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At least 253 records · Page 14

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DOE OSTI2025

Real-time process monitoring and automated control for direct ink write 3D printing of frontally polymerizing thermosets

Additive manufacturing (AM) enables the fabrication of complex geometries, yet its application to thermosets remains limited by post-processing requirements. Frontal ring-opening metathesis polymerization (FROMP) offers a promising alternative, enabling energy-efficient, in situ curing of freestanding thermoset structures. This study presents a real-time process monitoring and automated control system for direct ink writing (DIW) of FROMP thermosets. By integrating thermochromic leuco dyes and computer vision, we enable real-time polymerization front tracking, allowing autonomous printing parameter adjustments for consistent geometries across resin formulations. The system’s accuracy was validated against manual tracking, demonstrating precise front velocity detection. Its adaptability was confirmed by printing freestanding mechanical springs with different resins, achieving consistent geometries and mechanical properties despite front velocity variations. These findings highlight the potential of automated DIW control for scalable, repeatable, and material-agnostic 3D printing of thermosets.

Mejia, Edgar Brian [Sandia National Laboratories (↗

DOE OSTI2021

Trapped Electrons and Ions as Particle Detectors

Electrons and ions trapped with electromagnetic fields have long served as important high-precision metrological instruments, and more recently have also been proposed as a platform for quantum information processing. Here we point out that these systems can also be used as highly sensitive detectors of passing charged particles, due to the combination of their extreme charge-to-mass ratio and low-noise quantum readout and control. In particular, these systems can be used to detect energy depositions many orders of magnitude below typical ionization scales. As illustrations, we suggest some applications in particle physics. We outline a nondestructive time-of-flight measurement capable of sub-eV energy resolution for slowly moving, collimated particles. We also show that current devices can be used to provide competitive sensitivity to models where ambient dark matter particles carry small electric millicharges $\ll$e. Furthermore, our calculations may also be useful in the characterization of noise in quantum computers coming from backgrounds of charged particles.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Vibrational processing of a dynamic structural flight system: A multichannel spectral estimation approach

Dynamic testing of large flight vehicles (rockets) is not only complex, but also can be very costly. These flights are infrequent and can lead to disastrous effects if something were to fail during the flight. The development of sensors coupled to internal components offers a great challenge in reducing their size, yet still maintaining their precision. Sounding rockets provide both a viable and convenient alternative to the more costly vehicular flights. Some of the major objectives are to test various types of sensors for monitoring components of high interest as well as investigating real-time processing techniques. Signal processing presents an extreme challenge in this noisy multichannel environment. The estimation and tracking of modal frequencies from vibrating structures is an important set of features that can provide information about the components under test; therefore, high resolution multichannel spectral processing is required. Finally, the application of both single channel and multichannel techniques capable of producing reliable modal frequency estimates of a vibrating structure from uncertain accelerometer measurements is discussed.

42 ENGINEERING↗

DOE OSTI2020

Analysis of Space-Conditioning Loads in Commercial Buildings

Space conditioning end-uses, which include heating, cooling, and ventilation, represent a significant fraction of commercial building energy use, with a wide variety of heating and cooling technology options available in the market. In the interest of improving the overall efficiency of heating, ventilation and air-conditioning (HVAC) technologies, governments, utilities and private sector entities have implemented a variety of market transformation policies that aim to influence consumer purchase decisions. To evaluate the costs and benefits of such programs, analysts typically postulate a hypothetical default equipment choice, and compare it to one that provides comparable service with lower energy and/or power use. The corresponding reduced operating cost provides a benefit that offsets the potential higher cost of improved efficiency. Typically, life-cycle cost or cash-flow analyses are used to quantify the net economic benefit. These analyses require the capability to assess how a given equipment design would perform across a broad range of characteristics, both of the building and of the local weather. While these assessments can be performed using customized building simulations, it is generally not practical to develop and validate detailed building simulation code to cover all the potential variations of equipment design and installation. An alternative, and somewhat simpler, approach is to solely use detailed building simulations to generate time series of heating and cooling loads in commercial buildings. These loads can then be used as input to more detailed, stand-alone engineering models that simulate HVAC system performance under different equipment designs. This approach was used to evaluate a range of high-efficiency commercial packaged air conditioner design options for the Department of Energy’s Appliance and Equipment Standards Program (DOE-EERE 2015). While there may be some loss of precision relative to full simulation, the accuracy of this approach is sufficient for practical applications of cost-benefit analysis. This report describes the development of a database of commercial building heating and cooling loads, generated using the EnergyPlus software package, a whole building energy use model supported by the Department of Energy (DOE-EERE 2020a). EnergyPlus takes as input a set of configuration files that describe the building itself (size, zoning, envelope characteristics, etc.) and the various systems within it (HVAC, lighting, water heating, etc.). This analysis uses a publicly available collection of commercial reference buildings (CRB), comprised of sixteen building types and three vintages (DOE-EERE 2020b; Deru et al 2011). Each building is simulated in eighteen different locations, covering a wide range of climatic conditions. The prototype building description files assign the type of HVAC equipment used, and capacities across climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

TDCOSMO - XVII. New time delays in 22 lensed quasars from optical monitoring with the ESO-VST 2.6m and MPG 2.2m telescopes

We present new time delays, the main ingredient of time delay cosmography, for 22 lensed quasars resulting from high-cadence r-band monitoring on the 2.6 m ESO VLT Survey Telescope and Max-Planck-Gesellschaft 2.2 m telescope. Each lensed quasar was typically monitored for one to four seasons, often shared between the two telescopes to mitigate the interruptions forced by the COVID-19 pandemic. The sample of targets consists of 19 quadruply and 3 doubly imaged quasars, which received a total of 1918 hours of on-sky time split into 21 581 wide-field frames, each 320 seconds long. In a given field, the 5-σ depth of the combined exposures typically reaches the 27th magnitude, while that of single visits is 24.5 mag – similar to the expected depth of the upcoming Vera-Rubin LSST. The fluxes of the different lensed images of the targets were reliably de-blended, providing not only light curves with photometric precision down to the photon noise limit, but also high-resolution models of the targets whose features and astrometry were systematically confirmed in Hubble Space Telescope imaging. This was made possible thanks to a new photometric pipeline, lightcurver, and the forward modelling method STARRED. Finally, the time delays between pairs of curves and their uncertainties were estimated, taking into account the degeneracy due to microlensing, and for the first time the full covariance matrices of the delay pairs are provided. Of note, this survey, with 13 square degrees, has applications beyond that of time delays, such as the study of the structure function of the multiple high-redshift quasars present in the footprint at a new high in terms of both depth and frequency. The reduced images will be available through the European Southern Observatory Science Portal.Key words: methods: data analysis / surveys / distance scale

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗

DOE OSTI2021

RISE: robust iterative surface extension for sub-nanometer X-ray mirror fabrication

Precision optics have been widely required in many advanced technological applications. X-ray mirrors, as an example, serve as the key optical components at synchrotron radiation and free electron laser facilities. They are rectangular silicon or glass substrates where a rectangular Clear Aperture (CA) needs to be polished to sub-nanometer Root Mean Squared (RMS) to keep the imaging capability of the incoming X-ray wavefront at the diffraction limit. The convolutional polishing model requires a CA to be extended with extra data, from which the dwell time is calculated via deconvolution. However, since deconvolution is very sensitive to boundary errors and noise, the existing surface extension methods can hardly fulfill the sub-nanometer requirement. On one hand, the figure errors in a CA were improperly modeled during the extension, leading to continuity issues along the boundary. On the other hand, uncorrectable high-frequency errors and noise were also extended. In this study, we propose a novel Robust Iterative Surface Extension (RISE) method that resolves these problems with a data fitting strategy. RISE models the figure errors in a CA with orthogonal polynomials and ensures that only correctable errors are fit and extended. Combined with boundary conditions, an iterative refinement of dwell time is then proposed to compensate the errors brought by the extension and deconvolution, which drastically reduces the estimated figure error residuals in a CA while the increase of total dwell time is negligible. To our best knowledge, RISE is the first data fitting-based surface extension method and is the first to optimize dwell time based on iterative extension. An experimental verification of RISE is given by fabricating two elliptic cylinders (10 mm × 80 mm CAs) starting from a sphere with a radius of curvature around 173 m using ion beam figuring. The figure errors in the two CAs greatly improved from 204.96 nm RMS and 190.28 nm RMS to 0.62 nm RMS and 0.71 nm RMS, respectively, which proves that RISE is an effective method for sub-nanometer level X-ray mirror fabrication.

36 MATERIALS SCIENCE↗

DOE OSTI2023

HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations

Publicly available collections of drug-like molecules have grown to comprise tens of billions of compounds due to advances in combinatorial chemistry. Traditional methods for identifying "hit" molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have the major drawback that they require exceptional computing capabilities for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently-proposed learning paradigm that represents data with high-dimension binary vectors; this allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encodings of a commonly-used molecular representation, the extended connectivity fingerprint (ECFP). We show that HDC-based inference methods are as much as 91 times more efficient than traditional machine learning methods, and achieve an acceleration of nearly nine orders of magnitude compared to molecular docking. Our results show that HDC accelerated methods retain competitive accuracy on a number of well-studied tasks such as molecular property predictions using the MoleculeNet dataset, and bind/no-bind activity classification using the DUD-E and LIT-PCBA datasets. Our work thus motivates further investigation into molecular representation learning to develop ultraefficient pre-screening tools.

Jones, William↗

DOE OSTI2021

Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of Ultraviolet–Visible Spectra

Optical spectroscopy is a powerful characterization tool with applications ranging from fundamental studies to real-time process monitoring. However, it can be difficult to apply to complex samples that contain interfering analytes which are common in processing streams. Multivariate (chemometric) analysis has been examined for providing selectivity and accuracy to the analysis of optical spectra and expanding its potential applications. Here we will discuss chemometric modeling with an in-depth comparison to more simplistic analysis approaches and outline how chemometric modeling works while exploring the limits on modeling accuracy. Understanding the limitations of the chemometric model can provide better analytical assessment regarding the accuracy and precision of the analytical result. This will be explored in the context of UV–Vis absorbance of neodymium (Nd 3+ ) in the presence of interferents, erbium (Er 3+ ) and copper (Cu 2+ ) under conditions simulating the liquid–liquid extraction approach used to recycle plutonium (Pu) and uranium (U) in used nuclear fuel worldwide. Finally, the selected chemometric model, partial least squares regression, accurately quantifies Nd 3+ with a low percentage error in the presence of interfering analytes and even under conditions that the training set does not describe.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Command of three-dimensional solitary waves via photopatterning

Multidimensional solitons are prevalent in numerous research fields. In orientationally ordered soft matter system, three-dimensional director solitons exemplify the localized distortion of molecular orientation. However, their precise manipulation remains challenging due to unpredictable and uncontrolled generation. Here, we utilize preimposed programmable photopatterning in nematics to control the kinetics of director solitons. This enables both unidirectional and bidirectional generation at specific locations and times, confinement within micron-scaled patterns of diverse shapes, and directed propagation along predefined trajectories. A focused dynamical model provides insight into the origins of these solitons and aligns closely with experimental observations, underscoring the pivotal role of anchoring conditions in soliton manipulation. Our findings pave the way for diverse fundamental research avenues and promising applications, including microcargo transportation and optical information processing.

Science & Technology - Other Topics↗

DOE OSTI2022

The potential of bacterial microcompartment architectures for phytonanotechnology

The application of nanotechnology to plants, termed phytonanotechnology, has the potential to revolutionize plant research and agricultural production. Advancements in phytonanotechnology will allow for the time-controlled and target-specific release of bioactive compounds and agrochemicals to alter and optimize conventional plant production systems. A diverse range of engineered nanoparticles with unique physiochemical properties is currently being investigated to determine their suitability for plants. Improvements in crop yield, disease resistance and nutrient and pesticide management are all possible using designed nanocarriers. However, despite these prospective benefits, research to thoroughly understand the precise activity, localization and potential phytotoxicity of these nanoparticles within plant systems is required. Protein-based bacterial microcompartment shell proteins that self-assemble into spherical shells, nanotubes and sheets could be of immense value for phytonanotechnology due to their ease of manipulation, multifunctionality, rapid and efficient producibility and biodegradability. Here in this review, we explore bacterial microcompartment-based architectures within the scope of phytonanotechnology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Low-Pressure Diffusion Bonding of Vanadium to Commercially Pure Titanium and Ti-6Al-4V

Modern technology increasingly requires components to be made from specific high-performance materials. To support complex multi-metal structures, a variety of techniques are required to join dissimilar metal parts with precise shapes. This study evaluates vacuum diffusion bonding as a method for joining titanium and vanadium, focusing on both pure titanium and the common alloy Ti-6Al-4V. We employ modest pressure provided by a weight and simple surface preparation to simulate the most commercially applicable process. Here, we show that Ti-6Al-4V alloy bonds readily to V, forming a continuous, clearly defined interdiffusion layer with predictable kinetics. Conversely, commercially pure Ti forms a crack-prone bond with V that fails to improve at higher bonding temperatures or longer times. We attribute this failure to the concentration of stress caused by the ß-to-α phase transformation in Ti. The contrasting bonding efficacy between pure Ti and the alloy provides insight into the crystallographic interactions that occur at the interface during bonding and cooling. These results provide surprising insight into a new method for bonding Ti alloys to V and possibly other metals that share the body-centered cubic crystal structure.

36 MATERIALS SCIENCE↗

DOE OSTI2022

The need for speed – Burnup determination of spent nuclear fuel

Here we present the application of a novel analytical approach for high-speed evaluation of burn-up in spent nuclear fuel. This automated online separation–direct analysis method, which simultaneously measures the isotopic compositions and concentrations of Nd, Pu, and U using isotope dilution-inductively coupled plasma mass spectrometry, enables the determination of burnup in as little as 4.5 h per sample with expanded uncertainties as low as 1.2% using as little as 1µg of fuel. We compare the determined burn-up values, sample requirements, and analysis times with an ultra-high precision, in-house developed, methodology which employs multiple analytical techniques to measure the isotopic compositions and concentrations of Nd, Pu, and U yielding burn-up values with expanded uncertainties as low as 0.49% at the 95% confidence level, the lowest reported to date.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

High-Efficiency Solar-To-Fuel Photoelectrochemistry in Disordered Photonic Glass Electrodes (Final Technical Report)

This project investigated how photonic glass (PG) photoelectrodes—disordered arrangements of dielectric scatterers—can serve as scalable, tunable platforms for light trapping in photoelectrochemical (PEC) solar-to-fuel systems. By leveraging disorder-driven optical phenomena such as multiple scattering resonances and light localization, PG structures offer an alternative to conventional photonic crystals and inverse opals that require high structural precision. The scientific goals were to twofold: (1) develop approaches to predictive models for high performance PG electrodes based on light absorption simulations, and (2) fabricate, characterize, and optimize PG-based photoelectrodes for solar-to-hydrogen and solar-to-fuel photoelectrochemical applications. To overcome the complexity of ensemble optical simulations for disordered materials, the researchers developed a machine-learning-accelerated emulation of all configurations in the design space. With this approach, PG photoelectrodes based on a TiO2 semiconductor were designed to enhance PEC currents of up to one hundred times higher than the equivalent ultra-thin film photoanodes and several times higher than the equivalent photonic crystal. The research also explored integrated systems for electrochemical hydrogen production based on replacing water oxidation with the specific glycerol oxidation electrocatalysis. Overall, the project outlined an approach to a simple-to-fabricate photoelectrode system to drive photoelectrochemical reactions relevant to solar photochemical energy conversion.

14 SOLAR ENERGY↗

DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Development of X-ray Wavefront Sensing Techniques for Adaptive Optics Control at the Advanced Photon Source

The planning and construction of new and upgraded high-brightness X-ray synchrotron and free electron laser sources, such as the Advanced Photon Source upgrade project (APS-U), are driving numerous opportunities to advance X-ray science and technologies. At the same time, an increasing number of highly diverse beamline experiments demand wavefront-preserving adaptive X-ray optics with both high precision and flexibility. At the APS, significant effort has been devoted to developing next-generation adaptive and corrective optics combined with state-of-the-art at-wavelength wavefront sensing techniques and an intelligent feedback control system for the automation and self-alignment of beamline optical systems. This article reviews recent achievements in these areas at the APS. Further, these include the development of in-situ wavefront sensing, the application in active feedback control of ultra-precision deformable mirrors, and the exploration of non-invasive wavefront sensing techniques for adaptive optics and beamline diagnostics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Beyond the Hype: An Evaluation of Commercially Available Machine-Learning-Based Malware Detectors

There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). Consequently, efficacy of malware detectors is opaque, inhibiting end users from making informed decisions and researchers from targeting gaps in current detectors. In this paper, we present a scientific evaluation of four prominent commercial malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously and never-before-seen files? Is purchasing a network-level malware detector worth the cost? To investigate, we tested each tool against 3,536 total files (2,554 or 72% malicious, 982 or 28% benign) of a variety of file types, including hundreds of malicious zero-days, polyglots, and APT-style files, delivered on multiple protocols. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of the recent cost-benefit evaluation procedure of Iannacone & Bridges. Although the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool might still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files—37% of malware, including all polyglot files, were undetected. Priorities for researchers and takeaways for end users are given. Code for future use of the cost model is provided.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Simulating the X-ARAPUCA, DUNE's next generation light sensors

The word arapuca refers to a bird trap built by the indigenous peoples of Brazil. On the other hand, our ARAPUCA is a photon trap for Noble Gases' scintillation light and it is the sensitive element upon which DUNE's whole photon detection system is based on. Here we present a glimpse of our state-of-the-art simulation of such device, the ArapucaSim, highlighting its philosophy and main components. As an application, we present here one of the studies being carried out, on the effects of light reflections inside the light guide and how affects the overall photon detection efficiency. Based on these results, we are able to not only acquire a better understanding of the device but also impose limits on the quality of the mechanical polishing of such surfaces. The ArapucaSim was developed with two main goals in mind: to assist in R&D by allowing short turnout times between new ideas and experimental tests, and to bridge the gap between high-precision time-consuming hardware simulations and the full-scale models for DUNE's Photon Detection System. This research also led to the development new algorithms for simulating wavelength shifting/scintillation processes and dichroic filters, which will be properly described in future publications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗