Reduced-order model to approximate response matrices for filter stack spectrometers.

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.

Surface Treatment Strategies and Their Impact on the Material Behavior and Interfacial Adhesion Strength of Shape Memory Alloy NiTi Wire Integrated in Glass Fiber-Reinforced Polymer Laminate Structures.

Over the past few decades, there has been a growing trend in designing multifunctional materials and integrating various functions into a single component structure without defects. This research addresses the contemporary demand for integrating multiple functions seamlessly into thermoplastic laminate structures. Focusing on NiTi-based shape memory alloys (SMAs), renowned for their potential in introducing functionalities like strain measurement and shape change, this study explores diverse surface treatments for SMA wires.

ZIF-67-derived Co-N-C supported nickel cobalt sulfide as a bifunctional electrocatalyst for sustainable hydrogen production alkaline electrolysis.

As non-renewable resources are finite and cannot be utilized indefinitely, hydrogen (H) has emerged as a promising alternative for clean and sustainable energy. The cost-effective hydrogen production to meet large-scale commercial demand poses a significant challenge. Water electrolysis, powered by electricity derived from renewable resources, stands out as a viable route towards sustainable hydrogen production, with electrocatalysis playing a pivotal role in this process.

Robust unfolding of MeV x-ray spectra from filter stack spectrometer data.

We present an inversion method capable of robustly unfolding MeV x-ray spectra from filter stack spectrometer (FSS) data without requiring an a priori specification of a spectral shape or arbitrary termination of the algorithm. Our inversion method is based upon the perturbative minimization (PM) algorithm, which has previously been shown to be capable of unfolding x-ray transmission data, albeit for a limited regime in which the x-ray mass attenuation coefficient of the filter material increases monotonically with x-ray energy. Our inversion method improves upon the PM algorithm through regular smoothing of the candidate spectrum and by adding stochasticity to the search.