Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques

Diffuse soft matter interfaces take many forms, from end-tethered polymer brushes or adsorbed surfactants to self-assembled layers of lipids. These interfaces play crucial roles across a multitude of fields, including materials science, biophysics, and nanotechnology. Understanding the nanostructure and properties of these interfaces is fundamental for optimising their performance and designing novel functional materials. In recent years, reflectometry techniques, in particular neutron reflectometry, have emerged as powerful tools for elucidating the intricate nanostructure of soft matter interfaces with remarkable precision and depth. This review provides an overview of selected recent developments in reflectometry and their applications for illuminating the nanostructure of diffuse interfaces. We explore various principles and methods of neutron and X-ray reflectometry, as well as ellipsometry, and discuss advances in their experimental setups and data analysis approaches. Improvements to experimental neutron reflectometry methods have enabled greater time resolution in kinetic measurements and elucidation of diffuse structure under shear or confinement, while innovation in analysis protocols has significantly reduced data processing times, facilitated co-refinement of reflectometry data from multiple instruments and provided greater-than-ever confidence in proposed structural models. Furthermore, we highlight some significant research findings enabled by these techniques, revealing the organisation, dynamics, and interfacial phenomena at the nanoscale. We also discuss future directions and potential advancements in reflectometry techniques. By shedding light on the nanostructure of diffuse interfaces, reflectometry techniques enable the rational design and tailoring of interfaces with enhanced properties and functionalities.

Advances in sample environments for neutron scattering for colloid and interface science

This review describes recent advances in sample environments across the full complement of applicable neutron scattering techniques to colloid and interface science. Temperature, pressure, flow, tensile testing, ultrasound, chemical reactions, IR/visible/UV light, confinement, humidity and electric and magnetic field application, as well as tandem X-ray methods, are all addressed. Consideration for material choices in sample environments and data acquisition methods are also covered as well as discussion of current and potential future use of machine learning and artificial intelligence.

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.

Review of air-water interface adsorption and reactions between trace gaseous organic and oxidant compounds

The surface chemistry of the atmospheric aerosol through homogeneous and heterogeneous catalytic reactions in the bulk water and the air-water surface is reviewed. Water plays a critical role as a substrate or an actual reactant in atmospheric reactions. The atmospheric aerosol differs in shape and surface area. Many gaseous reactive species and oxidants react at the air-water surface. Different thermodynamic methods to estimate partitioning coefficients are explored. The Gibbs free energy is reduced when reactant gaseous species react with oxidant at the air-water surface; this phenomenon is explained using examples. Langmuir-Hinshelwood reaction mechanism to quantify the heterogeneous reaction rate at the air-water interface is discussed. Critical comparisons of various sampling techniques used to analyze adsorption and reaction at the water surface are presented. The heterogeneous reaction rate at the air-water surface is significantly higher than in the bulk water phase due to a cage effect, higher rate of reactions, and lower Gibbs free energy of adsorption.

Cinematic reflectometry using QIKR, the quite intense kinetics reflectometer

The Quite Intense Kinetics Reflectometer (QIKR) will be a general-purpose, horizontal-sample-surface neutron reflectometer. Reflectometers measure the proportion of an incident probe beam reflected from a surface as a function of wavevector (momentum) transfer to infer the distribution and composition of matter near an interface. The unique scattering properties of neutrons make this technique especially useful in the study of soft matter, biomaterials, and materials used in energy storage. Exploiting the increased brilliance of the Spallation Neutron Source Second Target Station, QIKR will collect specular and off-specular reflectivity data faster than the best existing such machines. It will often be possible to collect complete specular reflectivity curves using a single instrument setting, enabling "cinematic" operation, wherein the user turns on the instrument and "films" the sample. Samples in time-dependent environments (e.g., temperature, electrochemical, or undergoing chemical alteration) will be observed in real time, in favorable cases with frame rates as fast as 1 Hz. Cinematic data acquisition promises to make time-dependent measurements routine, with time resolution specified during post-experiment data analysis. This capability will be deployed to observe such processes as in situ polymer diffusion, battery electrode charge-discharge cycles, hysteresis loops, and membrane protein insertion into lipid layers.

A mobile setup for simultaneous and in situ neutron reflectivity, infrared spectroscopy, and ellipsometry studies

Neutron reflectivity at the solid/liquid interface offers unique opportunities for resolving the structure-function relationships of interfacial layers in soft matter science. It is a non-destructive technique for detailed analysis of layered structures on molecular length scales, providing thickness, density, roughness, and composition of individual layers or components of adsorbed films. However, there are also some well-known limitations of this method, such as the lack of chemical information, the difficulties in determining large layer thicknesses, and the limited time resolution. We have addressed these shortcomings by designing and implementing a portable sample environment for in situ characterization at neutron reflectometry beamlines, integrating infrared spectroscopy under attenuated total reflection for determination of molecular entities and their conformation, and spectroscopic ellipsometry for rapid and independent measurement of layer thicknesses and refractive indices. The utility of this combined setup is demonstrated by two projects investigating (a) pH-dependent swelling of polyelectrolyte layers and (b) the impact of nanoparticles on lipid membranes to identify potential mechanisms of nanotoxicity.

Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement

An approach is presented for analysis of real-time X-ray reflectivity (XRR) process data not just as a function of the magnitude of the reciprocal-space vector q, as is commonly done, but as a function of both q and time. The real-space structures extracted from the XRR curves are restricted to be solutions of a physics-informed growth model and use state-of-the-art convolutional neural networks (CNNs) and differential evolution fitting to co-refine multiple time-dependent XRR curves R(q, t) of a thin film growth experiment. Thereby it becomes possible to correctly analyze XRR data with a fidelity corresponding to standard fits of individual XRR curves, even if they are sparsely sampled, with a sevenfold reduction of XRR data points, or if the data are noisy due to a 200-fold reduction in counting times. The approach of using a CNN analysis and of including prior information through a kinetic model is not limited to growth studies but can be easily extended to other kinetic X-ray or neutron reflectivity data to enable faster measurements with less beam damage.

Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering

A Python-based analysis pipeline for the fast analysis of X-ray and neutron reflectivity data using neural networks is presented.

The Python package mlreflect is demonstrated, which implements an optimized pipeline for the automated analysis of reflectometry data using machine learning. The package combines several training and data treatment techniques discussed in previous publications. The predictions made by the neural network are accurate and robust enough to serve as good starting parameters for an optional subsequent least-mean-squares (LMS) fit of the data. For a large data set of 242 reflectivity curves of various thin films on silicon substrates, the pipeline reliably finds an LMS minimum very close to a fit produced by a human researcher with the application of physical knowledge and carefully chosen boundary conditions. The differences between simulated and experimental data and their implications for the training and performance of neural networks are discussed. The experimental test set is used to determine the optimal noise level during training. The extremely fast prediction times of the neural network are leveraged to compensate for systematic errors by sampling slight variations in the data.

Neural network analysis of neutron and x-ray reflectivity data: pathological cases, performance and perspectives

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.

The reaction of oleic acid monolayers with gas-phase ozone at the air water interface: the effect of sub-phase viscosity, and inert secondary components

Organic films that form on atmospheric particulate matter change the optical and cloud condensation nucleation properties of the particulate matter and consequently have implications for modern climate and climate models. The organic films are subject to attack from gas-phase oxidants present in ambient air. Here we revisit in greater detail the oxidation of a monolayer of oleic acid by gas-phase ozone at the air-water interface as this provides a model system for the oxidation reactions that occur at the air-water interface of aqueous atmospheric aerosol. Experiments were performed on monolayers of oleic acid at the air-liquid interface at atmospherically relevant ozone concentrations to investigate if the viscosity of the sub-phase influences the rate of the reaction and to determine the effect of the presence of a second component within the monolayer, stearic acid, which is generally considered to be non-reactive towards ozone, on the reaction kinetics as determined by neutron reflectometry measurements. Atmospheric aerosol can be extremely viscous. The kinetics of the reaction were found to be independent of the viscosity of the sub-phase below the monolayer over a range of moderate viscosities, , demonstrating no involvement of aqueous sub-phase oxidants in the rate determining step. The kinetics of oxidation of monolayers of pure oleic acid were found to depend on the surface coverage with different behaviour observed above and below a surface coverage of oleic acid of ∼1 × 1018 molecule m-2. Atmospheric aerosol are typically complex mixtures, and the presence of an additional compound in the monolayer that is inert to direct ozone oxidation, stearic acid, did not significantly change the reaction kinetics. It is demonstrated that oleic acid monolayers at the air-water interface do not leave any detectable material at the air-water interface, contradicting the previous work published in this journal which the authors now believe to be erroneous. The combined results presented here indicate that the kinetics, and thus the atmospheric chemical lifetime for unsaturated surface active materials at the air-water interface to loss by reaction with gas-phase ozone, can be considered to be independent of other materials present at either the air-water interface or in the aqueous sub-phase.

Night-Time Oxidation of a Monolayer Model for the Air–Water Interface of Marine Aerosols—A Study by Simultaneous Neutron Reflectometry and in Situ Infra-Red Reflection Absorption Spectroscopy (IRRAS)

This paper describes experiments on the ageing of a monolayer model for the air–water interface of marine aerosols composed of a typical glycolipid, galactocerebroside (GCB). Lipopolysaccharides have been observed in marine aerosols, and GCB is used as a proxy for these more complex lipopolysaccharides. GCB monolayers are investigated as pure films, as mixed films with palmitic acid, which is abundant in marine aerosols and forms a stable attractively mixed film with GCB, particularly with divalent salts present in the subphase, and as mixed films with palmitoleic acid, an unsaturated analogue of palmitic acid. Such mixed films are more realistic models of atmospheric aerosols than simpler single-component systems. Neutron reflectometry (NR) has been combined in situ with Fourier transform infra-red reflection absorption spectroscopy (IRRAS) in a pioneering analysis and reaction setup designed by us specifically to study mixed organic monolayers at the air–water interface. The two techniques in combination allow for more sophisticated observation of multi-component monolayers than has previously been possible. The structure at the air–water interface was also investigated by complementary Brewster angle microscopy (BAM). This study looks specifically at the oxidation of the organic films by nitrate radicals (NO3•), the key atmospheric oxidant present at night. We conclude that NO3• oxidation cannot fully remove a cerebroside monolayer from the surface on atmospherically relevant timescales, leaving its saturated tail at the interface. This is true for pure and salt water subphases, as well as for single- and two-component films. The behaviour of the unsaturated tail section of the molecule is more variable and is affected by interactions with co-deposited species. Most surprisingly, we found that the presence of CaCl2 in the subphase extends the lifetime of the unsaturated tail substantially—a new explanation for longer residence times of materials in the atmosphere compared to lifetimes based on laboratory studies of simplified model systems. It is thus likely that aerosols produced from the sea-surface microlayer at night will remain covered in surfactant molecules on atmospherically relevant timescales with impact on the droplet’s surface tension and on the transport of chemical species across the air–water interface.