Traditional theoretical and empirical calculation methods can guide the design of β-and metastable β-alloys for bio-titanium. However, it is still difficult to obtain novel near-β-Ti alloys with low modulus. This s...Traditional theoretical and empirical calculation methods can guide the design of β-and metastable β-alloys for bio-titanium. However, it is still difficult to obtain novel near-β-Ti alloys with low modulus. This study developed a method that combines machine learning with calculation of phase diagrams(CALPHAD) to facilitate the design of near-β-Ti alloys. An elastic modulus database of Ti–Nb–Zr–Mo–Ta–Sn system was constructed first, and then three features(the electron to atom ratio, mean absolute deviation of atom mass, and mean electronegativity) were selected as the key factors of modulus by performing a three-step feature selection. With these features, a highly accurate model was built for predicting the modulus of near-β-Ti alloys. To further ensure the accuracy of modulus prediction, machine learning with the elastic constants calculated was leveraged by CALPHAD database. The root mean square error of the well-trained model can be as low as 6.75 GPa. Guided by the prediction of machine learning and CALPHAD, three novel near-β-Ti alloys with elastic modulus below 50 GPa were successfully designed in this study. The best candidate alloy(Ti–26Nb–4Zr–4Sn–1Mo–Ta) exhibits an ultra-low modulus(36.6 GPa) after cold rolling with a thickness reduction of 20%. Our method can greatly save time and resources in the development of novel Ti alloys, and experimental verifications have demonstrated the reliability of this method.展开更多
Industrial application of superhydrophobic surfaces is often hindered by complicated process and sophisticated machines. A facile wet etching method （sandblast, HCl and sandblast/HCl） with vapor deposition of PFDS ...Industrial application of superhydrophobic surfaces is often hindered by complicated process and sophisticated machines. A facile wet etching method （sandblast, HCl and sandblast/HCl） with vapor deposition of PFDS （1H, 1H, 2H, 2H- perfluorodecyltriethoxysilane） was applied to fabricate superhydrophobic surface of heat-resistant steel used for vane. The coating component, surface morphology and surface roughness parameters of sample were observed by attenuated total reflectance Fourier transform infrared spectroscopy, scanning electron microscopy and atomic force microscopy. Static water contact angle （WCA） of samples with and without PFDS coating was measured by contact angle goniometer. The results showed that WCA values of polished, sandblast, HCl and sandblast/HCl-etched samples are 98°, 97°, 100° and 101°, respectively, and increase to 112°, 148°, 151 ° and 154v after vapor deposition of PFDS. The sandblast/HCl-etched sample with PFDS coating shows higher superhydrophobicity because of very large surface roughness and lotus protrusionlike structure. The superhydrophobicity of this fabricated surface has no obvious change after 38 cycles of the film adhesion test, indicating excellent durability.展开更多
基金financially supported by the National Natural Science Foundation of China (No.52071339)the Natural Science Foundation of Hunan Province,China (No.2020JJ4739)Guangxi Key Laboratory of Information Materials(Guilin University of Electronic Technology),China (No.201009-K)。
文摘Traditional theoretical and empirical calculation methods can guide the design of β-and metastable β-alloys for bio-titanium. However, it is still difficult to obtain novel near-β-Ti alloys with low modulus. This study developed a method that combines machine learning with calculation of phase diagrams(CALPHAD) to facilitate the design of near-β-Ti alloys. An elastic modulus database of Ti–Nb–Zr–Mo–Ta–Sn system was constructed first, and then three features(the electron to atom ratio, mean absolute deviation of atom mass, and mean electronegativity) were selected as the key factors of modulus by performing a three-step feature selection. With these features, a highly accurate model was built for predicting the modulus of near-β-Ti alloys. To further ensure the accuracy of modulus prediction, machine learning with the elastic constants calculated was leveraged by CALPHAD database. The root mean square error of the well-trained model can be as low as 6.75 GPa. Guided by the prediction of machine learning and CALPHAD, three novel near-β-Ti alloys with elastic modulus below 50 GPa were successfully designed in this study. The best candidate alloy(Ti–26Nb–4Zr–4Sn–1Mo–Ta) exhibits an ultra-low modulus(36.6 GPa) after cold rolling with a thickness reduction of 20%. Our method can greatly save time and resources in the development of novel Ti alloys, and experimental verifications have demonstrated the reliability of this method.
文摘Industrial application of superhydrophobic surfaces is often hindered by complicated process and sophisticated machines. A facile wet etching method （sandblast, HCl and sandblast/HCl） with vapor deposition of PFDS （1H, 1H, 2H, 2H- perfluorodecyltriethoxysilane） was applied to fabricate superhydrophobic surface of heat-resistant steel used for vane. The coating component, surface morphology and surface roughness parameters of sample were observed by attenuated total reflectance Fourier transform infrared spectroscopy, scanning electron microscopy and atomic force microscopy. Static water contact angle （WCA） of samples with and without PFDS coating was measured by contact angle goniometer. The results showed that WCA values of polished, sandblast, HCl and sandblast/HCl-etched samples are 98°, 97°, 100° and 101°, respectively, and increase to 112°, 148°, 151 ° and 154v after vapor deposition of PFDS. The sandblast/HCl-etched sample with PFDS coating shows higher superhydrophobicity because of very large surface roughness and lotus protrusionlike structure. The superhydrophobicity of this fabricated surface has no obvious change after 38 cycles of the film adhesion test, indicating excellent durability.
