Effects of high-temperature thermal reduction on thermal conductivity of reduced graphene oxide polymer composites

被引:14
作者
Lee, Yun Seon [1 ,2 ]
Kim, Nam Ryeol [1 ,3 ]
Park, Sang Ki [1 ]
Ko, Yong-il [1 ]
Shin, Yunjae [4 ]
Yang, Beomjoo [4 ]
Yang, Cheol-Min [1 ]
机构
[1] Korea Inst Sci & Technol KIST, Inst Adv Composite Mat, 92 Chudong Ro, Seoul 55324, Jeollabuk Do, South Korea
[2] Inha Univ, Dept Chem Engn, 100 Inha Ro, Incheon 22212, South Korea
[3] Chonnam Natl Univ, Grad Sch, Dept Polymer Engn, 77 Yongbong Ro, Gwangju 61186, South Korea
[4] Chungbuk Natl Univ, Sch Civil Engn, 1 Chungdae Ro, Cheongju 28644, South Korea
关键词
Reduced graphene oxide; Thermal reduction; Polymer composite; Thermal conductivity; GRAPHITE OXIDE; CARBON NANOTUBES; NANOPLATELETS; FILLER; RAMAN; MANAGEMENT;
D O I
10.1016/j.apsusc.2023.159140
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The graphitic crystalline structure of reduced graphene oxide (rGO) can be improved by high-temperature thermal reduction at various heat-treatment temperatures ranging from 1000 to 2500 C-degrees. The crystallinity significantly increased with increasing heat-treatment temperature. The electrical conductivities of the rGOs heat-treated at 2000 and 2500 C-degrees (h-rGO-2000 and h-rGO-2500, respectively) were similar to those of commercial graphite. The isotropic thermal conductivity of rGO/epoxy composite with 10 wt% h-rGO-2500 (2.56 W/ mK) was 11.6 times higher than that of pristine rGO (p-rGO; 0.22 W/mK) and significantly superior to those of epoxy composites with commercial graphite (0.82 W/mK) and mesophase pitch-based carbon fibers (MPCFs; 1.29 W/mK). Moreover, owing to the synergistic effect operating in the MPCF-h-rGO hybrid filler in epoxy composites, this combination of fillers increased the thermal conductivity to a greater extent than the MPCF-prGO hybrid filler. Optimum synergistic effects on the isotropic and in-plane thermal conductivities were achieved with an MPCF:h-rGO-2000 weight ratio of 49:1 (11.90 and 17.93 W/mK, 1.48 and 1.85 times higher than 8.02 and 9.69 W/mK for MPCF-p-rGO, respectively). Finally, a machine learning method that could predict and optimize the properties of rGOs based on their heat-treatment temperatures and material compositions was developed.
引用
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页数:13
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