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Hexemer, Alexander

Publications and source records attributed to Hexemer, Alexander.

17 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Biopolymer-Templated Titania Film Formation for Nanostructured Coatings Revealed by Machine Learning-Supported Time-Resolved Analysis

This study presents a machine learning approach to derive the film formation of biopolymer-templated titania nanostructures during spray deposition, in combination with in situ grazing-incidence small-angle X-ray scattering (GISAXS). A neural network trained on synthetic GISAXS data directly predicts domain-size distributions from experimental two-dimensional scattering patterns, capturing the full kinetics of nanostructure evolution with high temporal resolution. The predictions reveal hierarchical size distributions and periodic growth features, consistent with layer-by-layer spray deposition and validated by complementary scanning electron microscopy (SEM) imaging. Quantitative comparison with conventional parametric GISAXS fits shows good qualitative agreement, with systematic differences explained by domain-shape assumptions and resolved by applying a geometric scaling factor. Simulated SEM-like surfaces derived from neural network outputs reproduce the porous, foam-like nanoscale morphology observed experimentally, reinforcing the method’s credibility. This integrated approach enables real-time, nondestructive, statistically averaged monitoring of bulk nanostructure development in functional coatings, offering a scalable methodology to accelerate the characterization and process control of sustainably manufactured nanostructured titania films for energy-related applications such as photocatalysis and photovoltaics.

Heger, JulianEliah↗
DOE OSTI2025

Accelerating Advanced Light Source Science Through Multi-Facility HPC Workflows

Synchrotron light sources support a wide array of techniques to investigate materials, often producing complex, high-volume data that challenge traditional workflows. At the Advanced Light Source (ALS), we developed infrastructure to move microtomography data over ESnet to ALCF and NERSC, where CPU- and GPU-based algorithms generate 3D reconstructed volumes of experimental samples. We employ two data movement and reconstruction models: real-time processing as data streams directly to NERSC compute nodes, and automated file transfer to NERSC and ALCF file systems. The streaming pipeline provides users with feedback in under ten seconds, while the file-based workflow produces high-quality reconstructions suitable for deeper analysis in 20-30 minutes. This infrastructure enables users to utilize HPC resources without direct access to backend systems. We plan to extend this architecture to more endstations, supporting our beamline scientists and users.

Abramov, David↗
DOE OSTI2025

Optimizing inference of segmentation on high-resolution images in MLExchange

MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3×$$\times$$ speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40×$$\times$$ speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time.

Lu, Shizhao↗
DOE OSTI2024

Characterization of latent image of electron beam resist via critical-dimension resonant soft X-ray scattering

Metrology plays a crucial role in semiconductor manufacturing by providing accurate and precise measurement and characterization of critical parameters. With the development of high-resolution extreme ultraviolet lithography (EUVL) processes, critical dimensions are shrinking to sub-10 nm. Resist materials encounter the challenge of providing heightened sensitivity and a handle on exacerbating stochastic variations. A comprehensive understanding of the chemical profile of the latent image is pivotal for mitigating stochastic effects and optimizing pattern quality. However, the subtle differences in chemistry between the exposed and unexposed regions of the resists make it extremely challenging to characterize the latent images with sub-nanometer precision. Here, we develop the metrology with critical-dimension resonant soft X-ray scattering (CD-RSoXS) to probe the chemical profiles of latent images stored in resist after exposure. The combination of absorption spectroscopy and enhanced scattering contrast makes it possible to characterize the subtle structural and chemical variations in the latent image. Moreover, the results of the measurements are compared with the simulations with a finite element method–based Maxwell solver to extract a detailed profile of the latent and developed images. We demonstrate that the CD-RSoXS technique can provide valuable insights into the high spatial resolution and local chemical sensitivity simultaneously, which is crucial to understanding the resolution limits and stochastic effects in EUVL processes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Double-Helical Tiled Chain Structure of the Twist-Bend Liquid Crystal Phase in CB7CB

The twist-bend nematic liquid crystal phase is a three-dimensional fluid in which achiral bent molecules spontaneously form an orientationally ordered, macroscopically chiral, heliconical winding of a ten nanometer-scale pitch in the absence of positional ordering. Here, the structure of the twist-bend phase of the bent dimer CB7CB and its mixtures with 5CB is characterized, revealing a hidden invariance of the self-assembly of the twist-bend structure of CB7CB, such that over a wide range of concentrations and temperatures, the helix pitch and cone angle change as if the ground state for a pitch of the TB helix is an inextensible heliconical ribbon along the contour formed by following the local molecular long axis (the director). Remarkably, the distance along the length for a single turn of this helix is given by 2πR mol , where R mol is the radius of bend curvature of a single all-trans CB7CB molecule. This relationship emerges from frustrated steric packing due to the bent molecular shape: space in the fluid that is hard to fill attracts the most flexible molecular subcomponents, a theme of nanosegregation that generates self-assembled, oligomer-like correlations of interlocking bent molecules in the form of a brickwork-like tiling of pairs of molecular strands into duplex double-helical chains. At higher temperatures in the twist-bend phase, the cone angle is small, the director contour is nearly along the helix axis z, and the duplex chains are sequences of biaxial elements formed by overlapping half-molecule pairs, with an approximately 45° rotation of the biaxis between each such element along the chain.

36 MATERIALS SCIENCE↗
DOE OSTI2023

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. Here 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 q z , 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 q z 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.

36 MATERIALS SCIENCE↗
DOE OSTI2022

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. Furthermore, 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 10 9 –10 11 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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

MLExchange v1

A Machine Learning Platform that connects beamline scientists and machine learning (ML) researchers so that the ML researchers can quickly distribute their ML models to beamline scientists, and beamline scientists can give feedback to help ML researchers improve their models.

Hexemer, Alexander↗
DOE OSTI2020

K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy

Abstract We propose the combination of k-means clustering with Gaussian Process (GP) regression in the analysis and exploration of 4D angle-resolved photoemission spectroscopy (ARPES) data. Using cluster labels as the driving metric on which the GP is trained, this method allows us to reconstruct the experimental phase diagram from as low as 12% of the original dataset size. In addition to the phase diagram, the GP is able to reconstruct spectra in energy-momentum space from this minimal set of data points. These findings suggest that this methodology can be used to improve the efficiency of ARPES data collection strategies for unknown samples. The practical feasibility of implementing this technology at a synchrotron beamline and the overall efficiency implications of this method are discussed with a view on enabling the collection of more samples or rapid identification of regions of interest.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Preferred crystallographic orientation of cellulose in plant primary cell walls

Abstract 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)/( $$1\bar 10$$ 1 1 ¯ 0 ) 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.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Estimation of Line Cross Sections Using Critical-Dimension Grazing-Incidence Small-Angle X-Ray Scattering

Currently, the semiconductor industry is continuously pushing the limits of photolithography, with feature sizes now smaller than 10 nm. To ensure quality, it has become necessary to look beyond the conventional metrological techniques. X-ray scattering has emerged as a possible contender to determine the average shape of a line grating with subnanometer precision. Yet, to fulfill its promise, faster algorithms must also be developed to interpret and extract metrics from reciprocal-space scattering data. In this paper, we present a fast and accurate x-ray technique and analysis algorithm: critical-dimension grazing-incidence small-angle x-ray scattering (CD GISAXS). The CD GISAXS technique is used in grazing-incidence configuration with a continuous azimuthal rotation of the sample, and thus does not require high-energy x-rays to penetrate the wafer and greatly reduces the data-acquisition times, permitting analysis within the framework of the distorted-wave Born approximation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

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 13 C, 15 N, and 17 O chemical shifts when compared to ab initio quantum chemistry methods, with the highest accuracy found for 1 H 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 1 H , as well as indicating that chemical shift prediction for 13 C, 15 N, and 17 O, which have far fewer training environments than the 1 H atom type, will improve once more unique training samples are made available to exploit the deep network architecture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Polarized Soft X-ray Scattering Reveals Chain Orientation within Nanoscale Polymer Domains

Fully conjugated donor-acceptor block copolymers can serve as the active layer in organic photovoltaics and other organic electronic devices. Additionally, self-assembly into periodic domains and crystallization of the constituent blocks are crucial to enable control of the multiscale structure and consequently electronic properties. Resonant soft X-ray scattering (RSoXS) is an invaluable tool to characterize such materials, where tuning the X-ray energy and polarization reveals molecular orientation and domain spacing. In this work, anisotropic soft X-ray scattering data reveal the type and degree of orientation within conjugated block copolymers composed of poly(3-hexylthiophene-2,5-diyl) and various push-pull copolymers. The maximum anisotropy is observed at the scattering vector corresponding to the spacing between block copolymer domains, which scales with the end-to-end distance of the blocks, as expected. Furthermore, the anisotropy in RSoXS data reveals that the crystalline P3HT blocks orient, on average, parallel or nearly parallel to the block copolymer interface; the average tilt angle between P3HT chains and domain interfaces can be extracted from the dependence of the anisotropy with polar angle. We interpret this angle to correspond to the average tilt of rings in the unit cell, potentially within a chiral mesostructure. Results are corroborated with scattering calculations based on simple model structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification

Nano-structured thin films have a variety of applications from waveguides, gaseous sensors to piezoelectric devices. Grazing Incidence Small Angle x-ray Scattering images enable classification of such materials. One challenge is to determine structure information from scattering patterns alone. This paper highlights the design of multiple Convolutional Neural Networks (CNN) to classify nanoparticle orientation in a thin film by learning scattering patterns. The network was trained on several thin films with a success rate of 94%. We demonstrate CNN robustness under different noises as well as demonstrate the potential of our proposed approach as a strategy to decrease scattering pattern analysis time.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Random Copolymers Allow Control of Crystallization and Microphase Separation in Fully Conjugated Block Copolymers

Thin films of fully conjugated donor–acceptor block copolymers composed of an electron donating block and an electron accepting block can be used as single component photoactive layers in organic photovoltaic (OPV) devices. In order to realize their full potential, control over microphase separation and thin-film morphology are critical. In conjugated block copolymer systems where one or more blocks can crystallize, the morphological evolution is governed by the competition between microphase separation and crystallization. In this work, we control crystallization of fully conjugated block copolymers with a random copolymer block. Additionally, we suppress the crystal packing of poly(3-hexylthiophene-2,5-diyl) (P3HT) through the insertion of a small number of 3-octylthiophene (3OT) units within the chains, yielding poly(3-hexylthiophene-2,5-diyl- random -3-octylthiophene-2,5-diyl) (P[3HT- r -3OT]). Furthermore, while crystallization of P3HT dominates the morphology and prevents microphase separation in poly(3-hexylthiophene-2,5-diyl)- block -poly((9,9-dioctylfluorene-2,7-diyl)- alt -(4,7-di(thiophene-2-yl)-2,1,3-benzothiadiazole)-5',5"-diyl) (P3HT- b -PFTBT), modest levels of 3OT suppress crystallization in P[3HT- r -3OT]- b -PFTBT, and permit microphase separation. Thus, we demonstrate that incorporating a random copolymer into a donor–acceptor block copolymer can increase control over microphase separation and lead to enhanced performance in OPV devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Using resonant soft x-ray scattering to image patterns on undeveloped resists

Copyright © 2018 SPIE. Extreme ultraviolet lithography is one of the most promising printing techniques for high volume semiconductor manufacturing at the 14 nm half-pitch device node and beyond. However, key challenges around EUV photoresist materials such as the exposure-dose sensitivity or the line-width roughness continue to impede the full adoption into industrial nanofab facilities. New metrology tools are required to address these challenges by helping to determine the impact of the EUV materials' properties and processing conditions on the roughness through the different step of the process. Here, we apply the resonant soft x-ray scattering (RSOXS) technique to gain insights into the structure of patterned EUV resists before the development step takes place. By using energies around the carbon absorption edge to take advantage of small differences in chemistry, the electronic density contrast between the exposed and unexposed regions of the resists could be enhanced in order to image the patterns with sub-nm precision. Critical-dimension grazing incidence small-angle X-ray scattering (CDGISAXS) was then performed at energies where the contrast was maximized, enabling the reconstruction of the 3D shape of the latent image. This demonstrates the potential of RSOXS to provide a high-resolution heightsensitive profile of patterned EUV resists, which will help to quantify the evolution of critical features, such as the line edge roughness, at each step of the nanofabrication process.

Pandolfi, Ronald J↗