Zn-Fe layered double hydroxide with chloride intercalation (ZFCL) was synthesized by a coprecipitation method at room temperature. ZFCL was characterized by N 2 adsorptiondesorption isotherms, X-ray diffraction, scanning electron microscope, Zeta-sizer analyzer, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy. The results showed that ZFCL had large surface area and layered structure. The maximum adsorption capacity of ZFCL was 150.6 mg/g at 25°C. That was higher than most other adsorbent which were reported. The kinetic data were described better by the pseudo-second-order adsorption kinetic rate model. The adsorption isotherm on the adsorbent was described by Langmuir, Freundlich, and Sips models at pH 6 and followed the fitting order: Sips >Freundlich>Langmuir. Thermodynamic analyses indicated that the phosphate adsorption on ZFCL was endothermic and spontaneous in nature. The sequence of coexisting cations and anions competing with phosphate was Ca 2+ > Mg 2+ > Na + and SO 4 2− > NO 3 − > Cl − . ZFCL can be regenerated by the sequential use of NaOH and ZnCl 2 . The adsorption capacity remained high as 108.6 mg/g after regeneration of 3 times. The results of zeta potential, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy analyses indicated that the phosphate adsorption mechanisms involved ion exchange, Zn 3 (PO 4 ) 2 precipitation, and the formation of inner-sphere complex via replacement of surface hydroxyl groups by phosphate.
Machine vision technology was integrated into the manufacturing workshop, to achieve an effective and high-quality production mode for furniture manufacturing. Machine vision can be used for information collecting, quality detecting, positioning, automatic sorting, intelligent monitoring, etc., which largely make up for the shortcomings of poor quality, low precision, low efficiency, and high labor intensity of manual operation. In this study, the method of systematic literature review was applied, and 128 relevant literatures in the field of machine vision application in manufacturing were retrieved and screened from 2011 to 2022. Statistical analysis was carried out on the extracted application directions and related technologies. The current status of machine vision technology’s implementation in furniture manufacturing was summarized. In view of the new demand of the rapid development of intelligent manufacturing, the challenges, faced by machine vision, were also summarized. To build a more intelligent, comprehensive, and effective manufacturing workshop for wooden products, cutting-edge technologies, such as deep learning and 3D point cloud, must be further integrated into machine vision. This study can efficiently assist the pertinent practitioners in furniture manufacturing in quickly grasping the pertinent technical principles and future development directions of machine vision, which would be benefit for accomplishing intelligent manufacturing.
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