A simple kinetic sensor to structural transitions

TL;DR

This paper introduces a simple kinetic sensor based on non-Gaussian noise analysis to detect structural phase transitions, demonstrated through nickel-titanium alloy martensite transformations, offering a robust, portable method for studying phase changes.

Contribution

The study presents a novel application of non-Gaussian noise statistics as a kinetic detector for structural phase transitions, validated through experiments and models, with potential broad applicability.

Findings

Non-Gaussian noise deviations mark phase transformation zones.

The method accurately estimates transformation temperature scales.

Microstructure evolution correlates with noise statistics.

Abstract

Driven non-equilibrium structural phase transformation has been probed using time varying resistance fluctuations or noise. We demonstrate that the non-Gaussian component (NGC) of noise obtained by evaluating the higher order statistics of fluctuations, serves as a simple kinetic detector of these phase transitions. Using the martensite transformation in free-standing wires of nickel-titanium binary alloys as a prototype, we observe clear deviations from the Gaussian background in the transformation zone, indicative of the long range correlations in the system as the phase transforms. The viability of non- Gaussian statistics as a robust probe to structural phase transition was also confirmed by comparing the results from differential scanning calorimetry measurements. We further studied the response of the NGC to the modifications in the microstructure on repeated thermal cycling, as well as the variations in the temperature drive rate, and explained the results using established simplistic models based on the different competing time scales. Our experiments (i) suggest an alternative method to estimate the transformation temperature scales with high accuracy, and (ii) establish a connection between the material-specific evolution of microstructure to the statistics of its linear response. Since the method depends on an in-built long-range correlation during transformation, it could be portable to other structural transitions, as well as to materials of different physical origin and size.