Predictions of Lattice Parameters in NiTi High-Entropy Shape-Memory Alloys Using Different Machine Learning Models.

This work applied three machine learning (ML) models-linear regression (LR), random forest (RF), and support vector regression (SVR)-to predict the lattice parameters of the monoclinic B19' phase in two distinct training datasets: previously published ZrO-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for a, a, b, and c in NiTi-based HESMAs, while RF excelled in computing β for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets.

In-Situ Synchrotron SAXS and WAXS Investigation on the Deformation of Single and Coaxial Electrospun P(VDF-TrFE)-Based Nanofibers.

Coaxial core/shell electrospun nanofibers consisting of ferroelectric P(VDF-TrFE) and relaxor ferroelectric P(VDF-TrFE-CTFE) are tailor-made with hierarchical structures to modulate their mechanical properties with respect to their constituents. Compared with two single and the other coaxial membranes prepared in the research, the core/shell-TrFE/CTFE membrane shows a more prominent mechanical anisotropy between revolving direction (RD) and cross direction (CD) associated with improved resistance to tensile stress for the crystallite phase stability and good strength-ductility balance. This is due to the better degree of core/shell-TrFE-CTFE nanofiber alignment and the crystalline/amorphous ratio.