Elastocaloric potential in copper-based SMAs through a combinatorial approach

Elastocaloric applications exploit the latent heat from Shape Memory Alloy (SMA) through its stress-induced phase transformation. The elastocaloric potential of a SMA depends on its latent heat, critical transformation stress, hysteresis, heat capacity and conductivity, and, most importantly, its cost-effectiveness. While the elastocaloric effect has been studied over ten years by now, the field still lacks cost-effective SMAs that can generate a reasonable deltaT with low biasing stress. To date, NiTi prevails in the field with deltaT as high as 25°C and good fatigue resistance. However, the high cost and high biasing stress (>600 MPa) limit its potential for widespread industrial and household elastocaloric cooling applications. Copper-based SMAs are low-cost elastocaloric materials with low biasing stress except that they have low latent heat. This talk reports a comprehensive compositional optimizing effort to maximize latent heat and transition window while minimizing hysteresis for copper-based SMAs. The effort uses a high throughput combinatorial approach to scan through more than 300 bulk samples (1g each) with different compositions. The samples were synthesized by combi-arc melting and then heat-treated. The transformation characteristics of grouped samples were determined simultaneously using a novel DTA type method based on thermal imaging. DSC was then used on down-selected compositions for verification and latent heat analysis. The combinatorial effect has benchmarked CuAlNi compositions with a latent heat of 8.4 J/g and hysteresis of 18°C. The processability, mechanical properties, and deltaT were also explored in selected compositions with great elastocaloric potential.