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In this study, a NASICON-structured Li1.3Al0.3Ti1.7(PO4)3 (LATP) powder is prepared by hydrothermal methods followed by calcination, cold pressing and post-sintering processes. The white, solid product is characterized thoroughly using powder X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) equipped with Energy Dispersive X-ray Spectroscopy (EDS). The conductivity of the material is measured by a impedance spectroscopy as a function of temperature. Initially, hydrothermal synthesis yields a material isostructural with the orthorhombic oxyphosphate, LiTiOPO4. EDS analysis shows that the distribution of aluminum throughout this material is uniform. A systematic study is then performed to investigate how altering the sintering parameters (such as powder pre-sintering temperature and pellet sintering temperature) affect the formation of LATP. The structure is determined by Rietveld refinement against XRD data and the effects of sintering temperature on porosity, microstructure and electrical conductivity were resolved. The experimental results show that the optimum pre-sintering and sintering temperatures of LATP powders and pellets respectively are 900 ℃ and 1100 ℃. These conditions produce materials with the highest density (99.07% of theoretical), superior conductivity (grain-, grain boundary-and total lithium-ion conductivities of 6.5710 -4 , 4.5910 -4 and 2.7010 -4 S cm -1 , respectively) and with an activation energy for Li motion of 0.17 eV.

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Product disassembly is a labor-intensive process and is far from being automated. Typically, disassembly is not robust enough to handle product varieties from different shapes, models, and physical uncertainties due to component imperfections, damage throughout component usage, or insufficient product information. To overcome these difficulties and to automate the disassembly procedure through human-robot collaboration without excessive computational cost, this paper proposes a real-time receding horizon sequence planner that distributes tasks between robot and human operator while taking real-time human motion into consideration. The sequence planner aims to address several issues in the disassembly line, such as varying orientations, safety constraints of human operators, uncertainty of human operation, and the computational cost of large number of disassembly tasks. The proposed disassembly sequence planner identifies both the positions and orientations of the to-be-disassembled items, as well as the locations of human operator, and obtains an optimal disassembly sequence that follows disassembly rules and safety constraints for human operation. Experimental tests have been conducted to validate the proposed planner: the robot can locate and disassemble the components following the optimal sequence, and consider explicitly human operator’s real-time motion, and collaborate with the human operator without violating safety constraints.