The layering of ionic liquids close to flat, charged interfaces has been identified previously through theoretical and some experimental measurements. Here we present evidence for oscillations in ion density ('layering') in a long chain ionic liquid (1-decyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide) near the interface with mica using two complementary approaches. Neutron reflection at the ionic liquid-mica interface is used to detect structure at a single interface, and surface force balance (SFB) measurements carried out with the same ionic liquid reveal oscillatory density in the liquid confined between two mica sheets. Our findings imply the interfacial structure is not induced by confinement alone. Structural forces between two mica surfaces extend to approximately twice the distance of the density oscillations measured at a single interface and have similar period in both cases.

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Neutron and x-ray reflectometry (NR and XRR) are powerful techniques to investigate the structural, morphological and even magnetic properties of solid and liquid thin films. While neutrons and x-rays behave similarly in many ways and can be described by the same general theory, they fundamentally differ in certain specific aspects. These aspects can be exploited to investigate different properties of a system, depending on which particular questions need to be answered. Having demonstrated the general applicability of neural networks to analyze XRR and NR data before (Greco et al 2019 J. Appl. Cryst. 52 1342), this study discusses challenges arising from certain pathological cases as well as performance issues and perspectives. These cases include a low signal-to-noise ratio, a high background signal (e.g. from incoherent scattering), as well as a potential lack of a total reflection edge (TRE). By dynamically modifying the training data after every mini batch, a fully-connected neural network was trained to determine thin film parameters from reflectivity curves. We show that noise and background intensity pose no significant problem as long as they do not affect the TRE. However, for curves without strong features the prediction accuracy is diminished. Furthermore, we compare the prediction accuracy for different scattering length density combinations. The results are demonstrated using simulated data of a single-layer system while also discussing challenges for multi-component systems.

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