Accelerating sample preparation of graded thermoelectric materials using an automatic powder feeding system
Year: 2019
Type: article
Abstract: In recent years, the development of techniques for the controlled preparation of functional graded materials (FGMs) has become a vigorous research field. In this study, to improve the efficiency and accuracy of sample preparations, an automated feeding system based on gravimetric principles for dry powder with three dosing feeders is designed and realized. The feeding rate and accuracy can be regulated by coordinating the protruded length L (mm) and rotational speed V (r/min) of the feeder stirrer. To demonstrate this automatic sample preparation system, the well-known thermoelectric material Bi x Sb 2− x Te 3 ( x = 0.3, 0.4, 0.5, 0.6, 0.7 and 0.8) is selected and prepared by the developed system, and the composition distribution of the functional graded material is characterized. Experimental results show that the Bi x Sb 2− x Te 3 ( x = 0.3–0.8) functionally graded material crystalizes in the rhombohedral phase after hot-pressing sintering and annealing and the prepared sample has a good gradient composition distribution. This verifies the reliability and accuracy of the feeding system. The concept of samples with a gradient component and application of the automatic powder feeding system could considerably accelerate the research and development of new materials.
Source: Advances in Manufacturing
Institution: Shanghai University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,690
Funders: National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Henan Polytechnic University, Tianjin University, Zhengzhou University, Shanghai Jiao Tong University, Nankai University, Ritsumeikan University, Freie Universität Berlin, Tianjin Polytechnic University, Shanghai Rising-Star Program, National Key Research and Development Program of China, Institute of Intelligent Machines, Chinese Academy of Sciences
OpenAlex ID: W2970629531
