Magnetic Hydrogel Defect Detection Based on Infrared Thermal Image of Alternating Magnetic Field

doi:10.3901/JME.2020.18.001

Abstract

Abstract: Magnetic hydrogel, a hydrogel with magnetic nanoparticles and unique magnetic response characteristics, has been widely used in fields of tissue engineering scaffolds, bioengineered cell separation, drug carriers, and environmental engineering. The distribution of magnetic nanoparticles in magnetic hydrogels plays a decisive role in their magnetic responses, which makes the analysis of the magnetic homogeneity of magnetic hydrogels a necessity. Yet there still lacks sufficient research or practice in non-destructive testing of magnetic hydrogels and has not been reported in the literature. Based on the backgrounds above, a novel non-destructive testing method for magnetic hydrogel defect detection has been developed, that is, the infrared thermal imaging method based on alternating magnetic field. The specific work is as follows. The magnetic hydrogel and copper powder silica gel plate have been detected, and furthermore, the feasibility of the principle of the magnetic hydrogel's getting heated has been verified under the alternating magnetic field. through the detection of magnetic hydrogels with different concentrations, the feasibility of using alternating magnetic fields infrared for The uniformity analysis of magnetic nanoparticles in magnetic hydrogels is verified. The uneven distribution of magnetic particles and bubble defect have also been detected and it has been found that the method works in the above detections. Based on theoretical analysis, it is deduced that there could be a linear relationship between the surface temperature rise rate of the magnetic hydrogel and the concentration of the magnetic nanoparticles under the alternating magnetic field. Through fitting the measured data of different concentration hydrogel, the above deduction is further proved.

Key words: magnetic hydrogels, magnetic nanoparticle, defect detection, alternating magnetic field, infrared thermal imaging

CLC Number:

TB34

QIAO Yancheng, XIE Shejuan, TONG Zongfei, TANG Jingda, XU Panpan, LI Ji, LI Lijuan, CHEN Zhenmao. Magnetic Hydrogel Defect Detection Based on Infrared Thermal Image of Alternating Magnetic Field[J]. Journal of Mechanical Engineering, 2020, 56(18): 1-6.

References

[1] KIM Y S,LIU M,ISHIDA Y,et al. Thermoresponsive actuation enabled by permittivity switching in an electrostatically anisotropic hydrogel[J]. Nature Mater-ials,2015,14(10):1002-1007.
[2] KIM J I,CHUN C J,KIM B,et al. Thermosensit-ive/magnetic poly(organophosphazene) hydrogel as a long-term magnetic resonance contrast platform[J]. Biomaterials,2012,33(1):218-224.
[3] SATARKAR N S,HILT J Z. Magnetic hydrogel nanocomposites for remote controlled pulsatile drug release[J]. Journal of Controlled Release,2008,130(3):246-251.
[4] 华蓉. 磁性水凝胶的制备及其对重金属的吸附研究[D].南京:南京大学,2014. HUA Rong. Synthesis and heavy metal ion adsorption studies of a magnetic hydrogel[D]. Nanjing:Nanjing University,2014.
[5] HU Ke,SUN Jianfei,GUO Zhaobin,et al. A novel magnetic hydrogel with aligned magnetic colloidal assemblies showing controllable enhancement of magne-tothermal effect in the presence of alternating magnetic field[J]. Advanced Materials,2015,27:2507-2514.
[6] CEZAR C,KENNEDY SM,MEHTA M,et al. Biphasic ferrogels for triggered drug and cell delivery[J]. Advanced Healthcare Materials,2014,3(11):1869-1876.
[7] HU Haibo,CHEN Qianwang,CHENG Kai,et al. Visually readable and highly stable self-display photonic humidity sensor[J]. Journal of Materials Chemistry,2012,22:1021-1027.
[8] GE Jianping,YIN Yadong. Responsive photonic cryst-als[J]. Angewandte Chemie International Edition,2011,50(7):1492-1522.
[9] 郭兴旺,李彬. 重型铝合金结构裂纹振动红外热像检测的建模和分析[J]. 机械工程学报,2014,50(24):31-37. GUO Xingwang,LI Bin. Modeling and analysis of vibrothermography of cracks in heavy aluminum alloy structures[J]. Journal of Mechanical Engineering,2014,50(24):31-37.
[10] 张超省,冯辅周,闵庆旭,等. 超声红外热像技术中预紧力对金属平板振动特性的影响[J]. 机械工程学报,2016,52(5):154-161. ZHANG Chaosheng,FENG Fuzhou,MIN Qingxu,et al. Effect of engagement force on vibration characteristics of metal plate in sonic infrared imaging[J]. Journal of Mechanical Engineering,2016,52(5):154-161.
[11] 陆向宁,何贞志,胡宁宁,等. 主动红外热成像焊球缺陷检测方法研究[J]. 机械工程学报,2016,52(10):17-24. LU Xiangning,HE Zhenzhi,HU Ningning,et al. Research on defects inspection of solder bumps using active infrared thermography technology[J]. Journal of Mechanical Engineering,2016,52(10):17-24.
[12] HERGT R,ANDRA W,D AMBLY C G,et al. Physical limits of hyperthermia using magnetite fine particles[J]. IEEE Transactions on Magnetics,1998,34(5):3745-3754.
[13] LI Yuhui,HUANG Guoyou,ZHANG Xiaohui,et al. Magnetic hydrogels and their potential biomedical applications[J]. Advanced Functional Materials,2013,23(6):660-672.
[14] TANG Jingda,TONG Zongfei,XIA Yukun,et al. Super tough magnetic hydrogels for remotely triggered shape morphing[J]. Journal of Materials Chemistry B,2018,6(18):2713-2722.

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[11]	HU Kun, ZHANG Shuyou, ZHAO Xinyue, AO Zhigang. Defect Detection Based on Contourlet-based Shape from Shading [J]. Journal of Mechanical Engineering, 2018, 54(6): 102-109.
[12]	HE Cunfu, DENG Peng, Lü Yan, JIAO Jingpin, WU Bin. A New Surface Wave EMAT with High SNR and the Application for Defect Detection in Thick-walled Pipes [J]. Journal of Mechanical Engineering, 2017, 53(4): 59-66.
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[15]	LIU Zenghua;YU Fengxiang;YU Hongtao;HE Cunfu;WU Bin. Ultrasonic Guided Wave Technology Based on Group Velocity Calibration and Its Application for Defect Detection in Composite Plates [J]. , 2012, 48(20): 8-15.