4.6 Article

Smart polyimide with recovery stress at the level of high temperature shape memory alloys

Journal

SMART MATERIALS AND STRUCTURES
Volume 30, Issue 3, Pages -

Publisher

IOP Publishing Ltd
DOI: 10.1088/1361-665X/abe182

Keywords

shape memory polymer; recovery stress; high temperature; shape memory alloy; polyimide; carbon fiber cloth

Funding

  1. Natural Science Foundation of Heilongjiang Province [LC2018023]
  2. Science and Technology Innovation Program for Talents of Harbin [2017RALXJ004]

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This study introduces a method to prepare high recovery stress shape memory polyimide (HRSMPI) by reinforcing smart polyimide matrix with carbon fiber cloth (CFC). The HRSMPI possesses a unique sandwich structure with good interfacial bonding, exhibiting high glass transition temperature and excellent performance, with recovery stress close to high temperature shape memory alloys.
Recovery stress is important for smart shape memory materials (SMMs) and low recovery stress of shape memory polymers hinders their applications badly, while high temperature shape memory alloys (HTSMAs) are very difficult to be obtained. In order to obtain high temperature SMM with high recovery stress, convenient preparation methods, low cost and good workability, shape memory polyimide with high recovery stress (HRSMPI) at the level of HTSMA is prepared by reinforcing smart polyimide matrix with carbon fiber cloth (CFC) in common lab furnace. The HRSMPI possesses unique sandwich structure with good interfacial bonding between the matrix and filler. It exhibits high glass transition temperature of 300 degrees C, shape fixity of 90.6% and shape recovery of 92.2%. The recovery stress of HRSMPI is 116 MPa, close to those of some HTSMAs. The primitive actuator made of HRSMPI can overturn metal sheet 147 times heavier than itself, similar to the commercial TiNiHf HTSMA. Meanwhile, its density is 0.96 g cm(-3), less than 1/6 of that of TiNiHf. The elastic strain energy is mainly stored in CFC and then released as recovery stress under constrained shape recovery, and HRSMPI has great potential in practical applications.

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