Ice formation and accretion on windows of buildings and windshields of automobile lead to various inconveniences and operational difficulties in cold regions. To prevent ice accretion and fasten ice removal on glass, a new generation of transparent deicing materials with high efficiency and energy saving is highly expected through low-cost approaches. However, conventional anti-icing/deicing coatings are opaque materials that are difficult to implement on automotive glass and building windows. As a typical photothermal semiconductor material, Cu2-xS has high near infrared (NIR) light absorbance and excellent photothermal conversion realized by excitation and relaxation of electron-hole pairs, which differs from noble metal nanoparticles. The unique advantage makes Cu2-xS is used widely in photothermal tumor and cancer therapy, while the application in anti/deicing area is limited. Here, we develop a low-cost transparent photothermal nanocomposite coating based on solution-processed Cu2-xS for active photothermal deicing. The photothermal nanocomposite coating was first prepared by the integration of Cu2-xS nanoparticles and commercially available acrylic paints, and then brushed onto glass surfaces of automobile and buildings. The deicing results show that when exposed to the near-infrared laser illumination at the wavelength of 808 nm, the surface coating temperature of glass covered with 3mm ice layer rapidly increases over 30℃ and the ice layer melts in 300 seconds at different ambient temperatures of -16 ℃, -20℃ and -24℃, demonstrating the high light-to-heat conversion efficiency and remarkable deicing property of transparent photothermal coating under extreme cold conditions. This study of transparent photothermal nanocomposite coating fabricated by simple brushing method provides enormous potential for ice removal applications on glass in building structures and automobile without highly affecting the visible transmittance, which is expected for further development in various shaped components.
Acoustic-laser technique has been developed as a promising method to detect defects in structures by vibrating the target object with an acoustic excitation, especially to identify near-surface defects in fiber-reinforced polymer (FRP)-bonded systems. The vibration characteristics are measured by laser beam to determine the integrity of interfacial bonding in structural systems. The sensitivity of acoustic-laser technique can be affected by several operational parameters. The limitation of data acquisition system and the missing data during measurement can influence the accuracy of defect detection. The defect size can also affect the effectiveness of acoustic-laser technique as the acoustic wave is unable to excite the defect region if the defect size is too small. To efficiently reconstruct acoustic-laser measurement for continuous or random missing data situations, a machine learning approach is proposed considering the effect of defect size. This method is based on K-singular value decomposition (K-SVD) with the orthogonal matching pursuit (OMP) algorithm. In this study, FRPbonded systems with two different sizes of interfacial defect are adopted in the experimental measurement using acoustic laser technique for defect detection. The results demonstrate the effectiveness of machine learning method in the reconstruction of the missing information for electrical signals. The reconstructed data is more reliable for the cases with smaller defect sizes and random missing data. For further application in a broader range, more measured results of defect size should be considered in the dataset of the proposed method.
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