No Arabic abstract
A general formalism of X-ray scattering from different kinds of surface morphologies is described. Based on a description of the surface morphology at the atomic scale through the use of the paracrystal model and discrete distributions of distances, the scattered intensity by non-periodic surfaces is calculated over the whole reciprocal space. In one dimension, the scattered intensity by a vicinal surface, the two-level model, the N-level model, the faceted surface and the rough surface are addressed. In two dimensions, the previous results are generalized to the kinked vicinal surface, the two-level vicinal surface and the step meandering on a vicinal surface. The concept of crystal truncation rod is generalized considering also the truncation of a terrace by a step (yielding a terrace truncation rod) and a step by a kink (yielding a step truncation rod).
We studied the resonant diffraction signal from stepped surfaces of SrTiO3 at the Ti 2p -> 3d (L2,3) resonance in comparison with x-ray absorption (XAS) and specular reflectivity data. The steps on the surface form an artificial superstructure suited as a model system for resonant soft x-ray diffraction. A small step density on the surface is sufficient to produce a well defined diffraction peak, showing the high sensitivity of the method. At larger incidence angles, the resonant diffraction spectrum from the steps on the surface resembles the spectrum for specular reflectivity. Both deviate from the XAS data in the relative peak intensities and positions of the peak maxima. We determined the optical parameters of the sample across the resonance and found that the differences between the XAS and scattering spectra reflect the different quantities probed in the different signals. When recorded at low incidence or detection angles, XAS and specular reflectivity spectra are distorted by the changes of the angle of total reflection with energy. Also the step peak spectra, though less affected, show an energy shift of the peak maxima in grazing incidence geometry.
The structural investigations of nanomaterials motivated by their large variety and diverse set of applications have attracted considerable attention. In particular, the ever-improving machinery, both in laboratory and at large scale facilities, together with the methodical improvements available for studying nanostructures ranging from epitaxial nanomaterials, nanocrystalline thin films and coatings, to nanoparticles and colloidal nanocrystals allows us to gain a more detailed understanding of their structural properties. As the structure essentially determines the physical properties of the materials, this advances the possibilities of structural studies and also enables a deeper understanding of the structure to property relationships. In this special issue entitled Investigation of Nanostructures with X-ray Scattering Techniques five contributions show the recent progress in various research fields. Contributions cover topics as diverse as neutron scattering on magnetic multilayer films, epitaxial orientation of organic thin films, nanoparticle ordering and chemical composition analysis, and the combination of nanofocused X-ray beams with electrical measurements.
Surface X-ray scattering studies of electrochemical Stern layer are reported. The Stern layers formed at the interfaces of RuO2 (110) and (100) in 0.1 M CsF electrolyte are compared to the previously reported Stern layer on Pt(111) [Liu et al., J. Phys. Chem. Lett., 9 (2018) 1265]. While the Cs+ density profiles at the potentials close to hydrogen evolution reaction are similar, the hydration layers intervening the surface and the Cs+ layer on RuO2 surfaces are significantly denser than the hydration layer on Pt(111) surface possibly due to the oxygen termination of RuO2 surfaces. We also discuss in-plane ordering in the Stern layer on Pt(111) surface.
Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.
The theoretical formulation of x-ray resonant magnetic scattering from rough surfaces and interfaces is given for the diffuse (off-specular) scattering, and general expressions are derived in both the Born approximation (BA) and the distorted-wave Born approximation (DWBA) for both single and multiple interfaces. We also give in the BA the expression for off-specular magnetic scattering from magnetic domains. For this purpose, structural and magnetic interfaces are defined in terms of roughness parameters related to their height-height correlation functions and the correlations between them. The results are generalized to the case of multiple interfaces, as in the case of thin films or multilayers. Theoretical calculations for each of the cases are illustrated as numerical examples and compared with experimental data of mangetic diffuse scattering from a Gd/Fe multilayer.