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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Supervisory Control and Data Acquisition for Electrochemical Separation Experimentation

The Python-based program is a laboratory automation tool designed to control and monitor electrochemical systems. The tool was developed for capacitive deionization (CDI) experiments, but it can be used for any system that requires controlled voltage or current segments and multi-parameter monitoring. The program integrates hardware components to run user-defined experimental parameters, providing operational control of a programmable power supply, peristaltic pump, and data acquisition devices. Currently, the program is structured with a workflow that includes an initialization (or pre-run) phase, a main loop, and a post-experiment stabilization (or post-run) phase. The initialization phase prepares and stabilizes the cell, ensuring that the electrodes and solution reach a baseline state before the experiment begins. The main loop consists of multiple voltage segments that repeat, controlling the experiment while recording key parameters such as time, voltage, current, pH, and conductivity. Finally, the post-experiment stabilization phase allows the system to stabilize after the experiment, returning the cell and solution to equilibrium conditions before ending the sequence. The program is designed with four variations, each tailored to different experimental needs. All variations include both the initialization and post-experiment stabilization stages, which run for a set amount of time, voltage, current, and flow rate before and after the main experiment block. The main loop runs for a set number of cycles, as defined by the user input, and each cycle is composed of 2 or 4 segments. The 4 program variations are described as follows: Program 1: The main program includes 2 segments. Each segment is defined to have a set duration, flow rate, voltage, and current. This program measures conductivity, flow rate, voltage, and current. Program 2: The main program expands Program 1 to include 4 segments. Each segment has a specified duration, flow rate, voltage, and current. Like Program 1, it measures conductivity, flow rate, voltage, and current. Program 3: The main program consists of 2 segments, each defined by time, flow rate, voltage, and current. In addition to conductivity, flow rate, voltage, and current, Program 3 collects pH and temperature data through a 4-channel data acquisition device. Program 4: This program independently controls two channels of a multi-channel power supply simultaneously. While conductivity can only be measured for one cell at a time, the dual-channel control makes it possible to operate two cells simultaneously under different voltage/current conditions. The main program includes 2 segments.For each program, all measurements are automatically logged and integrated into a single Excel output file. Data are displayed in numerical format and plotted, both in real time, to track system performance. A key feature of the program is its ability to synchronize all outputs so that every measurement shares a single timestamp, ensuring accurate alignment of voltage, current, pH, conductivity, and pH data.By combining hardware control, real-time monitoring, and unified data collection, this program significantly reduces manual workload and minimizes errors, making it a reliable platform for researchers, engineers, and laboratory technicians conducting CDI experiments, among other electrochemical tests.

Valentino, Lauren [Argonne National Laboratory (AN↗
DOE OSTI2025

The Automatic Reactor Control System (ARCS) Upgrade, version 2.0 for TREAT

The Transient REActor Test Facility (TREAT) recently underwent an upgraded to the Automatic Reactor Control System (ARCS). The purpose of the upgrade was to institute a new software architecture that is better suited for the programming environment, patched software bugs and applied two new segments for reactor control. This paper will focus on the new segments for reactor control. The two new control segments added to ARCS are called Generic Power and Generic Rods. The original version of ARCS provided only two power related functions, namely periods and ramps, that had to be spliced together to generate any power shape. The Generic Power segment allows the user to input data points to define the function and then the algorithm does its best to create the desired shape. The original ARCS program utilized two direct rod commands (i.e. open loop control), these were a rod stop and clip. A Generic Rod segment extends the ability of the computer to directly command any rod position. The functionality of these new segments was proven during the TREAT outage in 2024.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2026

Impact of Porous Transport Layer In-Plane Conduction on Spatially Resolved Current and EIS Measurements in a Proton Exchange Membrane Water Electrolyzer

An XY segmented cell was developed for low temperature PEM water electrolysis (PEMWE). The system can assess the local performance by enabling in situ measurements of spatial currents and impedances. In this work, we show through experiments, as well as through modelling work, that the porous transport layer (PTL) must be segmented to eliminate crosstalk. Accurate measurements are only possible when crosstalk is fully eliminated. The XY segmented cell is applied to a case study characterizing the impact of a PTL platinum coating void on spatial performance. The localized performance impact of the coating void is found to be orientation specific: coating voids facing the catalyst layer reduce performance significantly more than coating voids facing the flow field. The results suggest that the tolerances for PTL coating uniformity can be lower at the side facing the flow field. The work showcases the feasibility of the XY segmented cell for impact assessment studies. The presented XY segmented cell enables the characterization of spatial phenomena in PEMWE devices and is envisioned to support modeling efforts and the investigation of manufacturing related tolerances for mass produced PEMWE devices.

08 HYDROGEN↗
DOE OSTI2026

Correlating and Simulating Socio-Demographically Driven Residential End-Use Activity Schedules

Incorporating socio-demographic and behavioral considerations into decision-support tools is crucial for identifying gaps and addressing consumer needs to ensure reliable and affordable energy solutions. In energy simulation models, the correlation between socio-demographics and time-use behavior is not well-captured. Thus, we developed a large-scale simulation workflow to generate schedules for 10 residential activities across 24 population segments defined by age, income, and employment status. Using pre-pandemic 2015-2019 American Time Use Survey (ATUS) data, we used ANOVA to confirm the correlation between demographic factors and time use. We explored three k-modes clustering methods-backward, forward, and a new hybrid approach-to delineate the occupancy patterns based on demographics. Using the probability of cluster membership for each population segment and a time inhomogeneous Markov chain to generate activity transition probabilities for each cluster, we simulated 50,000 schedules per segment and validated them against the ATUS data. The hybrid method produced the most socio-demographically differentiated clusters while demonstrating comparable performance to other approaches, with an overall root mean square error of 0.12 for both weekday and weekend schedules. Thus, the hybrid method, where each cluster is dominated by certain demographic segments and occupancy patterns, offers more modeling versatility in terms of scenario analysis. The new workflow improves the socio demographic differentiation of energy consumption by considering differences in time use. This approach enables future research on demographically segmented time of use (TOU) energy consumption, including impacts of TOU utility bills and rate analysis, long-run marginal emissions, and energy retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Quantifying Trapped Powder in Electron Beam Powder Bed Fusion

Abstract Electron beam powder bed fusion (PBF-EB) shows great potential for manufacturing complex parts including those with internal cavities for heat exchanger, manifold systems, or energy absorption purposes. PBF-EB allows for the manufacture of channel geometries without the need for support structures. Due to the nature of the powder spreading process, powder feedstock is often trapped in intentionally manufactured cavities. This trapped powder can often be difficult to remove and can disturb the intended flow of fluid through the cavity or damage downstream components in its use case. These trapped powder particles present a risk of contamination and component failure if not completely evacuated. Ti6Al4V is a choice material for aerospace applications due to its high strength to weight ratio and its composition as a nonferrous metal; however, in weight sensitive applications excess entrapped powders or powders loosely attached to the surface could cause undesirable weight increases. The inherent spreading process of PBF-EB is different than laser powder bed fusion (PBF-LB) in its operational temperature, sintering. In addition, PBF-EB is less commonly studied in literature compared to its PBF-LB counterpart, and as a result the complexity of the semi-sintered powder and its spreading behavior are not well understood. Prior work has investigated the difficulty in removing trapped powder from PBF-EB, but these studies do not address how to quantify the amount of trapped powder in the cavity. Thus, an accurate method to measure the amount of trapped powder in the cavity must be investigated. In this work, Ti6Al4V coupons were manufactured with horizontal and vertical cavities of three different sizes. Archimedes testing allows for the determination of density differences caused by porosity and trapped powders by measuring mass and volumetric dispersion. Computed tomography (CT) is well suited for segmenting the internal structure and features of a part and has been studied for applications including voids, porosity, and dross. Thus, CT was explored as a method for evaluating trapped powder content in this work. The volumetric representation of the segmentation of the reconstructed CT volume can vary greatly depending on the input filter and thresholding methods. In this study, four different types of segmentation approaches were evaluated to determine the best approach for segmenting the volume as compared to an operator labeled ground truth. The percentage density results from the Archimedes testing were compared to the volumetric percent density from the computed tomography approach. Differences in packing density between two different internal channel features were investigated. Overall, this work sought to validate the use of computed tomography for the detection of trapped powders and present a framework for volumetric segmentation.

Johnstone, Brian↗
DOE OSTI2026

Dielectric Spectroscopy Cable NDE for Unequal Aging Over Different Lengths

This Pacific Northwest National Laboratory milestone report assesses the effect of unequal aging over different lengths of cable on dielectric spectroscopy (DS) bulk impedance measurements from the cable end. DS measurements can indicate the remaining useful life of cables; however, most benchmark tests are based on accelerated aging of the entire cable. If only a portion of the cable is exposed to a harsh environment (as is frequently the case), the DS measurement will indicate a significantly less-aged cable than if the entire cable were exposed to a uniform environmental stress. The DS measurement is primarily related to the intrinsic insulation material relative permittivity—typically between 1.5 and 3.0. Insulation aging increases permittivity and, correspondingly, the cable’s overall capacitance measured from the cable end. To judge the health of the overall cable system, operators are primarily interested in the condition of the most severely aged section since that is where a failure is most likely to occur. This can be expressed as the dimensionless relative permittivity of the severely aged section divided by the permittivity of a pristine cable. This relative permittivity is an intrinsic material parameter independent of cable length and can serve as a quantitative indication of the insulation condition. If the relative percentages of cable exposed to a harsh and benign environment are known, the DS measurement of the entire cable length, including both pristine and aged sections, can be compensated to predict the relative permittivity (relative to pristine permittivity) of the most severly aged segment. A test was performed on three 50 ft (16 m) cables with 10%, 50%, and 90% of the cable inside a thermal aging oven. Predictably, the aging effect on the DS response was greatest in the 90% sample, followed by the 50% sample, and then the 10% sample. The more interesting result is the ratio of the aged insulation permittivity to the pristine insulation permittivity. Based on prior work, an end-of-life cable has a value of approximately 1.35. If the relative percentages of pristine and severely aged cable lengths are known, and the total cable-length capacitance of the initial pristine cable and the combined pristine plus aged cable are known, the relative aged permittivity (ratio of aged section permittivity / pristine section permittivity) can be estimated. The relative cable lengths exposed to a harsh environment may be known or estimated based on plant layout drawings or cable reflectometry tests that can locate the oven entry and exit points. The compensation was verified using a lumped-parameter model to predict the aged-segment permittivity e2/e1, where e2 is the aged-segment permittivity and e1 is the pristine-segment permittivity. For all three percentages of aged cable lengths, the ratio was quite similar, as would be expected since all cable segments were exposed to the same aging environment. This ratio of aged permittivity to pristine permittivity can be compared to other damage indicating tests, including elongation at break, to assess cable condition.

ARENA Test Bed↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Monitoring Depolymerization in Mesopores Using Dynamic Properties of Polymeric Melt Accessed via Dielectric Spectroscopy

Traditional design principles for heterogeneous catalysis guide the use of catalytic particles with mesosized (∼2–50 nm) pores to increase the number of surface-active sites by way of an increased surface area. However, the entry of long-chain polymers into such pores may be significantly limited by the size and entanglement of polymers in the melt state, thereby decreasing the number of accessible sites. Assessment of catalyst performance from traditional reactor-based studies averages over intrapore reaction events as well as reactions on the surface of a particle, resulting in an inability to distinguish between differences in site accessibility and activity. Techniques that assess the intrapore performance can inform the design of future heterogeneous catalysts for polymer upcycling. In this work, we demonstrate the use of broadband dielectric spectroscopy to monitor depolymerization of a polymer melt within mesopores via changes in the segmental relaxation time scale of amorphous polymer chains. In particular, we highlight the use of an anodic aluminum oxide (AAO) membrane as a readily available model for catalyst pores with a well-characterized pore morphology. The decrease in the segmental relaxation (α-relaxation) time of the melt with increasing chain scission emerges as a measure of the extent of polymer deconstruction inside mesopores. To demonstrate the utility of this technique, we demonstrate the decomposition of two commercial poly(propylene carbonate) polymers with different decomposition rates within mesopores. As the polymers depolymerize, their segmental relaxation time decreases as the molecular weight decreases (as predicted by the Fox–Flory equation). The BDS-measured change in segmental relaxation time mirrors the expected trend based on change in molecular weight measured by size exclusion chromatography.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Crack Identification and Characterization in Deformed Nb3Sn Rutherford Cable Stacks Using Machine Learning

An investigation of instance segmentation of cracks in Nb3Sn 4-stack 40-strand Rutherford cables using machine learning is presented. Three samples were uniaxially and biaxially loaded before metallographic inspections were performed. The Mask R-CNN model was used in the Detectron2 framework with pre-trained weights but fine-tuned to detect and segment cracks. The model detected cracks with bounding box and mask average precisions (AP) of 42.8 and 27.9, respectively, and was used for instance segmentation of all cracks in the three samples. More cracks were found in the sample pre-loaded along the z-axis (i.e., along the cable length). Pre-loading along the x-axis (i.e., on the cables edges) reduced the number of cracks and changed the crack orientation distribution, away from being highly aligned with the y-axis (i.e., normal to the cables broad faces), i.e., the direction with the highest applied load. Fine-tuning of the Segment Anything Model (SAM) was also studied but performed poorly without human-provided prompts. However, the zero-shot capability of SAM showed high promises to accelerate the image annotation process for applications beyond this study.

Croteau, Jean-Francois↗
DOE OSTI2025

Stochastic parametric skeletal dosimetry model for humans: Anatomical-morphological basis and parameter evaluation

Radiation exposure of the hematopoietic system that results in a radiation dose to bone marrow of more than 100 mGy leads to an increase in the risk of leukemia in humans. Excess relative risk of leukemia was observed in cohorts whose members lived in the territories of the Southern Urals that were radioactively contaminated in the 1950s. As part of the dosimetric support of epidemiological studies of these cohorts, an original methodology for stochastic bone dosimetric modeling was developed, termed the Stochastic Parametric Skeletal Dosimetry (SPSD) model. The purpose of this work was to present the anatomical and morphological bases of the SPSD model, which includes an assessment of the parameters of the microstructure of the trabecular bone in the hematopoietic areas of the human skeleton, as well as a description of the macrostructural division (segmentation) of hematopoietic areas into simple bone segments. As a result, an anatomical-morphological basis of the SPSD model was created based on published data. Data collection work included the analysis of original articles, atlases, manuals, monographs and the formation of primary data files. Data on the duration of hematopoiesis in various parts of the skeleton; and data on age-related changes in the microstructure and linear dimensions of human bones and their segments were analyzed. The paper describes the full set of parameters to be used for the dosimetric model for newborns, children aged 1, 5 and 10 years, as well as for adolescents aged 15 years and adults; for the latter, sex differences in bone size were considered. In total, the SPSD model includes 289 unique basic (bone) phantom segments, each of which is described by 7 or more parameters describing the microstructure, thickness of the cortical layer and linear dimensions. Population variability was estimated for each parameter. The approach to SPSD modeling, i.e., the use of simple geometric shapes, was successfully verified using independent datasets on bone masses and volumes.

Science & Technology - Other Topics↗
DOE OSTI2024

The Design and Evaluation of Zero Trust Architecture for Electric Vehicle Charging Infrastructure: EVs @ Scale Series on EV Charging Station Cybersecurity

Implementing a zero trust architecture can significantly bolster the security of electric vehicle (EV) charging infrastructure. EV charging infrastructure includes numerous networked interfaces, each of which can present potential vulnerabilities. When these vulnerabilities are exploited, they can compromise the entire system, leading to severe operational and security risks. Zero trust is a security model that operates on the principle of "never trust, always verify," which helps manage the attack surface and limit the scope of any potential compromises. Fundamentally, this model ensures that no entity, whether inside or outside the network, is trusted by default. The design principles of zero trust include continuous verification, strict deny-by-default access controls, and micro-segmentation. Continuous verification ensures that every request is thoroughly checked, regardless of its origin. Strict access controls enforce the principle of least privilege, allowing users and devices only the minimum necessary access to perform their functions. Micro-segmentation involves dividing the network into smaller, isolated segments to prevent lateral movement in case of a breach. In the context of EV charging infrastructure, zero trust can be implemented through various strategies. For example, multi-factor authentication (MFA) can be required for engineers to access the management interfaces and control systems of charging stations. Real-time monitoring and analysis of network traffic can help detect and respond to anomalies. Systems that do not need to communicate with each other can be micro-segmented to enhance security. All communications should adhere to predefined policies to be permitted. Additionally, encrypting communications can protect sensitive information exchanged between chargers and management systems. This paper presents a zero trust architecture specifically designed for EV charging infrastructure. Implementing zero trust not only mitigates risks but also builds a resilient infrastructure capable of withstanding and quickly recovering from cyber threats. The architecture addresses six defined security objectives. A comprehensive test plan is developed to assess the architecture against these objectives, and the results of the evaluation are reported. This approach is essential for maintaining the reliability and integrity of EV charging services in an increasingly interconnected and vulnerable digital landscape. This is the first in a planned series of papers exploring the implementation of zero trust in EV charging infrastructure. Each paper will delve into different aspects and applications of zero trust, highlighting how various work processes and requirements can lead to distinct architectural designs. These architectures will be tailored to address specific security challenges and operational needs within the EV charging ecosystem, ensuring a robust and adaptable security framework.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

RU Net for Automatic Characterization of TRISO Fuel Cross Sections

TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

Convolutional Neural Networks↗
DOE OSTI2024

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Optical image analysis of WSe 2 − thresholding for layer detection

The fast and reliable layer identification of two-dimensional transition metal dichalcogenide (TMD), such as WSe 2 , is essential to investigating their thickness-dependent electronic and optical properties. This article presents efficient optical image thresholding methodology designed to segment the mono, bi, and tri-layer regions of WSe 2 flakes mechanically exfoliated onto a SiO 2 /Si substrate. The optical images were first preprocessed to exclude the background effect and analyzed using the pixel medians and interquartile ranges for fundamental color channels—red, green, and blue (RGB). The analysis of red channel pixel intensities yielded three distinct ranges, serving as thresholds for layer segmentation: monolayer (111.0–118.0), bilayer (103.0–110.0), and tri-layer (93.0–103.0). Similarly, thresholds were established for each color channel, facilitating a comparative study of the segmentation performances. Further, the intersection-over-union ($IoU$) calculations revealed that the red and green channels demonstrated greater than 99 % and 90 % accuracy in differentiating each layer, respectively. This approach yields remarkable results without substantial data calibration that utilizes time-intensive heuristic techniques. Moreover, the proposed methodology offers the flexibility to compare performances across different color channels, expanding the applicability for other 2D material systems.

2D Materials↗
DOE OSTI2025

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationship and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology is applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalyst NPs. The model's performance in detecting and segmenting NPs is validated across diverse heterogeneous catalyst systems, including various metals (Ru, Cu, PtCo, and Pt), supports (silica (SiO 2 ), γ-alumina (γ-Al 2 O 3 ), and carbon black), and particle diameter size distributions with mean and standard deviations ranging from 1.6 ± 0.2 nm to 9.7 ± 4.6 nm. The proposed machine learning (ML) methodology achieved an average F1 overlap score of 0.91 ± 0.01 and demonstrated the ability to disentangle overlapping NPs anchored on catalytic support materials. The segmentation accuracy is further validated using the Hausdorff distance and robust Hausdorff distance metrics, with the 90th percent of the robust Hausdorff distance showing errors within 0.4 ± 0.1 nm to 1.4 ± 0.6 nm. In conclusion, our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. Here, we propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback.

image segmentation↗
DOE OSTI2024

Quasi-elastic neutron scattering study on dynamically asymmetric polymer blends

Compatibility between polymers with different glass transition temperatures controls the thermomechanical properties of blends. This work explores the segmental dynamics of poly(methyl acrylate) (PMA) chains when they are blended with polymers of different rigidities and miscibility. Inspired by the intriguing dynamic asymmetry of chains within the interfacial layer of nanoparticles, this study aims to understand dynamic heterogeneity in dynamically asymmetric blends of PMA/poly(methyl methacrylate) (PMMA), PMA/polystyrene (PS), and PMA/poly(ethylene oxide) (PEO) using the differential scanning calorimetry (DSC) and quasi-elastic neutron scattering (QENS) measurements below and above the glass transition temperature of PMMA or PS. Further, results revealed that the segmental jump distance of PMA increased on blending due to volume enhancement. The reduced effective diffusivity of PMA in PMMA is attributed to PMMA's enhanced flexibility (lower characteristic ratio, C ∞ ) and superior interaction (lower ) when compared with the PS environment. The results demonstrate that the chain rigidity and miscibility affect the free volume, interchain cooperativity, and segmental dynamics in dynamically asymmetric blends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exact enforcement of temporal continuity in sequential physics-informed neural networks

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.

42 ENGINEERING↗