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DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2018

A distillation column for hydrogen isotope removal from liquid lithium

Recovery of tritium from plasma-facing components in fusion devices will be vital to future full-scale operation. Liquid, low-Z materials have demonstrated many inherent advantages over solid first wall materials. To this end, a thermal treatment method in the form of a distillation column for extraction of hydrogen isotopes from liquid lithium has been designed, developed, and constructed at the Center for Plasma-Material Interactions at the University of Illinois at Urbana-Champaign. Use of induction heating and lithium condensation stages are the two qualities that set this design apart from other thermal treatment systems. Induction heating capabilities were modeled using the COMSOL Multiphysics software, which were validated when commissioning the physical heater module. Proof-of-concept tests were performed in the prototype column, which were undertaken as batch processes to investigate the efficacy with which the column could remove hydrogen gas from lithium-rich and lithium hydride-rich samples. All of the tests reported used lithium hydride as a surrogate for lithium deuteride and lithium tritide. The design process and results from the initial tests will be discussed, along with the envisioned placement of this treatment scheme in a fully-functional lithium loop.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Abstract Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Ultrafast flow chemistry for the acid-catalyzed conversion of fructose

Biomass conversion to chemicals and fuels has been epitomized by batch processing and long reaction times that limit the economic viability of future biorefineries and prevent modular manufacturing near the source-production site that is necessary due to the biomass’ large water content. Herein we introduce a continuous flow microreactor to enable process intensification and one thousand-fold device miniaturization for remote chemical processing. We demonstrate the approach in HCl-catalyzed fructose dehydration in water. We characterize and quantify the mixing using laser induced fluorescence (LIF) of fluorescent dyes and particle image velocimetry (PIV) of fluorescent microspheres. The estimated mixing times ranged from 0.03 to 4.8 s for residence times from 1 to 120 s, respectively. Moreover, a curved channel geometry induces secondary (Dean) vortices that produce a three-fold increase in mixing by diffusion through advection along the cross-sectional direction of the channel, creating dissipative chaotic mixing under laminar flow. We then obtain for the first time isothermal kinetics of fructose dehydration at short contact times and high temperatures and compare to a previously published, hybrid ab initio and data-driven kinetic model. Rigorous model optimization is carried out and an optimal HMF yield of 54% is attained in a single aqueous phase. High temperatures and low pH improve the reactor throughput by increasing the HMF space-time yield. We demonstrate the highest ever productivity (HMF yield per unit time). Importantly, this can be achieved at ~100% fructose conversion, eliminating the need for recycling to improve economics. Furthermore, optimization of the reactor dimensions and the specific energy dissipation rate results in ~60% pressure-drop related energy savings. We describe a methodology to scale-up microreactors to millimeter size and increase productivity 16-fold under optimal conditions. The energy efficiency and cost analysis for HMF production from corn stover of a typical corn farm indicate an optimal strategy for numbering-up millimeter size reactors and, importantly, they underscore the potential feasibility of producing bioproducts in a distributed manner.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Rapid scalable fabrication of roll-to-roll slot-die coated flexible perovskite solar cells using intense pulse light annealing

Perovskite solar cells have achieved high efficiencies at the lab scale using batch processes that often may not be scalable. Processes that can be integrated into a continuous manufacturing platform, such as roll-to-roll, where several processes can be carried out sequentially are necessary to develop a new cost-competitive solar technology. In this work, intense pulsed light thermal post-processing is coupled with scalable slot-die coating and air knife evaporation in a high-speed roll-to-roll process of up to 2 m min –1 . The optimization of IPL energy parameters, web speeds, solution flow rates, and concentration of precursor inks yielded a champion power conversion efficiency of 11.24% for a 1 cm 2 roll-to-roll coated device. The overall processing time for a single layer in this process is reduced from minutes to seconds and the overall footprint of the manufacturing process is decreased. Furthermore, this demonstrates the promising capabilities for intensed pulsed light to reduce manufacturing costs and paves a roadmap for scalable and efficient PSCs on a roll-to-roll platform.

14 SOLAR ENERGY↗

DOE OSTI2021

Holistic Numerical Calibration of the iMESA Electrostatic Analyzer

A drifted Maxwellian velocity distribution is the most common model used to interpret the data from low-energy charged-particle instruments onboard spacecraft that are used to investigate the ambient plasma environment in the low Earth orbit (LEO). An original method is presented for determining the flow parameters (density, temperature, and flow energy) of such a distribution from the output of the integrated miniaturized electrostatic analyzer, which has been successfully flown on several LEO missions. Rather than attempting to deconvolve from the on-orbit data the analyzer’s response to an ideal, monoenergetic input, numerical simulation is used to predict and parameterize the response of the device to an input distribution that includes an isotropic, non-zero temperature, yielding a straightforward method for extracting the flow parameters from the spacecraft data. The method is computationally simple enough to be incorporated into a robust algorithm suitable for rapid batch processing or real-time analysis of data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Advanced Health Information Technology Analytic Framework and Application to Hazard Detection

Health Information Technology (HIT) aims to improve healthcare outcomes by organizing and analyzing various health-related data. With data accumulating at a staggering rate, the importance of real-time analytics has been increasing dramatically, shifting the focus of informatics from batch processing to streaming analytics. HIT is also facing unprecedented challenges in adapting to this new requirement and leveraging advanced IT technologies. This paper introduces a HIT data and compute platform that supports multi-granularity real-time analytics from heterogeneous data sources. The paper first identifies functional requirements and proposes a framework that satisfies the requirements using state-of-the-art big data technologies including Apache Kafka, Spark Structured Streaming Engine, and Delta Lake. To demonstrate its capability to support data analytics in multiple time granularities analytics, a statistical process control-based hazard detection algorithm has been implemented on top of the framework to detect unexpected hazards from order cancellation data of the Department of US Veterans Affairs (VA) in near real-time.

Kumar, Mohit↗

DOE OSTI2025

xGFabric: Coupling Sensor Networks and HPC Facilities with Private 5G Wireless Networks for Real-Time Digital Agriculture

Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.

Digital Agriculture↗

DOE OSTI2018

Lagomorph

Existing tools for computational anatomy and image registration are not aimed at HPC environments. As such they are not easily adapted to multimode platforms and contain inefficient batch processing modes. Lagomorph addresses this by providing an easy to install and use pure python implementation of very efficient image registration and atlas building which is aimed at extreme-scale anatomical studies using modern HPC and GPU resources.

Hinkle, JacobD↗

DOE OSTI2024

BARDI

BARDI: Batch-processing Abstraction for Raw Data Integration

Krawczuk, PatrycjaNikol↗

DOE OSTI2025

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗

DOE OSTI2024

Scaling the polymerization of polyaldehydes through continuous flow synthesis

Abstract A plug flow reactor was constructed to scale the synthesis of metastable, phthalaldehyde-based polymers to achieve production rates of 1–2 kg per day. The flow-induced mixing and in-line polymerization quench and precipitation sequences resulted in improved polymer purity and long-term stability compared to the same materials made from a conventional batch process. Cryogenic rheology was used to probe the complex fluid dynamics encountered during the polymerization of PPA homopolymers. It is envisioned that this continuous flow manufacturing approach could be extended to other low ceiling temperature or aldehyde monomer systems to help implement and support a plastic circular economy. Graphical abstract

Schwartz, Jared M.↗

DOE OSTI2019

Development of Ambient Mass Spectrometry Rapid Analysis Methods for Trace Inorganics

Ambient mass spectrometry is being investigated as a new method for rapidly characterizing trace inorganic species, including nuclear materials. SRNL is currently developing electrospray ionization (ESI) and paper spray ionization (PSI) methods for mass spectrometry for the detection of trace inorganic compounds JEOL AccuTOF™-DART ® LP 4G mass spectrometer. Sample analysis generates a complex three-dimensional data set with 10 2 -10 3 individual mass spectra spread temporally. To improve the quality and ease of data extraction from this amount of information, a simple data analysis pipeline written in R/R Studio has been improved and expanded upon. Specifically, the software now has header inputs to set base input/output filenames, isotopes of interest, and a list of concentrations analyzed on the instrument. Using list-based structures to enable batch processing of many concentrations at once, it then loads each file, extracts the relevant intensities for masses of interest from each spectrum, and iterates through each concentration to generate graphs of internal standard & analyte correlations, measured isotope ratios, and total ion chromatograms. It then filters the data to remove points at background levels or at the detector saturation limit. Using this filtered data, the pipeline re-creates the graphs to reveal high-quality information from a low-quality raw dataset. All graphs of the R software then output to PowerPoint format. Additionally, faster 3D visualization functionality was added to the pipeline for viewing the entire data file produced by the instrument as an intensity map. With these data analysis tools ready, samples of strontium nitrate/yttrium nitrate as an analyte/internal standard combination were measured on the AccuTOF. From the filtered data, calibration curves were prepared and limits of detection (LOD) of 1 ppb for ESI-MS and 20 ppb for PSI-MS were obtained. The quality available from the calibration curves and the low limits of detection demonstrate the value of the instrument/analysis pipeline system in characterizing trace inorganics.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Organomercury Measurements from Sludge Batch 10 Simulant Studies

Researchers at the Savannah River National Laboratory have recently performed testing to evaluate the Sludge Batch 10 flowsheet using simulated sludge waste. In the course of this testing several samples were taken to determine the concentration of organomercury species resulting from sludge batch processing. These samples were submitted to the Savannah River National Laboratory Sensing and Metrology department quantitation using a mercury analyzer. The signal amplifier used to perform organomercury quantitation in the mercury analyzer experienced a malfunction in the course of analysis, leading to uncertainty of organomercury concentrations observed. Seventy-three organomercury samples from six project submissions to the Savannah River National Laboratory Sensing and Metrology department have been critically reviewed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

User guide for the Geospatial Risk Analysis Tool (V.1.0.1)

Developed by the Pacific Northwest National Laboratory (PNNL), the Geospatial Risk Analysis Tool (GRAT) links to Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), a complete system for computing simple air parcel trajectories, as well as complex transport, dispersion, chemical transformation, and deposition simulations. The HYSPLIT model was developed by the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory and the Australian Bureau of Meteorology Research Center in 1998. GRAT is used to batch process meteorological data into ARL format. While HYSPLIT GUI limits users to convert meteorological data up to 6 date time points per operation, GRAT enables users to convert a significantly large amount of data (For example, 10 years of meteorological data measured with an interval of 5 minutes) within one operation. Incorporated with the Potential source distribution function (PSDF), GRAT can also be used to determine the areas influenced by the emission of hazardous chemicals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Dissolution of a Can Carrier Pin in Concentrated Nitric Acid

H=Canyon will be dissolving Fast Critical Assembly (FCA) fuel from the Japan Atomic Energy Agency (JAEA). The FCA fuel will be dissolved in the electrolytic dissolver in a solution that is a 50 wt.% nitric acid (HNO 3 ), 0.5 g/l gadolinium (Gd), and 0.05 M potassium fluoride (KF). The nitric acid concentration is expected to decrease during the batch process from 10.3 to 7 M. The temperature of the nitric acid solution may be as low as 15 °C. The fuel can will be placed in the dissolver basket insert utilizing a reusable charging device. The charging device is a coated stainless-steel rod with a clevis design. The charging device employs a linchpin to secure the FCA fuel can as it is being charged into the H-canyon dissolver. Prior to beginning the electrolytic dissolution process, the pin will be dissolved, the fuel can will remain in the dissolver basket insert and the charging device will be removed. Currently the time necessary for complete dissolution of the pin is unknown and thus the timing of the removal of the charging device cannot be planned. This process is to be performed remotely and therefore complete dissolution of the pin will not be able to be visually determined. The pin will be made of a material that dissolves in the concentrated nitric acid solution in the dissolver. The facility desired to know the time that the pin would be dissolved so that the charging device for the FCA can could be removed from the dissolver. In particular, the pin dissolution time as a function of the nitric acid concentration and the temperature was desired. The facility would like to ensure that the solution environment was such that the charging device could be removed within an operational shift (8-12 hours).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

SoW for KDP Precision Cleaning System Control Sys Refurbishment

The KDP Optic Precision Cleaning System (PCS) is used is a standalone batch-process system used to clean Potassium di-Hydrogen Phosphate (KDP) optics required for the National Ignition Facility (NIF). There are two nearly identical machines owned by the US government, as provided by Lawrence Livermore National Security (LLNS). One machine is located at Lawrence Livermore National Laboratory in Livermore, CA in a Class 100 cleanroom and the other is located at the Gooch & Housego (G&H) facility in Highland Heights, OH in a Class 1,000 cleanroom.

42 ENGINEERING↗