scholarly journals Future Prospects: Shape Memory Features in Shape Memory Polymers and Their Corresponding Composites

2019 ◽  
Author(s):  
Safaa N. Saud Al-Humairi ◽  
Hasan Sh. Majdi ◽  
Amir N. Saud Al-Humairi ◽  
Mohammed Al-Maamori
Keyword(s):  
Shape Memory ◽  
Shape Memory Polymers ◽  
Future Prospects

scholarly journals Shape Memory Corrosion-Resistant Polymeric Materials

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Nathan D. Jacobson ◽  
Jude Iroh
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Corrosion Inhibition ◽  
Shape Memory Polymer ◽  
Shape Memory Polymers ◽  
Polymeric Materials ◽  
Initial Position ◽  
Self Healing ◽  
Future Prospects ◽  
Corrosion Resistant

Shape memory alloys, materials capable of being deformed and maintaining the deformation and additionally capable of returning to the initial position, are valued for a range of applications from actuators to flexible microdevices. Maintaining the properties that make them useful, their ability to deform and reform, requires that shape memory alloys must be protected against corrosion, in which the integration of shape memory polymers can act as a means of protection. Thus, this review is to highlight the utility of self-healing shape memory polymers as a means of corrosion inhibition. Therefore, this review discusses the benefits of utilizing self-healing shape memory polymers for the protection of shape memory, several types of self-healing polymers that could be used, means of improving or tailoring the polymers towards specific usages, and future prospects in designing a shape memory polymer for use in corrosion inhibition.

Active composites based on shape memory polymers: overview, fabrication methods, applications, and future prospects

2020 ◽  
Vol 55 (25) ◽  
pp. 10975-11051 ◽  
Author(s):  
Stephen Kirwa Melly ◽  
Liwu Liu ◽  
Yanju Liu ◽  
Jinsong Leng
Keyword(s):  
Shape Memory ◽  
Shape Memory Polymers ◽  
Future Prospects

Tissue Integration of Two Different Shape-Memory Polymers with Poly(ε-Caprolactone) Switching Segment in Rats

2008 ◽  
Author(s):  
Bernhard Hiebl ◽  
Dorothee Rickert ◽  
Rosemarie Fuhrmann ◽  
Friedrich Jung ◽  
Andres Lendlein ◽  
...  
Keyword(s):  
Shape Memory ◽  
Shape Memory Polymers ◽  
Tissue Integration

Three-dimensional Graphene Coated Shape Memory Polyurethane Foam with Fast Responsive Performance

2021 ◽  
Author(s):  
Tianjiao Wang ◽  
Jun Zhao ◽  
Chuanxin Weng ◽  
Tong Wang ◽  
Yayun Liu ◽  
...  
Keyword(s):  
Shape Memory ◽  
Polyurethane Foam ◽  
Three Dimensional ◽  
Shape Memory Polymers ◽  
Practical Applications ◽  
External Stimuli ◽  
Shape Memory Polyurethane

Shape memory polymers (SMPs) that change shapes as designed by external stimuli have become one of the most promising materials as actuators, sensors, and deployable devices. However, their practical applications...

Macroporous Superhydrophobic Coatings with Switchable Wettability Enabled by Smart Shape Memory Polymers

2021 ◽  
pp. 2002111
Author(s):  
Calen J. Leverant ◽  
Yifan Zhang ◽  
Maria A. Cordoba ◽  
Sin‐Yen Leo ◽  
Nilesh Charpota ◽  
...  
Keyword(s):  
Shape Memory ◽  
Shape Memory Polymers ◽  
Superhydrophobic Coatings

scholarly journals Harnessing reversible dry adhesion using shape memory polymer microparticles

RSC Advances ◽  
2021 ◽  
Vol 11 (32) ◽  
pp. 19616-19622
Author(s):  
Wenbing Li ◽  
Junhao Liu ◽  
Wanting Wei ◽  
Kun Qian
Keyword(s):  
Shape Memory ◽  
Shape Memory Polymer ◽  
Shape Memory Polymers ◽  
Memory Effects ◽  
Reversible Adhesion ◽  
Dry Adhesion ◽  
Bonding Property ◽  
Shape Memory Effects ◽  
Polymer Microparticles

Shape memory polymers can provide excellent bonding property because of their shape memory effects. This paper proposes an adhesive unit that is capable of repeatable smart adhesion and exhibits reversible adhesion under heating.

scholarly journals Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset

Polymer ◽  
2021 ◽  
Vol 214 ◽  
pp. 123351
Author(s):  
Cheng Yan ◽  
Xiaming Feng ◽  
Collin Wick ◽  
Andrew Peters ◽  
Guoqiang Li
Keyword(s):  
Machine Learning ◽  
Shape Memory ◽  
Shape Memory Polymers ◽  
Training Dataset
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