L. Ding, W. Yang, S. Wu. Compos. B 223, 109106 (2021). https://doi.org/10.1016/j.compositesb.2021.109106
- H. Boroushak, Sh. Ajori, R. Ansari. Mol. Simul. 47, 1 (2021). https://doi.org/10.1080/08927022.2021.1873322
- R. Pan, Z. Xu, Z. Zhu, Z. Wang. Nanotechnology 18, 285704 (2007). DOI: 10.1088/0957-4484/18/28/285704
- R. Gulotty, M. Castellino, P. Jagdale, A. Tagliaferro, A.A. Balandin. ACS Nano 7, 5114 (2013). https://doi.org/10.1021/nn400726g
- X. Lan, C. Liu, T. Wang, J. Hou, J. Xu, R. Tan, G. Nie, F. Jiang. J. Electron. Mater. 48, 6978 (2019). https://doi.org/10.1007/s11664-019-07519-6
- J. Chen, Q. Chen, Q. Ma, Y. Li, Zh. Zhu. J. Mol. Catal. A 356, 114 (2012). DOI: 10.1016/j.molcata.2011.12.032
- E.J. Weydemeyer, A.J. Sawdon, Ch.-A. Peng. Chem. Commun. 51 (27), 5939 (2015). DOI: 10.1039/C5CC01115A
- N.N. Breslavskaya, P.N. Dyachkov. Zhurn. neorgan. khimii 45, 1830 (2000). (in Russian)
- A.V. Eletskiy. UFN 179, 225 (2009). https://doi.org/10.3367/UFNr.0179.200903a.0225
- A.G. Pronevsky, M.S. Ivanov. Vestn. BGU. Ser. Fizika. Matematika. Informatika 1, 48 (2015). (in Russian). https://elib.bsu.by/handle/123456789/134527
- S.N. Kapustin, S.I. Zabolotny, M.K. Eseev, Y.V. Tsykareva. Crystals 12, 10, 1501 (2022). https://doi.org/10.3390/cryst12101501
- L.B. Boinovich, A.M. Emelyanenko. Mendeleev Commun. 23, 1, 3 (2013). https://doi.org/10.1016/j.mencom.2013.01.002
- M. Eseev, A. Goshev, S. Kapustin, Y. Tsykareva. Nanomaterials 9, 1584 (2019). https://doi.org/10.3390/nano9111584
- S.N. Kapustin, M.K. Eseev, Y.V. Tsykareva, V.I. Voshchikov, D.S. Lugvishchuk. Glass Phys. Chem. 49, 526 (2023). https://doi.org/10.1134/S1087659623600527
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Дата начала обработки статистических данных - 27 января 2016 г.