X-ray reflectivity from curved liquid interfaces

The laser pump X-ray probe system at LISA P08 PETRA III

Understanding and controlling the structure and function of liquid interfaces is a constant challenge in biology, nanoscience and nanotechnology, with applications ranging from molecular electronics to controlled drug release. X-ray reflectivity and grazing incidence diffraction provide invaluable probes for studying the atomic scale structure at liquid-air interfaces. The new time-resolved laser system at the LISA liquid diffractometer situated at beamline P08 at the PETRA III synchrotron radiation source in Hamburg provides a laser pump with X-ray probe. The femtosecond laser combined with the LISA diffractometer allows unique opportunities to investigate photo-induced structural changes at liquid interfaces on the pico- and nanosecond time scales with pump-probe techniques. A time resolution of 38 ps has been achieved and verified with Bi. First experiments include laser-induced effects on salt solutions and liquid mercury surfaces with static and varied time scales measurements showing the proof of concept for investigations at liquid surfaces.

Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.

Millisecond X-ray reflectometry and neural network analysis: unveiling fast processes in spin coating

X-ray reflectometry (XRR) is a powerful tool for probing the structural characteristics of nanoscale films and layered structures, which is an important field of nanotechnology and is often used in semiconductor and optics manufacturing. This study introduces a novel approach for conducting quantitative high-resolution millisecond monochromatic XRR measurements. This is an order of magnitude faster than in previously published work. Quick XRR (qXRR) enables real time and in situ monitoring of nanoscale processes such as thin film formation during spin coating. A record qXRR acquisition time of 1.4 ms is demonstrated for a static gold thin film on a silicon sample. As a second example of this novel approach, dynamic in situ measurements are performed during PMMA spin coating onto silicon wafers and fast fitting of XRR curves using machine learning is demonstrated. This investigation primarily focuses on the evolution of film structure and surface morphology, resolving for the first time with qXRR the initial film thinning via mass transport and also shedding light on later thinning via solvent evaporation. This innovative millisecond qXRR technique is of significance for in situ studies of thin film deposition. It addresses the challenge of following intrinsically fast processes, such as thin film growth of high deposition rate or spin coating. Beyond thin film growth processes, millisecond XRR has implications for resolving fast structural changes such as photostriction or diffusion processes.

In Situ Probing of the Surface Properties of Droplets in the Air

Surface properties of nanodroplets and microdroplets are intertwined with their immense applicability in biology, medicine, production, catalysis, the environment, and the atmosphere. However, many means for analyzing droplets and their surfaces are destructive, non-interface-specific, not conducted under ambient conditions, require sample substrates, conducted ex situ, or a combination thereof. For these reasons, a technique for surface-selective in situ analyses under any condition is necessary. This feature article presents recent developments in second-order nonlinear optical scattering techniques for the in situ interfacial analysis of aerosol droplets in the air. First, we describe the abundant utilization of such droplets across industries and how their unique surface properties lead to their ubiquitous usage. Then, we describe the fundamental properties of droplets and their surfaces followed by common methods for their study. We next describe the fundamental principles of sum-frequency generation (SFG) spectroscopy, the Langmuir adsorption model, and how they are used together to describe adsorption processes at planar liquid and droplet surfaces. We also discuss the history of developments of second-order scattering from droplets suspended in dispersive media and introduce second-harmonic scattering (SHS) and sum-frequency scattering (SFS) spectroscopies. We then go on to outline the developments of SHS, electronic sum-frequency scattering (ESFS), and vibrational sum-frequency scattering (VSFS) from droplets in the air and discuss the fundamental insights about droplet surfaces that the techniques have provided. Finally, we describe some of the areas of nonlinear scattering from airborne droplets which need improvement as well as potential future directions and utilizations of SHS, ESFS, and VSFS throughout environmental systems, interfacial chemistry, and fundamental physics. The goal of this feature article is to spread knowledge about droplets and their unique surface properties as well as introduce second-order nonlinear scattering to a broad audience who may be unaware of recent progress and advancements in their applicability.

X-ray reflectivity from curved surfaces as illustrated by a graphene layer on molten copper

The X-ray reflectivity technique can provide out-of-plane electron-density profiles of surfaces, interfaces, and thin films, with atomic resolution accuracy. While current methodologies require high surface flatness, this becomes challenging for naturally curved surfaces, particularly for liquid metals, due to the very high surface tension. Here, the development of X-ray reflectivity measurements with beam sizes of a few tens of micrometres on highly curved liquid surfaces using a synchrotron diffractometer equipped with a double crystal beam deflector is presented. The proposed and developed method, which uses a standard reflectivity θ-2θ scan, is successfully applied to study in situ the bare surface of molten copper and molten copper covered by a graphene layer grown in situ by chemical vapor deposition. It was found that the roughness of the bare liquid surface of copper at 1400 K is 1.25 ± 0.10 Å, while the graphene layer is separated from the liquid surface by a distance of 1.55 ± 0.08 Å and has a roughness of 1.26 ± 0.09 Å.