Multifunctional Properties and Applications of Shape Memory Alloys
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Physics".
Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 1813
Special Issue Editors
Interests: shape memory alloys; martensitic transformation; first-principle calculations; magnetic materials; elastocaloric materials
Interests: shape memory alloys; first-principle calculations; lightweight alloys; machine learning; caloric materials
Interests: shape memory alloys; martensitic transformation; first-principle calculations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The unique properties of shape memory alloys make them highly valuable in various industries, as they can undergo phase transformations and recover their original shape in response to external stimuli. This ability makes them ideal for use in products that require deformation or mechanical actuation. In recent years, advancements have been made in developing new types of shape memory alloys and exploring their functional properties. The goal is to design and develop alloys that are more efficient and better suited to meet the demands of various industries, such as aerospace, automotive, electronic, and biomedical. The Special Issue of Materials aims to bring together the latest research and developments in this field and provide insights into the multifunctional properties and applications of shape memory alloys. The issue covers topics such as new functionalities, high-throughput multiscale materials computing and machine learning for efficient design methods, additive manufacturing for innovative synthesis and processing, and advanced material characterization techniques.
We invite submissions of manuscripts to this Special Issue that address the listed topics.
Prof. Dr. Xiaohua Tian
Dr. Wenbin Zhao
Prof. Dr. Changlong Tan
Guest Editors
Manuscript Submission Information
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Keywords
- shape memory alloys
- phase transformation
- first-principle calculations
- machine learning
- structural characterization
- elastocaloric effect
- magnetocaloric effect
- barocaloric effect
- additive manufacturing.
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