Neutron Reflectometry Data Research Articles

Studying Transient Phenomena in Thin Films with Reinforcement Learning.

Neutron reflectometry has long been a powerful tool to study the interfacial properties of energy materials. Recently, time-resolved neutron reflectometry has been used to better understand transient phenomena in electrochemical systems. Those measurements often comprise a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information content compared to a typical steady-state measurement. In this work, we present an approach that leverages existing reinforcement learning tools to model time-resolved data to extract the time evolution of structure parameters. By mapping the reflectivity curves taken at different times as individual states, we use the Soft Actor-Critic algorithm to optimize the time series of structure parameters that best represent the evolution of an electrochemical system. We show that this approach constitutes an elegant solution to the modeling of time-resolved neutron reflectometry data.

In Situ Measurement of Buried Electrolyte–Electrode Interfaces for Solid State Batteries with Nanometer Level Precision

Many technologies including high-energy solid-state batteries rely on high quality solid–solid interfaces. Solid–solid interfaces are particularly difficult to study as their nature requires the interface to be buried. In this work we demonstrate the use of a combination of neutron reflectometry and in situ electrochemistry to measure the interface between Li metal and the solid electrolyte LiPON across an 8 cm2 area. Neutron reflectometry measurements confirm the interphase to be less than 7 nm thick. The neutron reflectometry data further emphasize that the interphase that forms is a chemical gradient consisting of a Li-rich layer that gradually decreases in Li content until it blends into pure LiPON. Experimental confirmation that we can make ideal solid–solid interphases less than 10 nm thick will help facilitate the adoption of high efficiency next generation solid state batteries. Further this combination of complementary techniques provides a more general methodology for studying buried solid–solid interfaces across applications.

Neutral polymers partial exclusion in ionic solutions near charged membranes.

Neutral polymers have become a useful tool in biophysics. They are currently used in a variety of single-channel conductance measurements, to change solution conductivity and viscosity, to induce osmotic stress in polymer-excluded regions, or simply as crowding agents. Many experiments involve the proximity of neutral polymers to charged membranes or their confinement in aqueous pores formed by protein channels with charged residues. A well-known phenomenon relevant to this problem is the polarization of a neutral particle with dielectric properties different from the surrounding medium under the effect of an electric field. This polarization causes a dielectrophoretic force when inhomogeneous electric fields act on the particle and it has been extensively used in many biomedical applications involving particle trapping and cell separation, among others. In addition, whenever a neutral particle perturbs the equilibrium electric double layer near a charged surface, the osmotic pressure gradient acts as a net repulsive force that pushes the particle away from that surface. Both the dielectrophoretic force and the hydrostatic force contribute to exclude a neutral particle from a charged surface, provided the polarizability is lower in the particle than in the solution. Here we report analytical predictions and simulations of the exclusion of polyethylene glycol (PEG) molecules of different molecular weight from charged surfaces. Molecular dynamics of PEG near charged DPPS membranes and neutral DPPC membranes show a region of PEG exclusion near the membrane-solution interface for charged membranes. The simulations also point to a preferential alignment of PEG molecules near the charged membrane. The simulations are confirmed by preliminary neutron reflectometry data, which are consistent with an exclusion region of concentrated PEG solutions near the highly charged silicon dioxide/water interface.

The lithiation onset of amorphous silicon thin-film electrodes

The lithiation onset of amorphous silicon (a-silicon) films up to 10% state of charge (SOC) is characterized by a Li+-uptake region around 0.5 V vs a Li reference electrode. In the literature, this is commonly attributed to surface processes such as the formation of a solid electrolyte interphase layer and/or the reduction of the surface native oxide, and more seldom to bulk processes such as reduction of oxygen contaminations inside the silicon film and to silicon lithiation. This work presents evidence that this process is associated with the lithiation of elemental silicon using electrochemical and non-electrochemical measurements and including a discussion of literature data. Cyclic voltammetry performed on pre-lithiated a-silicon and a-silicon films with different oxygen concentrations and with different film thicknesses reveals a lithiation process that is not associated with the reduction of oxygen. Elemental depth-profiling with secondary ion mass spectrometry (SIMS) reveals a two-phase lithiation mechanism at the lithiation onset, which sharply delimits non-lithiated silicon from a Li-poor phase of constant Li content (Li0.3Si). The published operando neutron reflectometry data suggest that this is also the case for oxygen-free Si single crystal wafers. SIMS measurements further show that the whole a-silicon thin film is converted into the Li-poor phase. This phase remains stable in the vicinity of the current collector even at 100% SOC, indicating that mechanical stress may play an important role.

GenX 3: the latest generation of an established tool.

Since its publication more than 15 years ago the GenX software has been continuously developed and has established itself as a standard package for analyzing X-ray and neutron reflectometry data. The evolution of the software during the last two major revisions is reported here. This includes a simplified model builder for beginners, simple samples, additional sample models, statistical error analysis and the use of just-in-time compilation modules for the reflectometry kernel to achieve higher performance. In addition, the influence of experimental errors on the reflectivity curve is discussed, and new features are described that allow the user to include these in the error statistics to improve the fitting and uncertainty estimation.

Impact of the multiscale viscoelasticity of quasi-2D self-assembled protein networks on stem cell expansion at liquid interfaces

Although not typically thought to sustain cell adhesion and expansion, liquid substrates have recently been shown to support such phenotypes, providing protein nanosheets could be assembled at corresponding liquid-liquid interfaces. However, the precise mechanical properties required from such quasi-2D nanoassemblies and how these correlate with molecular structure and nanoscale architecture has remained unclear. In this report, we screen a broad range of surfactants, proteins, oils and cell types and correlate interfacial mechanical properties with stem cell expansion. Correlations suggest an impact of interfacial viscoelasticity on the regulation of such behaviour. We combine interfacial rheology and magnetic tweezer-based interfacial microrheology to characterise the viscoelastic profile of protein nanosheets assembled at liquid-liquid interfaces. Based on neutron reflectometry and transmission electron microscopy data, we propose that the amorphous nanoarchitecture of quasi-2D protein nanosheets controls their multi-scale viscoelasticity which, in turn, correlates with cell expansion. This understanding paves the way for the rational design of protein nanosheets for microdroplet and bioemulsion-based stem cell manufacturing and screening platforms.

Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge for parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator–ferromagnetic insulator heterostructure Bi2Se3/EuS exhibiting proximity magnetism in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator–antiferromagnet heterostructure (Bi,Sb)2Te3/Cr2O3 and identify possible interfacial proximity magnetism in this material. We anticipate that the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.

Using refnx to Model Neutron Reflectometry Data from Phospholipid Bilayers.

Neutron reflectometry has emerged as a powerful method for studying the structure of thin films in contact with solution at sub-molecular spatial resolution (Penfold and Thomas, J Phys Condens Matter 2:1369-1412, 1990). This type of experiment is undertaken at large international central facilities and experience in data analysis and interpretation is not always available "locally". Here, we describe the application of the refnx software suite (Nelson and Prescott, J Appl Crystallogr 52:193-200, 2019) to the analysis of a single phospholipid bilayer deposited at a silicon/buffer interface. The data is modeled such that the fitted parameters are readily interpretable by researchers working with lipid bilayers.

Anaklasis: a compact software package for model-based analysis of specular neutron and X-ray reflectometry data sets.

anaklasis constitutes a set of open-source Python scripts that facilitate a range of specular neutron and X-ray reflectivity calculations, involving the generation of theoretical curves and the comparison/fitting of interfacial model reflectivity against experimental data sets. The primary focus of the software is twofold: on one hand to offer a more natural framework for model definition, requiring minimum coding literacy, and on the other hand to include advanced analysis methods that have been proposed in recent work. Particular attention is given to the ability to co-refine reflectivity data and to the estimation of model-parameter uncertainty and covariance using bootstrap analysis and Markov chain Monte Carlo sampling. The compactness and simplicity of model definition together with the streamlined analysis do not present a steep learning curve for the user, an aspect that may accelerate the generation of reproducible, easily readable and statistically accurate reports in future neutron and X-ray reflectivity related literature.

Operando Characterization of the Solid Electrolyte Interphase: A Means to Validate Detailed Chemical Kinetic Modeling

The solid electrolyte interphase (SEI) is a layer that forms at the anode-electrolyte interface in lithium-ion batteries. The layer forms due to voltage instability of the electrolyte at low anode potentials but serves to passivate the electrolyte to protect against further uncontrolled decomposition. In theory, the SEI is self-limiting, but in reality, continued growth over the battery’s lifetime leads to capacity fade, poor rate capability, and eventually cell death. Though significant progress in recent years has improved the SEI’s function and stability, poor understanding of its most basic chemistry impedes “rational design” of SEI layers for advanced battery applications. Understanding and quantifying the elementary chemistry of the SEI is made challenging by the layer’s thickness (4 to ca. 100 nm), chemical sensitivity, mechanical fragility, and complex chemistry (upwards of 100 reactions have been proposed). These factors combine to make modeling the SEI's fundamental growth and evolution chemistry a significant challenge. Both the computational tools to model the SEI chemistry and the experimental data required to validate such models are all too rare.This talk will present two operando measurements of the SEI grown on a non-intercalating thin film tungsten anode: depth profiling via neutron reflectometry (NR) and SEI mass uptake data via quartz crystal microbalance with dissipation monitoring (QCM-D) taken during cyclic voltammetry cycling (Figure 1).[1,2] NR results directly observe the hypothesized two-layer SEI structure, with a thin, dense inorganic “inner” layer close to the anode and a thicker, porous organic “outer” layer closer to the electrolyte. QCM-D results show the dynamic evolution of the SEI, with heavier SEI products formed at higher potentials, followed by reactions forming lighter SEI products at lower potentials. Both techniques observe “SEI breathing,” whereby soluble SEI products exit the SEI during sweeps to more positive potentials. Interrogation of the results provides new insight into the detailed chemistry of SEI formation and evolution and a platform for validating numerical simulations of the detailed SEI chemistry. Such detailed models can identify prominent reaction pathways and key chemical species present in the SEI which, in turn, can help guide the design of thermally, mechanically, and chemically stable SEI layers for advanced batteries.[1] Rus, E.D., Dura, J.A., “In situ neutron reflectometry study of solid electrolyte interface (SEI) formation on tungsten thin-film electrodes.” ACS Appl. Mat. Int., 11(50), 2019, p. 47553—47563.[2] Lee, C.H, Dura, J.A., LeBar, A., DeCaluwe, S.C., “Direct, operando observation of the bilayer solid electrolyte interphase structure: Electrolyte reduction on a non-intercalating electrode.” J. Power Sources, 412, 2019, p. 725—735. Figure 1

Machine learning for neutron reflectometry data analysis of two-layer thin films * *Notice of Copyright: This manuscript has been authored by UT-Battelle, LLC under Contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish

Neutron reflectometry (NR) is a powerful tool for probing thin films at length scales down to nanometers. We investigated the use of a neural network to predict a two-layer thin film structure to model a given measured reflectivity curve. Application of this neural network to predict a thin film structure revealed that it was accurate and could provide an excellent starting point for traditional fitting methods. Employing prediction-guided fitting has considerable potential for more rapidly producing a result compared to the labor-intensive but commonly-used approach of trial and error searches prior to refinement. A deeper look at the stability of the predictive power of the neural network against statistical fluctuations of measured reflectivity profiles showed that the predictions are stable. We conclude that the approach presented here can provide valuable assistance to users of NR and should be further extended for use in studies of more complex n-layer thin film systems. This result also opens up the possibility of developing adaptive measurement systems in the future.

Architecture of Hydrated Multilayer Poly(methacrylic acid) Hydrogels: The Effect of Solution pH

We report on the evolution of the internal structure of dry and hydrated poly(methacrylic acid) (PMAA) hydrogels by quantifying the extent of layer interdiffusion in hydrogen-bonded (HB) films and upon subsequent cross-linking and hydration. These hydrogels are produced by ethylenediamine (EDA)-assisted cross-linking of PMAA in spin-assisted (SA) and dipped HB PMAA/poly(N-vinylpyrrolidone) (PVPON) multilayers followed by complete release of PVPON at pH 8 due to severing of hydrogen bonds with the PMAA network. Internal hydrogel architecture was monitored by neutron reflectometry using deuterated dPMAA marker layers. We found that even in the highly stratified SA HB films, layer interdiffusion extends over three (PMAA/PVPON) bilayers. Cross-linking of this film induces marker layer interpenetration more deeply into the surrounding material, extending over five layers. The volume fraction of dPMAA at the nominal center of a marker layer decreased from 0.65 to 0.51 after cross-linking. Hydrated SA hydrogels preserve well-organized layering and exhibit a persistent differential swelling with two distinct swelling ratios corresponding to MAA cross-link-rich and cross-link-poor strata. In contrast, layer organization in dipped films decays rapidly with distance from the silicon substrate. Both types of hydrogel swelled by factors of two and four times their dry total thicknesses at pH 5 and 7, respectively, and exhibited elevated surface roughness upon hydration. To fit the neutron reflectometry data, a self-consistent model was developed wherein the amount of PMAA initially deposited was preserved through subsequent chemical modification and hydration. Our results open opportunities for the development of thin hydrogels with a regulated structure, which can be utilized for efficient sensing, protection, activation, and rapid response in an aqueous environment. The internal morphological hierarchy of these multilayer hydrogels affords a means of fine-tuning their response to pH, temperature, or light to a degree rarely possible for randomly cross-linked responsive networks or brushes.

Transition in a Magnetic Non-Collinear Spin-Flop State in a Fe/Pd/Gd/Pd Superlattice

The influence of a Pd spacer on the magnetic properties of Gd/Fe heterostructures has been studied. We have prepared using ultrahigh vacuum sputtering multilayered [Fe(3.5 nm)/Pd(1.2 nm)/Gd(5.0 nm)/Pd(1.2 nm)]12 structure. By using Polarized Neutron Reflectometry at temperature of 10 K we have observed strong increase in the intensity of neutron spin-flip scattering above magnetic field of 1.5 kOe. Combined analysis of magnetometry and polarized neutron reflectometry data allowed us to find out that the observed increase in spin-flip scattering is related to the spin-flop transition. Although the spin-flop transition were already seen in Fe/Gd systems, the use of Pd in our case allowed the suppression of the magnetic field of the transition making thus possible the use of Fe/Pd/Gd systems for diverse spintronic applications.

Refnx: neutron and X-ray reflectometry analysis in Python.

refnx is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform and has been tested on Linux, macOS and Windows. Its graphical user interface is browser based, through a Jupyter notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterized in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule etc.). The model and data are used to create an objective, which is used to calculate the residuals, log-likelihood and log-prior probabilities of the system. Objectives are combined to perform co-refinement of multiple data sets and mixed-area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. The software offers a choice of fitting approaches, including least-squares (global and gradient-based optimizers) and a Bayesian approach using a Markov-chain Monte Carlo algorithm to investigate the posterior distribution of the model parameters. The Bayesian approach is useful for examining parameter covariances, model selection and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in Jupyter notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses.

Unveiling structural, chemical and magnetic interfacial peculiarities in \u03b5-Fe2O3/GaN (0001) epitaxial films

The metastable ε-Fe2O3 is known to be the most intriguing ferrimagnetic and multiferroic iron oxide phase exhibiting a bunch of exciting physical properties both below and above room temperature. The present paper unveils the structural and magnetic peculiarities of a few nm thick interface layer discovered in these films by a number of techniques. The polarized neutron reflectometry data suggests that the interface layer resembles GaFeO3 in composition and density and is magnetically softer than the rest of the ε-Fe2O3 film. While the in-depth density variation is in agreement with the transmission electron microscopy measurements, the layer-resolved magnetization profiles are qualitatively consistent with the unusual wasp-waist magnetization curves observed by superconducting quantum interference device magnetometry. Interestingly a noticeable Ga diffusion into the ε-Fe2O3 films has been detected by secondary ion mass spectroscopy providing a clue to the mechanisms guiding the nucleation of exotic metastable epsilon ferrite phase on GaN at high growth temperature and influencing the interfacial properties of the studied films.

Self-Assembled Layering of Magnetic Nanoparticles in a Ferrofluid on Silicon Surfaces.

This article describes the three-dimensional self-assembly of monodisperse colloidal magnetite nanoparticles (NPs) from a dilute water-based ferrofluid onto a silicon surface and the dependence of the resultant magnetic structure on the applied field. The NPs assemble into close-packed layers on the surface followed by more loosely packed ones. The magnetic field-dependent magnetization of the individual NP layers depends on both the rotational freedom of the layer and the magnetization of the adjacent layers. For layers in which the NPs are more free to rotate, the easy axis of the NP can readily orient along the field direction. In more dense packing, free rotation of the NPs is hampered, and the NP ensembles likely build up quasi-domain states to minimize energy, which leads to lower magnetization in those layers. Detailed analysis of polarized neutron reflectometry data together with model calculations of the arrangement of the NPs within the layers and input from small-angle scattering measurements provide full characterization of the core/shell NP dimensions, degree of chaining, arrangement of the NPs within the different layers, and magnetization depth profile.

Spin Solid versus Magnetic Charge Ordered State in Artificial Honeycomb Lattice of Connected Elements.

The nature of magnetic correlation at low temperature in two‐dimensional artificial magnetic honeycomb lattice is a strongly debated issue. While theoretical researches suggest that the system will develop a novel zero entropy spin solid state as T → 0 K, a confirmation to this effect in artificial honeycomb lattice of connected elements is lacking. This study reports on the investigation of magnetic correlation in newly designed artificial permalloy honeycomb lattice of ultrasmall elements, with a typical length of ≈12 nm, using neutron scattering measurements and temperature‐dependent micromagnetic simulations. Numerical modeling of the polarized neutron reflectometry data elucidates the temperature‐dependent evolution of spin correlation in this system. As temperature reduces to ≈7 K, the system tends to develop novel spin solid state, manifested by the alternating distribution of magnetic vortex loops of opposite chiralities. Experimental results are complemented by temperature‐dependent micromagnetic simulations that confirm the dominance of spin solid state over local magnetic charge ordered state in the artificial honeycomb lattice with connected elements. These results enable a direct investigation of novel spin solid correlation in the connected honeycomb geometry of 2D artificial structure.

Structure analysis of aqueous ferrofluids at interface with silicon: neutron reflectometry data

Adsorption of nanoparticles from aqueous ferrofluids (FFs) on solid surface (crystalline silicon) was studied by neutron reflectometry (NR). Two kinds of FFs were considered. First kind was heavy water-based ferrofluids with magnetite nanoparticles coated by double layer of sodium oleate. Second one FF was cobalt ferrite nanoparticles stabilized by lauric acid/sodium n-dodecylsulphate layer and dispersed in water. It was obtained only a single adsorption layer for two types of ferrofluids. The impact of the magnetic nanoparticles concentration and geometry was considered in frame of the adsorption characteristic of FFs.

Mechanism of Action of a Membrane-Active Quinoline-Based Antimicrobial on Natural and Model Bacterial Membranes.

HT61 is a quinoline-derived antimicrobial, which exhibits bactericidal potency against both multiplying and quiescent methicillin resistant and sensitive Staphylococcus aureus, and has been proposed as an adjunct for other antimicrobials to extend their usefulness in the face of increasing antimicrobial resistance. In this study, we have examined HT61's effect on the permeability of S. aureus membranes and whether this putative activity can be attributed to an interaction with lipid bilayers. Using membrane potential and ATP release assays, we have shown that HT61 disrupts the membrane enough to result in depolarization of the membrane and release of intercellular constituents at concentrations above and below the minimum inhibitory concentration of the drug. Utilizing both monolayer subphase injection and neutron reflectometry, we have shown that increasing the anionic lipid content of the membrane leads to a more marked effect of the drug. In bilayers containing 25 mol % phosphatidylglycerol, neutron reflectometry data suggest that exposure to HT61 increases the level of solvent in the hydrophobic region of the membrane, which is indicative of gross structural damage. Increasing the proportion of PG elicits a concomitant level of membrane damage, resulting in almost total destruction when 75 mol % phosphatidylglycerol is present. We therefore propose that HT61's primary action is directed toward the cytoplasmic membrane of Gram-positive bacteria.

Evidence of the Key Role of H3O+ in Phospholipid Membrane Morphology.

This study explains the importance of the phosphate moiety and H3O+ in controlling the ionic flux through phospholipid membranes. We show that despite an increase in the H3O+ concentration when the pH is decreased, the level of ionic conduction through phospholipid bilayers is reduced. By modifying the lipid structure, we show the dominant determinant of membrane conduction is the hydrogen bonding between the phosphate oxygens on adjacent phospholipids. The modulation of conduction with pH is proposed to arise from the varying H3O+ concentrations altering the molecular area per lipid and modifying the geometry of conductive defects already present in the membrane. Given the geometrical constraints that control the lipid phase structure of membranes, these area changes predict that organisms evolving in environments with different pHs will select for different phospholipid chain lengths, as is found for organisms near highly acidic volcanic vents (short chains) or in highly alkaline salt lakes (long chains). The stabilizing effect of the hydration shells around phosphate groups also accounts for the prevalence of phospholipids across biology. Measurement of ion permeation through lipid bilayers was made tractable using sparsely tethered bilayer lipid membranes with swept frequency electrical impedance spectroscopy and ramped dc amperometry. Additional evidence of the effect of a change in pH on lipid packing density is obtained from neutron reflectometry data of tethered membranes containing perdeuterated lipids.