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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Photodriven electron-transfer dynamics in a series of heteroleptic Cu(I)–anthraquinone dyads

Solar fuels catalysis is a promising route to efficiently harvesting, storing, and utilizing abundant solar energy. To achieve this promise, however, molecular systems must be designed with sustainable components that can balance numerous photophysical and chemical processes. To that end, here we report on the structural and photophysical characterization of a series of Cu(I)–anthraquinone-based electron donor–acceptor dyads. The dyads utilized a heteroleptic Cu(I) bis-diimine architecture with a copper(I) bis-phenanthroline chromophore donor and anthraquinone electron acceptor. We characterized the structures of the complexes using x-ray crystallography and density functional theory calculations and the photophysical properties via resonance Raman and optical transient absorption spectroscopy. The calculations and resonance Raman spectroscopy revealed that excitation of the Cu(I) metal-to-ligand charge-transfer (MLCT) transition transfers the electron to a delocalized ligand orbital. The optical transient absorption spectroscopy demonstrated that each dyad formed the oxidized copper–reduced anthraquinone charge-separated state. Unlike most Cu(I) bis-phenanthroline complexes where increasingly bulky substituents on the phenanthroline ligands lead to longer MLCT excited-state lifetimes, here, we observe a decrease in the long-lived charge-separated state lifetime with increasing steric bulk. The charge-separated state lifetimes were best explained in the context of electron-transfer theory rather than with the energy gap law, which is typical for MLCT excited states, despite the complete conjugation between the phenanthroline and anthraquinone moieties.

14 SOLAR ENERGY↗
DOE OSTI2025

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Ion Gating in Nanopore Electrode Arrays with Hierarchically Organized pH-Responsive Block Copolymer Membranes

Inspired by biological ion channels, artificial nanopore-based architectures have been developed for smart ion/molecular transport control with potential applications to iontronics and energy conversion. Advances in nanofabrication technology enable simple, versatile construction methods, and post-fabrication functionalization delivers nanochannels with unique ion transport-control attributes. Here, we characterize a pH-responsive, charge-selective dual-gating block copolymer (BCP) membrane composed of polystyrene-b-poly(4-vinylpyridine) (PS 48400 -b-P4VP 21300 ), capable of self-organizing into highly ordered nanocylindrical domains. Because the PS-b-P4VP membrane exhibits pH-dependent structural transitions, it is suitable for designing intelligent pH-gated biomimetic channels, for example, exhibiting on–off transport switching at pH values near the pK a of P4VP with excellent anion permselectivity at pH < pK a . Introducing the BCP membrane onto nanopore electrode arrays (BCP@NEAs) allows the BCP to serve as a pH-responsive gate controlling ion transfer into the NEA nanopores. Such selectively transported and confined ions are detected by using a 100 nm gap dual-ring nanoelectrode structure capable of enhancing current output by efficient redox cycling with an amplification factor >10 2 . In addition, BCP@NEAs exhibit extraordinary pH-gated ion selectivity, resulting in a 3380-fold current difference between anion and cation probes at pH 3.0. Furthermore, this hierarchically organized BCP-gated NEA system can serve as a template for the development of other stimulus-responsive ion gates, for example, those based on temperature and ligand gating, thus exploiting the intrinsic advantages of NEAs, such as enhanced sensitivity based on redox cycling, which may lead to technological applications such as engineered biosensors and iontronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm 2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Composite-dimensional topological codes with boundaries and defects

We introduce new algorithms and provide example constructions of stabilizer models for the gapped boundaries, domain walls, and 0D defects of Abelian composite-dimensional twisted quantum doubles. Using the physically intuitive concept of condensation, our algorithm explicitly describes how to construct the boundary and domain-wall stabilizers starting from the bulk model. This extends the utility of Pauli stabilizer models in describing nontranslationally invariant topological orders with gapped boundaries. To highlight this utility, we provide a series of examples, including a new family of quantum error-correcting codes where the double of ℤ4 is coupled to instances of the double semion (DS) phase. We discuss the codes' utility in the burgeoning area of quantum error correction with an emphasis on the interplay between deconfined anyons, logical operators, error rates, and decoding. We also augment our construction, built using algorithmic tools to describe the properties of explicit stabilizer layouts at the microscopic lattice level, with dimensional counting arguments and macroscopic-level constructions building on pants decompositions. The latter outlines how such codes' representation and design can be automated. Our results are validated by a series of error-correcting threshold calculations comparing our codes' performance with that of standard surface codes. To do so, we introduce a composite-dimensional belief-propagation decoder with ordered statistics that utilizes combination sweeps. Going beyond our worked-out examples, we expect our explicit step-by-step algorithms to pave the path for higher-dimensional codes to be discovered and implemented in near-future architectures that take advantage of various hardware platforms.

Mousa, Mohamad [Purdue University]↗
DOE OSTI2021

Probing many-body localization on a noisy quantum computer

A disordered quantum system of interacting particles exhibits localized behavior when the disorder is large compared to the interaction strength. Studying this phenomenon on a quantum computer with no, or limited, error correction is challenging because even weak coupling to a thermal environment destroys most signatures of localization. Fortunately, spectral functions of local operators are known to contain features that can survive the presence of noise. In these spectra, discrete peaks and a soft gap at low frequencies compared to the thermal phase indicate localization. Here, we present the computation of spectral functions on a trapped-ion quantum computer for a one-dimensional Heisenberg model with disorder. Further, we design an error-mitigation technique which is effective at removing the noise from the measurement allowing clear signatures of localization to emerge as the disorder increases. Furthermore, we show that spectral functions can serve as a robust and scalable diagnostic of many-body localization on current and future generations of quantum computers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Controlling Interfacial Energetics and Charge Transfer Rates in 2D Semiconductors: Fundamental Studies en Route to Photoelectrochemical Energy Conversion Beyond the Shockley-Queisser Limit (Final Scientific/Technical Report)

Current photovoltaic and solar-to-fuel technologies do not fully utilize the energy of sunlight because excess photon energy above the semiconductor band gap is rapidly lost as heat through hot-carrier thermalization. Overcoming this loss mechanism is critical, as hot-carrier-based energy conversion systems are predicted to exceed the conventional efficiency limit of ~33%. This project advanced fundamental understanding of hot-carrier energy conversion in two-dimensional (2D) semiconductors, with a focus on monolayer MoS 2 . Using a combination of electrochemical microscopy and in situ ultrafast spectroscopic measurements, this research directly demonstrated hot-carrier extraction from monolayer MoS 2 photoelectrodes in proof-of-concept liquid junction solar cells. These measurements established that hot-carrier transfer can compete with ultrafast carrier cooling at solid–liquid interfaces, providing unambiguous experimental evidence that hot-carrier extraction is feasible in atomically thin semiconductors under operating photoelectrochemical conditions. Beyond demonstration, the project identified design rules for tuning hot-carrier extraction rates relative to cooling rates in 2D semiconductor photoelectrodes. The outcomes of this research provide foundational thermodynamic and kinetic insights for the rational design of next-generation hot-carrier-enabled solar energy conversion systems. These findings have broad implications for photoelectrochemical solar fuels production, electrocatalysis, and emerging energy conversion architectures that seek to harness nonequilibrium charge carriers for enhanced efficiency.

14 SOLAR ENERGY↗
DOE OSTI2020

Coral Gardens Reef, Belize: A refugium in the face of Caribbean-wide Acropora spp. coral decline

Caribbean Acropora spp. corals have undergone a decline in cover since the second half of the twentieth century. Loss of these architecturally complex and fast-growing corals has resulted in significant, cascading changes to the character, diversity, and available ecospaces of Caribbean reefs. Few thriving Acropora spp. populations exist today in the Caribbean and western North Atlantic seas, and our limited ability to access data from reefs assessed via long-term monitoring efforts means that reef scientists are challenged to determine resilience and longevity of existing Acropora spp. reefs. Here we used multiple dating methods to measure reef longevity and determine whether Coral Gardens Reef, Belize, is a refuge for Acropora cervicornis against the backdrop of wider Caribbean decline. We used a new genetic-aging technique to identify sample sites, and radiocarbon and high-precision uranium-thorium (U-Th) dating techniques to test whether one of the largest populations of extant A. cervicornis in the western Caribbean is newly established after the 1980s, or represents a longer-lived, stable population. We did so with respect for ethical sampling of a threatened species. Our data show corals ranging in age from 1910 ( 14 C) or 1915 ( 230 Th) to at least November 2019. While we cannot exclude the possibility of short gaps in the residence of A. cervicornis earlier in the record, the data show consistent and sustained living coral throughout the 1980s and up to at least 2019. We suggest that Coral Gardens has served as a refuge for A. cervicornis and that identifying other, similar sites may be critical to efforts to grow, preserve, conserve, and seed besieged Caribbean reefs.

58 GEOSCIENCES↗
DOE OSTI2025

Computational Power of Random Quantum Circuits in Arbitrary Geometries

Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of approximately 50 qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and, therefore, how much infidelity is suffered) in generating highly entangled states. Here, we describe recent hardware upgrades to Quantinuum’s H2 quantum computer, enabling it to operate on up to 56 qubits with arbitrary connectivity and 99.843(5)% two-qubit gate fidelity. We define a class of circuits with random geometries that become hard to classically simulate in very low depth and implement them utilizing the flexible connectivity of H2. A careful analysis demonstrating the fast saturation of classical simulation complexity with depth indicates that H2 can yield data well beyond the reach of state-of-the art classical simulation methods at unprecedented fidelities. We find that the considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the quantum charge-coupled device architecture as continued progress is made toward building larger machines. Published by the American Physical Society 2025

DeCross, M.↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2023

Bridging Time-series Image Phenotyping and Functional–Structural Plant Modeling to Predict Adventitious Root System Architecture

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional–structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R 2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Determining the Effects of Neutron Irradiation on the Structural Integrity of Additively Manufactured Heat Exchangers for Very Small Modular Reactor Applications, DOE Final Report (Project # 19-16980)

Auburn University (AU) teamed with the University of Missouri Research Reactor (MURR) and Kansas State University (KSU) to determine how to best use laser-powder bed fusion (L-PBF) additive manufacturing (AM) methods for generating radiation resistant nickel-based superalloys, Inconel alloy 625 and 718, for special purpose reactor (SPR) or very small modular reactor (vSMR) heat-exchangers (HeXs). Compact, conformal, and durable HeXs that are tolerant of extreme environments are needed for supporting the technical maturity of next-generation, portable compact reactors. AM is an enabler for realizing this new wave of HeXs – providing a means to make customizable hot and cold stream architectures with novel flow path geometries (e.g., tortuous channels with non-uniform, asymmetric cross-sections) and reduced layer-to-layer contact resistance (i.e., no separate bonding procedure required). AM further enables a more time/cost efficient means for fabricating SPRs by reducing the number of suppliers required for HeX assembly and allowing for on-site HeX fabrication. The project aim has been to better understand how neutron irradiation affects the microstructure and properties of additively manufactured nickel-based superalloys, to accelerate their safe, reliable use in the modular reactor industry. The major objective was to qualify/quantify the microstructure and microhardness of nickel-based superalloys (including Inconel 718 and 625) additively manufactured via the L-PBF process in the neutron-dosed (irradiated) and non-irradiated states over a course of 3 years. Effects of build orientation during L-PBF and post-AM heat treatments on neutron resistance, microstructure and mechanical properties were also investigated. Neutron damage mechanisms via hardening were characterized. This project combined subject-matter experts in AM, mechanical/microstructure metallic part characterization, and neutron irradiation, as well as unique assets and capabilities at AU and MURR at MU, to ensure project results translated to effectively addressing known gaps in nuclear science and engineering. Parts were fabricated using L-PBF systems readily available at AU. Specimens were then irradiated using MURR facilities; a manipulator equipped hot cell was also used to measure material hardness after dosing. MURR, a 10 MW, light-water nuclear reactor, is the largest, highest powered, highest-flux university owned research reactor in the U.S. The major findings in this project provide evidence that AM can serve as an alternative way to build structural components for future advanced small modular reactors using advanced materials like Inconel 625 (IN625) or Inconel 718 (IN718). After full spectrum neutron irradiation, vertically as-built AM IN625 samples were observed to display better resistance towards radiation-induced-hardening defects relative to traditionally machined metals. A Vickers microhardness tester, using settings of 1 kgf and dwell time of 15 seconds per indentation, was used to measure hardness in this study. The as-built, vertically printed samples experienced 1.2% of radiation hardening vs. 5.25% radiation hardening observed in wrought IN625. Another set of IN625 and IN718 samples were exposed to fast neutron irradiation. It was observed that IN718 showed more resistance towards radiation hardening compared to IN625 samples indicating IN718 had a better performance. Results showed that the IN718 samples experienced less change (-2.5 to 3.24 %) in microhardness. On the other hand, IN625 samples underwent more (0.9 to 7.21%) change in microhardness after fast neutron irradiation. AM IN625 samples were irradiated using an ion (proton) beam in cyclotron. The mechanical properties of AM samples post irradiation were compared with wrought samples. The irradiated region on the samples were tested using nano-hardness indention. It was observed that the beam current and time used in this study generated an annealing effect and thus reduced the hardness of the samples. The sum of the project results provide precious insight into how one may minimize radiation hardening in AM materials while maintaining material property constraints. Results should assist engineers in selecting an appropriate heat treatment for AM nickel-based superalloys for increased radiation resistance. Results should increase confidence levels for adopting AM for building nuclear reactor components which perform the same or better than conventionally manufactured components. Fast neutron irradiation testing provided an accelerated means of obtaining radiation effects without making materials radioactive and difficult to handle.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Integrated Heat Exchanger-Phase Change Material Thermal Energy Storage System

The purpose of this study is to experimentally investigate the thermal performance of an innovative thermal energy storage (TES) system that combines the advantages of the phase-change material (PCM)/graphite foam latent heat TES medium developed at Argonne National Laboratory (Argonne) and the internally supported plate-fin (ISPE) cell architecture heat transfer fluid (HTF) flow channels developed at Brayton Energy (Brayton). Several essential tasks were accomplished: (1) Thermal property characterization. Thermal properties of the graphite foam were characterized, providing necessary data for experimental result analysis and numerical simulation. (2) Design and optimization of lab-scale test module. Based on Brayton’s full-scale heat exchanger (HX)-TES system, the experimental test module was designed, optimized, and fabricated. (3) Thermal performance testing and data analysis. Five cycle tests were successfully conducted—including one with approximately 3.5 psig of pressure applied to the diaphragms—to investigate the thermal performance of the experimental test module for charging and discharging. Temperature profiles were generated for each charging test and discharging test as a function of time. The temperature profiles clearly show three TES stages: sensible heat (temperature increase), latent heat (melting), and sensible heat (temperature increase) for the charging process. Similarly, the temperature profiles clearly show three thermal energy release stages: sensible heat (temperature decrease), latent heat (solidification), and sensible heat (temperature decrease). Melting and solidification of the PCM generally occurred in relatively narrow temperature ranges, indicated by the flattened temperature regions in the temperature profiles. These phase changes ranged approximately 3°C for melting and 3.5°C for solidification. The charging and discharging temperature profiles were similar for similar experimental parameter tests whether or not pressure was applied to the diaphragm to eliminate the gap between the HX surface and the TES subsystem. This indicates that the effect of a small gap between the HX surface and the TES subsystem is insignificant for charging and discharging. (4) Comparison of experimental data and simulation results. We compared the experimental data to the numerical simulation results. Numerical simulations were conducted by using the ANSYS FLUENT 2019 R3 commercial computational fluid dynamics software. The predicted phase-change times agreed reasonably well with those from the experimental data. In most cases, the estimated time differences between the relative phase changes were within 16%. The predicted start and end times for the charging process agreed well with those from the experimental data. However, the simulation results showed earlier start and end times than the experimental data for the discharging process. Overall, the experimental data and its comparison with the simulation predictions verified the technical viability of the integrated ISPF HX-PCM/graphite foam latent-heat TES system.

25 ENERGY STORAGE↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

ALD-Derived WO 3– x Leads to Nearly Wake-Up-Free Ferroelectric Hf 0.5 Zr 0.5 O 2 at Elevated Temperatures

Breaking the memory wall in advanced computing architectures will require complex 3D integration of emerging memory materials such as ferroelectrics─either within the back-end-of-line (BEOL) of CMOS front-end processes or through advanced 3D packaging technologies. Achieving this integration demands that memory materials exhibit high thermal resilience, with the capability to operate reliably at elevated temperatures, such as 125°C, due to the substantial heat generated by front-end transistors. However, silicon-compatible HfO 2 -based ferroelectrics tend to exhibit antiferroelectric-like behavior in this temperature range, accompanied by a more pronounced wake-up effect, posing significant challenges to their thermal reliability. Here, we report that by introducing a thin tungsten oxide (WO 3–x ) layer─known as an oxygen reservoir─and carefully tuning its oxygen content, ultrathin Hf 0.5 Zr 0.5 O 2 (5 nm) films can be made robust against the ferroelectric-to-antiferroelectric transition at elevated temperatures. This approach not only minimizes polarization loss in the pristine state but also effectively suppresses the wake-up effect, reducing the required wake-up cycles from 10 5 to only 10 at 125°C, a qualifying temperature for back-end memory integrated with front-end logic, as defined by the JEDEC standard. First-principles density functional theory (DFT) calculations reveal that WO 3 enhances the stability of the ferroelectric orthorhombic phase (o-phase) at elevated temperatures by increasing the tetragonal-to-orthorhombic phase energy gap and promoting favorable phonon mode evolution, thereby supporting o-phase formation under both thermodynamic and kinetic constraints.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Energy efficiency and thermal resilience analysis for row houses using representative building energy models

Row houses, a prevalent medium-density urban archetype, were popularized during industrial era to accommodate the working class in the U.S. cities. Today, many of these structures are deteriorating, exacerbating the energy cost for residents with limited economic resources. Moreover, many households that lack Air Conditioning (AC) are vulnerable to extreme summer heat, an issue expected to worsen in the future. Energy-efficient retrofits can help mitigate these challenges. However, current literature provides limited insight into how existing building energy codes impact the energy efficiency and thermal resilience of row houses in current and future weather scenarios. This study provides a detailed method to address this gap by employing calibrated representative energy models of row houses as a baseline, and evaluating the annual energy savings and summertime thermal safety improvements from code compliant retrofits under current and future typical weather conditions. Using Baltimore City as a case study, results show that under current weather, retrofitting row houses yields significant energy savings along with notable thermal resilience improvements. The end unit row house realizes higher energy savings, whereas the interior unit experiences greater thermal resilience improvement after retrofitting. In future, retrofitted row houses can still save energy, but residents will face numerous hours of fatal indoor temperatures without AC in summer. Additionally, findings reveal stark zone-wise variations in thermal safety, and elevated humidity risks in certain zones post-retrofit, emphasizing the need for zonal retrofit strategies and better natural ventilation strategies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗