Zhang, Chenbo | Search NASA

DOE OSTI2021

Energy Conversion from Heat to Electricity by Highly Reversible Phase-Transforming Ferroelectrics

The search for performant multiferroic materials has attracted general research interest in energy science as they have been increasingly exploited as the conversion media among thermal, electric, magnetic, and mechanical energies by using their temperature-dependent ferroic properties. Here we report a material development strategy that guides us to discover a reversible phase-transforming ferroelectric material exhibiting enduring energy harvesting from small temperature differences. The material satisfies the crystallographic compatibility condition between polar and nonpolar phases, which shows only 2.5°C thermal hysteresis and a high figure of merit. It stably generates 15μA of electricity in consecutive thermodynamic cycles in the absence of any bias fields. We demonstrate our device to consistently generate 6μA/cm 2 current density near 100°C over 540 complete phase transformation cycles without any electric and functional degradation. The energy-conversion device can light up an LED directly without attaching an external power source. Furthermore, this promising material candidate brings the low-grade waste-heat harvesting closer to a practical realization, e.g., small temperature fluctuations around the water boiling point can be considered as a clean energy source.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2019

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

A novel data-driven approach is proposed for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. It is demonstrated through typical examples including polycrystalline BaTiO 3 , multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern-by-pattern crystal indexing process. Here, this work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning perspective for the development of suitable feature extraction, clustering and labeling algorithms.

42 ENGINEERING↗