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Study on Near-Infrared Spectroscopy, Mechanics and Salt Water
Resistance of Epoxy Resin-Based Near-Infrared Absorbing Coatings |
ZHANG Wei-gang, PAN Lu-lu, LÜ Dan-dan |
College of Materials and Chemical Engineering, Chuzhou University, Chuzhou 239000, China
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Abstract A near-infrared low reflectivity coating with outstanding mechanical properties and salt water resistance was prepared using epoxy resin as a binder, Sm2O3 as a functional pigment, silane coupling agent and graphene as modifiers, respectively. The effects of Sm2O3 addition, silane coupling agent type, silane coupling agent addition and graphene addition on the coating properties were systematically studied. The results show that the increase of the additional amount of Sm2O3 can significantly reduce the reflectivity of the coating to 1.06 μm near-infrared light. When the additional amount of Sm2O3 is 50%, the reflectivity of the coating to 1.06 μm near-infrared light can be as low as 31.2%. At this time, the adhesion strength and impact strength of the coating can reach greades 1 and 50 kg·cm, respectively. The coating is modified with a silane coupling agent. The strong polar groups on the coupling agent can form covalent bonds with the resin matrix and the pigment in the coating, respectively, to play a bridging role, which can significantly improve the flexibility of the coating. Among which, KH560 has the best modification effect. When the addition amount of KH560 is 5%, the flexibility of the coating can be significantly improved from 9 mm before modification to 4 mm after modification. Graphene has a special coplanar structure and ultra-long conjugated structure characteristics so that its absorption of incident light can extend to the near-infrared region. In addition, the special lamellar structure of graphene makes it have high impact strength and flexibility. Adding graphene to the coating can significantly improve the mechanical properties of the coating. The study found that the addition of graphene can significantly reduce the reflectivity of the coating to 1.06 μm near-infrared light to further improve the mechanical properties of the coating. When the additional amount of graphene is 8%, the reflectivity of the coating to 1.06 μm near-infrared light can be as low as 12.6%, and the coating can have outstanding laser stealth performance at this time. At the same time, the adhesion strength, flexibility and impact strength of the coating can reach grades 1, 2 mm and 50 kg·cm, respectively, which can meet practical engineering application requirements. Under the synergistic interface optimization of epoxy resin, silane coupling agent and graphene, The microstructure, near-infrared low reflectivity properties and mechanical properties of the coating with the best formulation (50% of Sm2O3, 5% of KH560, and 8% of graphene) can remain stable after being corroded by salt water for 21 days. At this time, the reflectivity of the coating to 1.06 μm near-infrared light was 12.47%, and the adhesion strength, flexibility and impact strength of the coating can be maintained at grade 1, 2 mm and 45 kg·cm, respectively, indicating that the prepared coating has good saltwater resistance.
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Received: 2022-07-14
Accepted: 2022-10-21
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[1] XIA Yuan-jia, ZHAO Fang, LI Zhi-zun, et al(夏元佳, 赵 芳, 李志尊, 等). New Chemical Materials(化工新型材料), 2022, 50(5): 38.
[2] Li C, Hui T, Ping C. Optik, 2019, 183: 863.
[3] Golnabi H, Mahdieh M. Optics and Laser Technology, 2006, 38(2): 122.
[4] Zhang Y K, Qin Y S, Quan B, et al. Ceramics International, 2021, 47(22): 31180.
[5] Qin Y, Zhang M, Guan Y, et al. Ceramics International, 2019, 45(11): 14312.
[6] Wang Q C, Wang J C, Zhao D P, et al. Infrared Physics and Technology, 2016, 79: 144.
[7] JIN Guo-zhong, PEI He-zhong, ZHANG Guo-liang, et al(金国忠, 裴和中, 张国亮, 等). Materials Protection(材料保护), 2017, 50(3): 23.
[8] Zhang W G, Liu S, Li M R, et al. Infrared Physics & Technology, 2021, 112: 103603.
[9] ZHANG Wei-gang, WU Jia-jia(张伟钢, 吴佳佳). Surface Technology(表面技术), 2018, 47(1): 39.
[10] Yan X X, Xu G Y. Journal of Alloys and Compounds, 2010, 491: 649.
[11] Zhong M Z, Zhang Y, Li X Q, et al. Surface & Coating Technology, 2018, 347: 191.
[12] Yan X X, Xu G Y. Surface & Coatings Technology, 2010, 205: 2307.
[13] XU Chao-yang, YU Hong-wei, WANG Yuan-sheng, et al(徐朝阳, 余红伟, 王源升, 等). Polymer Materials Science and Engineering(高分子材料科学与工程), 2021, 37(10): 94.
[14] Zhang W Y, Lu C H, Ni Y R, et al. Materials Letters, 2012, 87: 13.
[15] YU Mao-lin, SUN Hao, XIE Ya, et al(余茂林, 孙 皓, 解 亚, 等). Surface Technology(表面技术), 2021, 50(11): 147.
[16] GE Cheng-yue, LUO Xiang-ping, WANG Jing, et al(戈成岳, 罗祥平, 王 静, 等). Journal of Chinese Society for Corrosion and Protection(中国腐蚀与防护学报), 2022, 42(4): 590.
[17] Yan X X, Xu G Y. Progress in Organic Coatings, 2012, 73: 232.
[18] TAN Yong-song, ZHU Jun-rong, CHEN Kun-lin, et al(谭永松, 朱俊荣, 陈坤林, 等). Fine Chemicals(精细化工), 2021, 38(1): 78.
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