Low-background neutron reflectometry from solid/liquid interfaces

Liquid cells are an increasingly common sample environment for neutron reflectometry experiments and are critical for measuring the properties of materials at solid/liquid interfaces. Background scattering determines the maximum useful scattering vector, and hence the spatial resolution, of the neutron reflectometry measurement. The primary sources of background are the liquid in the cell reservoir and the materials forming the liquid cell itself. Thus, characterization and mitigation of these background sources are necessary for improvements in the signal-to-background ratio and resolution of neutron reflectometry measurements employing liquid cells. Single-crystal silicon is a common material used for liquid cells due to its low incoherent scattering cross section for neutrons, and the path lengths of the neutron beam through silicon can be several centimetres in modern cell designs. Here, a liquid cell is constructed with a sub-50 µm thick liquid reservoir encased in single-crystal silicon. It is shown that, at high scattering vectors, inelastic scattering from silicon represents a significant portion of the scattering background and is, moreover, structured, confounding efforts to correct for it by established background subtraction techniques. A significant improvement in the measurement quality is achieved using energy-analyzed detection. Energy-analyzed detection reduces the scattering background from silicon by nearly an order of magnitude, and from fluids such as air and liquids by smaller but significant factors. Combining thin liquid reservoirs with energy-analyzed detection and the high flux of the CANDOR polychromatic reflectometer at the NIST Center for Neutron Research, a background-subtracted neutron reflectivity smaller than 10−8 from a liquid cell sample is reported.

Neutron Reflectometry-Based In Situ Structural Analysis of an Aligning Agent Additive for the Alignment of Nematic Liquid Crystals on Solid Substrates

Surface aligning agents, such as amphiphilic surfactants, are widely used to control the initial alignment of nematic liquid crystals (NLCs) in liquid crystal displays (LCDs). Generally, these agents are first...

Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry

Neutron reflectometry (NR) allows us to probe into the structure of the surfaces and interfaces of various materials such as soft matters and magnetic thin films with a contrast mechanism dependent on isotopic and magnetic states. The neutron beam flux is relatively low compared to that of other sources such as synchrotron radiation; therefore, there has been a strong limitation in the time-resolved measurement and further advanced experiments such as surface imaging. This study aims at the development of a methodology to enable the structural analysis by the NR data with a large statistical error acquired in a short measurement time. The neural network-based method predicts the true NR profile from the data with a 20-fold lower signal compared to that obtained under the conventional measurement condition. This indicates that the acquisition time in the NR measurement can be reduced by more than one order of magnitude. The current method will help achieve remarkable improvement in temporally and spatially resolved NR methods to gain further insight into the surface and interfaces of materials.