Effects of the Grazing Incidence Geometry on X-ray Photon Correlation Spectroscopy Measurements.

X-ray photon correlation spectroscopy (XPCS) is a versatile tool to measure dynamics on the nanometer to micrometer scale in bulk samples. XPCS has also been applied in grazing incidence (GI) geometry to examine the dynamics of surface layers. However, considering GI scattering experiments more universally, the GI geometry leads to a superposition of signals due to reflection and refraction effects, also known from the distorted-wave Born approximation (DWBA). In this paper, the impact of these reflection and refraction effects on the correlation analysis is determined experimentally by measuring grazing incidence transmission XPCS (GT-XPCS) and grazing incidence XPCS (GI-XPCS) simultaneously for a thin film sample, showing non-equilibrium dynamics. The results of the GI and GT geometry comparisons are combined within the framework of the standardly applied, simplified DWBA. These calculations allow identifying the main contributions of the detected signal from the leading scattering terms along the out-of-plane direction qz, which dominate the measured intensity pattern on the detector. In combination with the calculation of the non-linear effect of refraction in GTSAXS and GISAXS, it is possible to identify experimental conditions that can be chosen to run experiments and data analysis as close as possible to transmission XPCS and to explain which limitations for data interpretations are observed. Consequently, the beam exposure can be significantly reduced by using GI geometry only. Calculations of experimental settings prior to experiments are detailed to determine suitable qz regions for a variety of material systems measured in bulk-sensitive GI-XPCS experiments, allowing us to determine the scaling behavior of typical decay times as a function of q that is comparable to the scaling behavior obtained in distortion-free GT-XPCS or transmission XPCS experiments.

Sizes of pure and doped helium droplets from single shot x-ray imaging.

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5 µm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9 K. This work extends the measurement of large helium nanodroplets containing 109-1011 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.

Xi-cam: a versatile interface for data visualization and analysis.

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

Biomimetic self-templating supramolecular structures.

In nature, helical macromolecules such as collagen, chitin and cellulose are critical to the morphogenesis and functionality of various hierarchically structured materials. During tissue formation, these chiral macromolecules are secreted and undergo self-templating assembly, a process whereby multiple kinetic factors influence the assembly of the incoming building blocks to produce non-equilibrium structures. A single macromolecule can form diverse functional structures when self-templated under different conditions. Collagen type I, for instance, forms transparent corneal tissues from orthogonally aligned nematic fibres, distinctively coloured skin tissues from cholesteric phase fibre bundles, and mineralized tissues from hierarchically organized fibres. Nature's self-templated materials surpass the functional and structural complexity achievable by current top-down and bottom-up fabrication methods. However, self-templating has not been thoroughly explored for engineering synthetic materials. Here we demonstrate the biomimetic, self-templating assembly of chiral colloidal particles (M13 phage) into functional materials. A single-step process produces long-range-ordered, supramolecular films showing multiple levels of hierarchical organization and helical twist. Three distinct supramolecular structures are created by this approach: nematic orthogonal twists, cholesteric helical ribbons and smectic helicolidal nanofilaments. Both chiral liquid crystalline phase transitions and competing interfacial forces at the interface are found to be critical factors in determining the morphology of the templated structures during assembly. The resulting materials show distinctive optical and photonic properties, functioning as chiral reflector/filters and structural colour matrices. In addition, M13 phages with genetically incorporated bioactive peptide ligands direct both soft and hard tissue growth in a hierarchically organized manner. Our assembly approach provides insight into the complexities of hierarchical assembly in nature and could be expanded to other chiral molecules to engineer sophisticated functional helical-twisted structures.

Resonant Carbon K-Edge Soft X-Ray Scattering from Lattice-Free Heliconical Molecular Ordering: Soft Dilative Elasticity of the Twist-Bend Liquid Crystal Phase.

Resonant x-ray scattering shows that the bulk structure of the twist-bend liquid crystal phase, recently discovered in bent molecular dimers, has spatial periodicity without electron density modulation, indicating a lattice-free heliconical nematic precession of orientation that has helical glide symmetry. In situ study of the bulk helix texture of the dimer CB7CB shows an elastically confined temperature-dependent minimum helix pitch, but a remarkable elastic softness of pitch in response to dilative stresses. Scattering from the helix is not detectable in the higher temperature nematic phase.

Probing and controlling liquid crystal helical nanofilaments.

We report the first in situ measurement of the helical pitch of the helical nanofilament B4 phase of bent-core liquid crystals using linearly polarized, resonant soft X-ray scattering at the carbon K-edge. A strong, anisotropic scattering peak corresponding to the half-pitch of the twisted smectic layer structure was observed. The equilibrium helical half-pitch of NOBOW is found to be 120 nm, essentially independent of temperature. However, the helical pitch can be tuned by mixing guest organic molecules with the bent-core host, followed by thermal annealing.

Mesoscopic constructs of ordered and oriented metal-organic frameworks on plasmonic silver nanocrystals.

We enclose octahedral silver nanocrystals (Ag NCs) in metal-organic frameworks (MOFs) to make mesoscopic constructs O(h)-nano-Ag⊂MOF in which the interface between the Ag and the MOF is pristine and the MOF is ordered (crystalline) and oriented on the Ag NCs. This is achieved by atomic layer deposition of aluminum oxide on Ag NCs and addition of a tetra-topic porphyrin-based linker, 4,4',4″,4‴-(porphyrin-5,10,15,20-tetrayl)tetrabenzoic acid (H4TCPP), to react with alumina and make MOF [Al2(OH)2TCPP] enclosures around Ag NCs. Alumina thickness is precisely controlled from 0.1 to 3 nm, thus allowing control of the MOF thickness from 10 to 50 nm. Electron microscopy and grazing angle X-ray diffraction confirm the order and orientation of the MOF by virtue of the porphyrin units being perpendicular to the planes of the Ag. We use surface-enhanced Raman spectroscopy to directly track the metalation process on the porphyrin and map the distribution of the metalated and unmetalated linkers on a single-nanoparticle level.

Conjugated block copolymer photovoltaics with near 3% efficiency through microphase separation.

Organic electronic materials have the potential to impact almost every aspect of modern life including how we access information, light our homes, and power personal electronics. Nevertheless, weak intermolecular interactions and disorder at junctions of different organic materials limit the performance and stability of organic interfaces and hence the applicability of organic semiconductors to electronic devices. Here, we demonstrate control of donor-acceptor heterojunctions through microphase-separated conjugated block copolymers. When utilized as the active layer of photovoltaic cells, block copolymer-based devices demonstrate efficient photoconversion well beyond devices composed of homopolymer blends. The 3% block copolymer device efficiencies are achieved without the use of a fullerene acceptor. X-ray scattering results reveal that the remarkable performance of block copolymer solar cells is due to self-assembly into mesoscale lamellar morphologies with primarily face-on crystallite orientations. Conjugated block copolymers thus provide a pathway to enhance performance in excitonic solar cells through control of donor-acceptor interfaces.

DLSIA: Deep Learning for Scientific Image Analysis.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges. MLExchange is a Machine Learning framework deploying interactive web interfaces to enhance and accelerate data analysis. The platform allows users to easily upload, visualize, label, and train networks. The resulting models can be deployed on real data while both results and models could be shared with the scientists. The MLExchange web-based application for image segmentation allows for training, testing, and evaluating multiple machine learning models on hand-labeled tomography data. This environment provides users with an intuitive interface for segmenting images using a variety of machine learning algorithms and deep-learning neural networks. Additionally, these tools have the potential to overcome limitations in traditional image segmentation techniques, particularly for complex and low-contrast images.

Structure determination of Pt-coated Au dumbbells via fluctuation X-ray scattering.

A fluctuation X-ray scattering experiment has been carried out on platinum-coated gold nanoparticles randomly oriented on a substrate. A complete algorithm for determining the electron density of an individual particle from diffraction patterns of many particles randomly oriented about a single axis is demonstrated. This algorithm operates on angular correlations among the measured intensity distributions and recovers the angular correlation functions of a single particle from measured diffraction patterns. Taking advantage of the cylindrical symmetry of the nanoparticles, a cylindrical slice model is proposed to reconstruct the structure of the nanoparticles by fitting the experimental ring angular auto-correlation and small-angle scattering data obtained from many scattering patterns. The physical meaning of the refined structure is discussed in terms of their statistical distributions of the shape and electron density profile.

Collective osmotic shock in ordered materials.

Osmotic shock in a vesicle or cell is the stress build-up and subsequent rupture of the phospholipid membrane that occurs when a relatively high concentration of salt is unable to cross the membrane and instead an inflow of water alleviates the salt concentration gradient. This is a well-known failure mechanism for cells and vesicles (for example, hypotonic shock) and metal alloys (for example, hydrogen embrittlement). We propose the concept of collective osmotic shock, whereby a coordinated explosive fracture resulting from multiplexing the singular effects of osmotic shock at discrete sites within an ordered material results in regular bicontinuous structures. The concept is demonstrated here using self-assembled block copolymer micelles, yet it is applicable to organized heterogeneous materials where a minority component can be selectively degraded and solvated whilst ensconced in a matrix capable of plastic deformation. We discuss the application of these self-supported, perforated multilayer materials in photonics, nanofiltration and optoelectronics.

Defining the nanostructured morphology of triblock copolymers using resonant soft X-ray scattering.

The morphologies of a poly(1,4-isoprene)-block-polystyrene-block-poly(2-vinyl pyridine) (IS2VP) copolymer were investigated using resonant soft X-ray scattering (RSoXS) together with scanning force microscopy, small-angle X-ray scattering, and electron microscopy. Differences in the nanoscopic morphologies in the bulk and thin film samples were observed arising from the competition between segmental interactions between the blocks and the substrate and the surface energies of each block. Using soft X-rays at selected photon energies to isolate the scattering contribution from different polymer blocks, RSoXS unambiguously defined the complex morphology of the triblock copolymer. In the bulk sample, two nested, hexagonal arrays of P2VP and PI cylindrical microdomains residing in the PS matrix were observed. The cylindrical microdomains of one component were found to be located at the interstitial sites of the hexagonal array of the other component that has the larger d spacing. In solvent-annealed thin films with 40 nm in thickness, a hexagonal array of core-shell microdomains of P2VP cores with PS shells that reside in a PI matrix were observed.

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering.

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.

Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios-users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.

Electrophoresis Assisted Printing: A Method To Control the Morphology in Organic Thin Films.

A major advantage of organic solar cells (OSC) is the processability out of solution allowing for advanced printing methods toward large-scale production. Controlling the blend morphology of solution coated active layers is a key challenge to optimize their power conversion efficiency. We have derived a printing procedure from an industrial coating process that facilitates tuning the nanomorphology of a blend of poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) as model system for OSCs. Applying an electric field during printing and the film drying process modifies the vertical film composition of the photoactive layer and optimizes the polymer crystal orientation. The choice of chloroform as solvent allows us to obtain material transport within the wet film, due to an induced electrophoretic mobility. Tailoring the morphology improves the power conversion efficiency of the OSCs by up to 25%. Our findings indicate that electrophoresis assisted printing provides an efficient approach to optimize the active layer for various material and solvent combinations that exhibit an electrophoretic mobility.

Preferred crystallographic orientation of cellulose in plant primary cell walls.

Cellulose, the most abundant biopolymer on earth, is a versatile, energy rich material found in the cell walls of plants, bacteria, algae, and tunicates. It is well established that cellulose is crystalline, although the orientational order of cellulose crystallites normal to the plane of the cell wall has not been characterized. A preferred orientational alignment of cellulose crystals could be an important determinant of the mechanical properties of the cell wall and of cellulose-cellulose and cellulose-matrix interactions. Here, the crystalline structures of cellulose in primary cell walls of onion (Allium cepa), the model eudicot Arabidopsis (Arabidopsis thaliana), and moss (Physcomitrella patens) were examined through grazing incidence wide angle X-ray scattering (GIWAXS). We find that GIWAXS can decouple diffraction from cellulose and epicuticular wax crystals in cell walls. Pole figures constructed from a combination of GIWAXS and X-ray rocking scans reveal that cellulose crystals have a preferred crystallographic orientation with the (200) and (110)/([Formula: see text]) planes preferentially stacked parallel to the cell wall. This orientational ordering of cellulose crystals, termed texturing in materials science, represents a previously unreported measure of cellulose organization and contradicts the predominant hypothesis of twisting of microfibrils in plant primary cell walls.

Interfacial widths of conjugated polymer bilayers.

The interfaces of conjugated polyelectrolyte (CPE)/poly[2-methoxy-5-(2'-ethylhexyloxy)-p-phenylene vinylene] (MEH-PPV) bilayers cast from differential solvents are shown by resonant soft X-ray reflectivity (RSoXR) to be very smooth and sharp. The chemical interdiffusion due to casting is limited to less than 0.6 nm, and the interface created is thus nearly "molecularly" sharp. These results demonstrate for the first time and with high precision that the nonpolar MEH-PPV layer is not much disturbed by casting the CPE layer from a polar solvent. A baseline is established for understanding the role of interfacial structure in determining the performance of CPE-based polymer light-emitting diodes. More broadly, we anticipate further applications of RSoXR as an important tool in achieving a deeper understanding of other multilayer organic optoelectronic devices, including multilayer photovoltaic devices.

Multiresolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography.

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multiresolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training data set by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts when compared to ab initio quantum chemistry methods, with the highest accuracy found for 1H chemical shifts that is comparable to the error between the ab initio results and experimental measurements. Principal component analysis (PCA) is used to both understand these greatly improved predictions for 1H , as well as indicating that chemical shift prediction for 13C, 15N, and 17O, which have far fewer training environments than the 1H atom type, will improve once more unique training samples are made available to exploit the deep network architecture.

Self-consistent field theory simulations of block copolymer assembly on a sphere.

Recently there has been a strong interest in the area of defect formation in ordered structures on curved surfaces. Here we explore the closely related topic of self-assembly in thin block copolymer melt films confined to the surface of a sphere. Our study is based on a self-consistent field theory (SCFT) model of block copolymers that is numerically simulated by spectral collocation with a spherical harmonic basis and an extension of the Rasmussen-Kalosakas operator splitting algorithm [J. Polym. Sci. Part B: Polym. Phys. 40, 1777 (2002)]. In this model, we assume that the composition of the thin block copolymer film varies only in longitude and colatitude and is constant in the radial direction. Using this approach we are able to study the formation of defects in the lamellar and cylindrical phases, and their dependence on sphere radius. Specifically, we compute ground-state (i.e., lowest-energy) configurations on the sphere for both the cylindrical and lamellar phases. Grain boundary scars are also observed in our simulations of the cylindrical phase when the sphere radius surpasses a threshold value R_{c} approximately 5d , where d is the natural lattice spacing of the cylindrical phase, which is consistent with theoretical predictions [Bowick, Phys. Rev. B 62, 8738 (2000); Bausch, Science 299, 1716 (2003)]. A strong segregation limit approximate free energy is also presented, along with simple microdomain packing arguments, to shed light on the observed SCFT simulation results.