Multislice forward modeling of Coherent Surface Scattering Imaging on surface and interfacial structures

To study nanostructures on substrates, surface-sensitive reflection-geometry scattering techniques such as grazing incident small angle x-ray scattering are commonly used to yield an averaged statistical structural information of the surface sample. Grazing incidence geometry can probe the absolute three-dimensional structural morphology of the sample if a highly coherent beam is used. Coherent Surface Scattering Imaging (CSSI) is a powerful yet non-invasive technique similar to Coherent X-ray Diffractive Imaging (CDI) but performed at small angles and grazing-incidence reflection geometry. A challenge with CSSI is that conventional CDI reconstruction techniques cannot be directly applied to CSSI because the Fourier-transform-based forward models cannot reproduce the dynamical scattering phenomenon near the critical angle of total external reflection of the substrate-supported samples. To overcome this challenge, we have developed a multislice forward model which can successfully simulate the dynamical or multi-beam scattering generated from surface structures and the underlying substrate. The forward model is also demonstrated to be able to reconstruct an elongated 3D pattern from a single shot scattering image in the CSSI geometry through fast-performing CUDA-assisted PyTorch optimization with automatic differentiation.Comment: 12 pages, 4 figures, 1 tabl

Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter

Nonequilibrium states in soft condensed matter require a systematic approach to characterize and model materials, enhancing predictability and applications. Among the tools, X-ray photon correlation spectroscopy (XPCS) provides exceptional temporal and spatial resolution to extract dynamic insight into the properties of the material. However, existing models might overlook intricate details. We introduce an approach for extracting the transport coefficient, denoted as $J(t)$, from the XPCS studies. This coefficient is a fundamental parameter in nonequilibrium statistical mechanics and is crucial for characterizing transport processes within a system. Our method unifies the Green–Kubo formulas associated with various transport coefficients, including gradient flows, particle–particle interactions, friction matrices, and continuous noise. We achieve this by integrating the collective influence of random and systematic forces acting on the particles within the framework of a Markov chain. We initially validated this method using molecular dynamics simulations of a system subjected to changes in temperatures over time. Subsequently, we conducted further verification using experimental systems reported in the literature and known for their complex nonequilibrium characteristics. The results, including the derived $J(t)$ and other relevant physical parameters, align with the previous observations and reveal detailed dynamical information in nonequilibrium states. This approach represents an advancement in XPCS analysis, addressing the growing demand to extract intricate nonequilibrium dynamics. Further, the methods presented are agnostic to the nature of the material system and can be potentially expanded to hard condensed matter systems

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery

Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy

Understanding and interpreting dynamics of functional materials \textit{in situ} is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales, however spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work we have developed an unsupervised deep learning (DL) framework for automated classification and interpretation of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system behavior. We demonstrate how this method can be used to rapidly explore large datasets to identify samples of interest, and we apply this approach to directly correlate bulk properties of a model system to microscopic dynamics. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery

Examining Individual Demographic and School Support Factors Regarding Teachers’ Intention to Use Technology: A Hierarchical Regression Analysis

A proliferation of literature documented the correlation between the teachers’ use of technology and the factors of technostress, burnout as well as the peda-gogical content knowledge. Yet insufficient findings explored the impacting factors of demographic factors of individual teachers and school support on educational use of technology. Hierarchical regression employed in this study advanced the traditional regression analysis of individual demographic factors, added by the second-step of school support model. The statistical results sup-ported both hypotheses that Model 1 of individual factors and Model 2 of the combined factors of individual and school support significantly predicted teachers’ use of technology. In addition, the study results showed that R square value progressed from 0.26 in model 1 to 0.60 in Model 2, implying the additional 34% of the variance explained by the combined factors collectively. The findings shed lights on in robustness of the models in predicting teachers’ in-tention to use technology and the school administrative policy in advocating the persistent use of ICT in educational settings

Probing three-dimensional mesoscopic interfacial structures in a single view using multibeam X-ray coherent surface scattering and holography imaging

Abstract Visualizing surface-supported and buried planar mesoscale structures, such as nanoelectronics, ultrathin-film quantum dots, photovoltaics, and heterogeneous catalysts, often requires high-resolution X-ray imaging and scattering. Here, we discovered that multibeam scattering in grazing-incident reflection geometry is sensitive to three-dimensional (3D) structures in a single view, which is difficult in conventional scattering or imaging approaches. We developed a 3D finite-element-based multibeam-scattering analysis to decode the heterogeneous electric-field distribution and to faithfully reproduce the complex scattering and surface features. This approach further leads to the demonstration of hard-X-ray Lloyd’s mirror interference of scattering waves, resembling dark-field, high-contrast surface holography under the grazing-angle scattering conditions. A first-principles calculation of the single-view holographic images resolves the surface patterns’ 3D morphology with nanometer resolutions, which is critical for ultrafine nanocircuit metrology. The holographic method and simulations pave the way for single-shot structural characterization for visualizing irreversible and morphology-transforming physical and chemical processes in situ or operando

groEL Gene-Based Phylogenetic Analysis of Lactobacillus Species by High-Throughput Sequencing

Lactobacillus is a fairly diverse genus of bacteria with more than 260 species and subspecies. Many profiling methods have been developed to carry out phylogenetic analysis of this complex and diverse genus, but limitations remain since there is still a lack of comprehensive and accurate analytical method to profile this genus at species level. To overcome these limitations, a Lactobacillus-specific primer set was developed targeting a hypervariable region in the groEL gene—a single-copy gene that has undergone rapid mutation and evolution. The results showed that this methodology could accurately perform taxonomic identification of Lactobacillus down to the species level. Its detection limit was as low as 104 colony-forming units (cfu)/mL for Lactobacillus species. The assessment of detection specificity using the Lactobacillus groEL profiling method found that Lactobacillus, Pediococcus, Weissella, and Leuconostoc genus could be distinguished, but non-Lactobacillus Genus Complex could not be detected. The groEL gene sequencing and Miseq high-throughput approach were adopted to estimate the richness and diversity of Lactobacillus species in different ecosystems. The method was tested using kurut (fermented yak milk) samples and fecal samples of human, rat, and mouse. The results indicated that Lactobacillus mucosae was the predominant gut Lactobacillus species among Chinese, and L. johnsonii accounted for the majority of lactobacilli in rat and mouse gut. Meanwhile, L. delbrueckii subsp. bulgaricus had the highest relative abundance of Lactobacillus in kurut. Thus, this groEL gene profiling method is expected to promote the application of Lactobacillus for industrial production and therapeutic purpose