Quantum Beam Science

At a spallation neutron source, neutron pulses of varying energies are generated, and the detection of neutrons by instrument detectors is recorded as time-of-flight from the emission of the neutron pulse to its arrival at specific detector pixels with high time resolution. The flight path of neutrons from the moderator to the sample and then to the detector must be precisely calibrated at the detector-pixel level using standard powders, so the neutron events from all pixels can be time-focused to produce high-resolution diffraction patterns. Modern time-of-flight neutron diffractometers at spallation neutron sources are equipped with two-dimensional detectors with millimeter-scale pixelations. The number of pixels in a diffraction instrument can reach millions, which makes a single-pixel-level calibration process time-consuming or even impossible with conventional refinement or fitting approaches. Here we present a machine-learning-aided calibration process using a train-and-predict approach, in which machine learning models are trained on the relationship between an individual pixel time-of-flight diffraction pattern and its diffraction constant. These models use a portion of the available pixels for training, and a good model then predicts the diffraction constants precisely and rapidly for large sets of pixel diffraction patterns.

Understanding the deformation mechanisms of materials at cryogenic temperatures is crucial for cryogenic engineering applications. In situ neutron diffraction is a powerful technique for probing such mechanisms under cryogenic conditions. In this study, we present the development of a compact cryogenic environment (CCE) designed to facilitate in situ neutron diffraction experiments under mechanical loading at temperatures as low as 77 K with a maximum cooling rate of 6 K/min. The CCE features a polystyrene foam cryogenic chamber, aluminum blocks serving as neutron-transparent cold sinks, a liquid nitrogen dosing system for cryogen delivery, a nitrogen gas flow control system for thermal management, a process controller for temperature control, and a pair of thermally isolated grip adapters for mechanical testing. The CCE achieves reliable temperature control with minimal neutron attenuation. Utilizing this setup, we conducted three in situ neutron diffraction tensile tests on a 316L stainless steel at 77, 173, and 298 K, respectively. The results highlight the pronounced effects of cryogenic temperatures on the material’s deformation mechanisms, underscoring both the significance of cryogenic deformation studies and the effectiveness of the CCE.

Polarized neutron reflectometry (PNR) analyzes surface and interfacial structures of materials. For the SHARAKU reflectometer at the Materials and Life Science Experimental Facility in the Japan Proton Accelerator Research Complex, precise measurements under weak magnetic fields, which are critical for modern spintronics, have long been challenging. To address this issue, we developed a precise weak-field sample environment equipped with a newly designed coil system. The magnetic field at the sample position can be applied within the surface/interface plane, either in the scattering plane (horizontal configuration) or perpendicular to it (vertical configuration). The horizontal configuration achieved high polarization efficiency across a stable field range, whereas the vertical configuration enabled the experiments to cross zero into negative fields. We demonstrated the instrument’s capability by resolving the remanent magnetic structure of an Fe film. Its applicability to soft matter was proven through analysis of a cellulose thin film with roughness using magnetic contrast variation PNR. In this case, precise weak-field control is essential to tune the magnetic contrast from the reference layer beneath the soft film. These results establish the system as a versatile platform for future PNR and polarized off-specular scattering experiments across a wide range of materials.