Neutron dark-field imaging of hierarchical structures using INFER*

Neutrons are an essential tool for understanding the microstructure of soft matter. Due to the underlying theory of existing neutron scattering instruments, neutron studies have been limited to volume averages which precludes studying the evolution of hierarchical structures such as those found in many biological, geological and electrochemical systems. NIST is developing a prototype neutron dark-field imaging instrument, dubbed "INFER", based on far-field grating interferometers [1,2]. Dark-field images directly contain information of the pair-correlation function G(ξ) revealing microstructural information of the sample material [3]. The mathematics of dark-field image formation are analogous to transmission imaging, thus INFER will generate multi-scale data sets spanning length scales from the nm to the cm, in 3D with a voxel size 0.05 mm. To acquire these data sets (100+ tomograms) in a reasonable time (<1 day) requires new neutron optical components, including a novel neutron source grating. To analyze the more than 1 billion independent G(ξ) requires novel artificial intelligence based segmentation and curve fitting models. We will present simulated and measured results from model colloidal systems using a prototype INFER and discuss the outlook for future user experiments.