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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Toward Thermally Stimuli-Responsive Polymeric Vesicles Fabricated by Block Copolymer Blends for Nanocarriers

Polymeric vesicles, characterized by enhanced colloidal stability, excellent mechanical properties, controllable surface functionality, and adjustable membrane thickness, are extremely useful in nano- and bio-technology for potential applications as nanosized carriers for drugs and enzymes. However, a few preparative steps are necessary to achieve a unilamellar vesicle with a narrow size distribution. Herein, we report the spontaneous formation of unilamellar polymeric vesicles with nanometer sizes (<50 nm), fabricated by simply mixing diblock copolymers (P(EO-AGE)(2K-2K) and P(EO-AGE)(0.75K-2K)) with differing hydrophilic mass fractions in aqueous solutions. Depending on the mixing ratio of block copolymers and the temperature, the block copolymer mixtures self-assemble into various nanostructures, such as spherical and cylindrical micelles, or vesicles. The self-assembled structures of the block copolymer mixtures were characterized by small-angle neutron scattering, resulting in a phase diagram drawn as a function of temperature and the mixing condition. Notably, the critical temperature for the micelle-to-vesicle phase transition can be easily controlled by altering the mixing conditions; it decreases with an increase in the concentration of one of the block copolymers.

block copolymers↗
DOE OSTI2025

Uses of H 2 and CO 2 gases in hydrometallurgical processes: Potential towards sustainable pretreatment, metal production and carbon neutrality

Hydrometallurgical practices represent a vital area of focus within the metallurgical sector, and it is critical that these practices are carried out in an environmentally friendly manner in order to promote sustainability and responsibility. One promising approach is the use of hydrogen gas (H 2 ), known for its ‘green’ properties, along with the pollutant carbon dioxide CO 2 ), at various stages within the hydrometallurgical field. This review provides a detailed overview of the utilization of H 2 and CO 2 gases in the hydrometallurgical field, encompassing key stages including pretreatment, leaching, and metal recovery. Theoretical underpinnings are also provided for each stage, along with an overview of cutting-edge approaches such as application of supercritical CO 2 . Challenges and future directions associated with H 2 and CO 2 utilization are also discussed. This review serves an important step towards advancing sustainable and responsible hydrometallurgical processes in the pursuit of carbon neutrality.

36 MATERIALS SCIENCE↗
DOE OSTI2025

FLAMES─Fast, Low-Storage, Accurate, and Memory-Efficient Adaptive Sampling─Approach to Resolve Spatially Dependent Dynamics of Molecular Liquids

Many critical phenomena in soft matter occur at large length scales, necessitating the resolution of their structure and dynamics at low wavenumbers. However, resolving wavenumber-dependent dynamics computationally via molecular dynamics simulations presents significant challenges, as these phenomena span several orders of magnitude in both time and length scales, resulting in high computational costs and memory demands. Here, this work highlights the computational and memory challenges associated with analyzing molecular trajectories in reciprocal space and demonstrates a method to address them. We introduce FLAMESFast, Low-storage, Accurate, and Memory-Efficient adaptive Sampling, which is a direct method for calculation of structure factors, allowing us to select only the required number of wavevectors for binning. We also use wavenumber-dependent time steps to extract dynamics. Our FLAMES approach effectively mitigates computational and memory/storage bottlenecks. We demonstrate the method using simulations of a model system, liquid octane, at various temperatures. Comparisons with experimental data and real space computation show that the FLAMES technique achieves high accuracy in resolving temperature- and spatially dependent dynamics while being significantly more computationally efficient and requiring less memory and storage than methods based on a uniform wavevector grid and fixed temporal spacing.

Chen, Guang [Argonne National Laboratory (ANL), Ar↗
DOE OSTI2024

Commissioning of the Mu2e tracker DAQ, planning for the Vertical Slice Test and pre-pattern recognition studies

The primary objective of the Mu2e experiment at Fermilab is to search for the neutrino-less coherent $\mu \rightarrow e$ conversion in the field of an aluminum nucleus ($\mu^- \text{Al} \rightarrow e^- \text{Al}$). The signature of this process is a monochromatic Conversion Electron (CE) with an energy of approximately 104.97 MeV \cite{bartoszek2015mu2e}. Within the Standard Model (SM), the branching ratio for this process, including neutrino masses and oscillation, is expected to be less than $\mathcal{O}(10^{-50})$. This value is far beyond current experimental capabilities. However, models of physics beyond the SM predict much higher relative rates, approaching an observable level. The SINDRUM II experiment set an upper limit on muon conversion at $7 \times 10^{-13}$ (90\% CL) on Au target \cite{SINDRUMII:2006dvw}, and the Mu2e collaboration aims to improve this limit by four orders of magnitude. Observing this process would provide a clear evidence of physics beyond the Standard Model. A brief discussion of the theoretical and experimental aspects is provided in Chapter \ref{intr}. Mu2e adopts a sophisticated experimental setup to achieve its goals, further described in Chapter \ref{mu2echapter}. The central part of the Mu2e detector is the tracker, that consists of 18 tracking stations. The tracker must provide excellent momentum resolution, approximately 1 MeV/c, to distinguish the monochromatic CE signal from the background. To minimize the energy losses, a straw tube tracker will be used \cite{bobbb}. Chapter \ref{chaptertrk} provides an overview of the straw tracker design and its working principles. This Thesis presents a comprehensive study of the Mu2e tracker, covering complementary aspects from initial commissioning to optimization and first steps of the calibration processes. My work at Fermilab has been focused on the complete Data Acquisition (DAQ) testing from both hardware and software perspectives. I was involved in the commissioning of the Mu2e DAQ system and the Vertical Slice Test (VST) of the tracker. The VST encompasses the entire testing chain, from the straws to the readout, and to processed data on disk. I was also focused on the offline analysis, especially on pre-pattern recognition studies, to explore the best methods for identifying $\delta$-electrons during the data taking. Chapter \ref{commissioning} details the commissioning of the tracker DAQ system, emphasizing the importance of understanding of the readout process before the data acquisition. This includes validating the readout logic and firmware through Monte Carlo simulations to confirm functionality and buffering, monitoring the quality of the data from the tracker preamplifiers and front-end electronics, and assessing overall DAQ performance to ensure reliability during future calibration and data-taking. Chapter \ref{planning} discusses the initial steps towards the tracker calibration. The ultimate goal is to perform a time calibration of the first assembled station of the tracker using cosmic muons, aiming for a longitudinal hit position resolution better than 4 cm. This involves determining the signal propagation times and channel-to-channel delays. I performed a Monte Carlo study to determine the impact of the station orientation on the quality of the calibration, in particular on the cosmic track reconstruction, focusing on potential biases that could arise. These studies provide essential insights into the operation, optimization, and calibration of the Mu2e tracker system. Given the high data volume expected during Mu2e operations, estimated at approximately 7 PBytes per year, optimizing memory usage and minimizing CPU consumption are critical. A significant challenge lies in effectively flagging $\delta$-electron hits, which are the primary source of hits in the tracker, without compromising the efficiency of CE hit detection and track reconstruction. A detailed study of pre-pattern recognition and a thorough comparison of two $\delta$-electron flagging algorithms is provided in Chapter \ref{delta}. In Chapter \ref{conclusions}, the findings are concisely summarized, offering a comprehensive synthesis of the research and emphasizing the key insights derived from this study.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

A self-healing plastic ceramic electrolyte by an aprotic dynamic polymer network for lithium metal batteries

Abstract Oxide ceramic electrolytes (OCEs) have great potential for solid-state lithium metal (Li 0 ) battery applications because, in theory, their high elastic modulus provides better resistance to Li 0 dendrite growth. However, in practice, OCEs can hardly survive critical current densities higher than 1 mA/cm 2 . Key issues that contribute to the breakdown of OCEs include Li 0 penetration promoted by grain boundaries (GBs), uncontrolled side reactions at electrode-OCE interfaces, and, equally importantly, defects evolution (e.g., void growth and crack propagation) that leads to local current concentration and mechanical failure inside and on OCEs. Here, taking advantage of a dynamically crosslinked aprotic polymer with non-covalent –CH 3 ⋯CF 3 bonds, we developed a plastic ceramic electrolyte (PCE) by hybridizing the polymer framework with ionically conductive ceramics. Using in-situ synchrotron X-ray technique and Cryogenic transmission electron microscopy (Cryo-TEM), we uncover that the PCE exhibits self-healing/repairing capability through a two-step dynamic defects removal mechanism. This significantly suppresses the generation of hotspots for Li 0 penetration and chemomechanical degradations, resulting in durability beyond 2000 hours in Li 0 -Li 0 cells at 1 mA/cm 2 . Furthermore, by introducing a polyacrylate buffer layer between PCE and Li 0 -anode, long cycle life >3600 cycles was achieved when paired with a 4.2 V zero-strain cathode, all under near-zero stack pressure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Spherical tokamak physics research in preparation for the operation of NSTX-U

The National Spherical Torus Experiment Upgrade (NSTX-U) is preparing to resume operation, representing a crucial step toward realizing compact, cost-effective fusion pilot plants. In advance of this, extensive modeling and data analysis have been conducted to advance the physics basis for low-aspect-ratio, high-performance plasma regimes, focusing on three core objectives: confinement and stability, power and particle handling, and steady-state operation. Significant progress has been made in understanding the electron temperature flattening in high-β plasmas, which is shown to be driven by a complex interplay of magnetohydrodynamic instabilities (e.g. non-resonant infernal modes), fast-ion-driven Alfvén eigenmodes, and electron and ion-scale micro-instabilities, particularly Kinetic Ballooning Modes (KBMs), whose destabilization is strongly dependent on parallel magnetic field fluctuations (δB ∥ ). Furthermore, a new gyrokinetic critical pedestal model was developed, accurately predicting pedestal structure by identifying KBMs as the primary stability limit, offering a critical constraint for future high-confinement scenarios. To address the challenge of high heat flux, novel liquid lithium plasma-facing components were modeled. The analysis confirmed that lithium vapor shielding is a self-regulating mechanism for heat mitigation, while also emphasizing that strong main ion parallel flow is essential to minimize core lithium contamination. Finally, progress toward steady-state operation was anchored by developing the required physics basis and control tools. This includes predictive modeling for reversed magnetic shear sustainment, demonstrating that magnetic island-induced bootstrap current reduction is negligible in STs, and advancing real-time control and disruption avoidance capabilities. The development of high-speed surrogate models (e.g. MMMNet) provides computationally efficient tools vital for non-inductive scenario optimization and integrated, low-disruptivity operations planned for NSTX-U.

NSTX-U↗
DOE OSTI2022

A scanning-to-incision switch in TFIIH-XPG induced by DNA damage licenses nucleotide excision repair

Nucleotide excision repair (NER) is critical for removing bulky DNA base lesions and avoiding diseases. NER couples lesion recognition by XPC to strand separation by XPB and XPD ATPases, followed by lesion excision by XPF and XPG nucleases. Here, we describe key regulatory mechanisms and roles of XPG for and beyond its cleavage activity. Strikingly, by combing single-molecule imaging and bulk cleavage assays, we found that XPG binding to the 7-subunit TFIIH core (coreTFIIH) stimulates coreTFIIH-dependent double-strand (ds)DNA unwinding 10-fold, and XPG-dependent DNA cleavage by up to 700-fold. Simultaneous monitoring of rates for coreTFIIH single-stranded (ss)DNA translocation and dsDNA unwinding showed XPG acts by switching ssDNA translocation to dsDNA unwinding as a likely committed step. Pertinent to the NER pathway regulation, XPG incision activity is suppressed during coreTFIIH translocation on DNA but is licensed when coreTFIIH stalls at the lesion or when ATP hydrolysis is blocked. Moreover, ≥15 nucleotides of 5'-ssDNA is a prerequisite for efficient translocation and incision. Our results unveil a paired coordination mechanism in which key lesion scanning and DNA incision steps are sequentially coordinated, and damaged patch removal is only licensed after generation of ≥15 nucleotides of 5'-ssDNA, ensuring the correct ssDNA bubble size before cleavage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Efficient Contingency Analysis in Power Systems via Network Trigger Nodes

Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such ’what-if’ scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k ‘trigger’ components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.

Tabassum, Anika↗
DOE OSTI2021

Developing a strategy for the national coordinated soil moisture monitoring network

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Numerical modeling of chemical looping oxidative dehydrogenation of ethane in parallel packed beds

Chemical looping oxidative dehydrogenation (CL-ODH) of ethane has the potential to be a highly efficient alternative to steam cracking for ethylene production. Accurate reactor modeling is of critical importance to efficiently scale up and optimize this new technology. This study reports a one-dimensional, heterogeneous packed bed model to simulate the CL-ODH of ethane to ethylene with a Na 2 MoO 4 -promoted CaTi 0.1 Mn 0.9 O 3 redox catalyst. Here, the overall reaction kinetics was well-described by coupling the gas-phase steam cracking of ethane with the reduction kinetics of the redox catalyst by H 2 and C 2 H 4 . The impact of H 2 on the formation rate of CO 2 byproduct from C 2 H 4 conversion was also thoroughly investigated to validate the applicability of the kinetic model under operational environments. The temperature variation within the different CL-ODH steps and the temperature distribution along the bed were also carefully considered. The accuracy of the model was validated by experiments conducted in a large lab-scale packed bed reactor (200 g catalyst loading), with an average deviation of 2.8% in terms of ethane conversion and ethylene yield. The model was subsequently used to optimize the operating parameters of the CL-ODH reactor, indicating that up to 63.7% single-pass C 2 +olefin yield can be achieved with the current redox catalyst bed whereas further optimization of the redox catalyst to inhibit C 2 H 4 activation can result in 69.4% single-pass C 2 +yield while maintaining low CO 2 selectivity.

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

Measurement of post-disruption runaway electron kinetic energy and pitch angle during final loss instability in DIII-D

Post-disruption runaway electron (RE) kinetic energy K and pitch angle sin$\vartheta$ are critical parameters for determining resulting first wall material damage during wall strikes, but are very challenging to measure experimentally. During the final loss instability, confined RE K and sin$\vartheta$ are reconstructed during center-post wall strikes for both high impurity (high-Z) and low impurity (low-Z) plasmas by combining soft x-ray, hard x-ray, synchrotron emission, and total radiated power measurements. Deconfined (wall impacting) RE sin$\vartheta$ is then reconstructed for these shots by using time-decay analysis of infra-red imaging. Additionally, deconfined RE K and sin$\vartheta$ are reconstructed for a low-Z downward loss shot by analyzing resulting damage to a sacrificial graphite dome limiter. The damage analysis uses multi-step modeling simulating plasma instability, RE loss orbits, energy deposition, and finally material expansion (MARS-F, KORC, GEANT-4, and finally COMSOL). Overall, mean kinetic energies are found to be in the range $\langle$$K$$\rangle$ $≈ 3-4$ MeV for confined REs. KORC simulations indicate that the final loss instability process does not change individual RE kinetic energy K. In conclusion, confined RE pitch angles are found to be fairly low initially pre-instability, $\langle$sin$\vartheta$$\rangle$ $≈ 0.1 – 0.2$, but appear to increase roughly 2x, to $\langle$sin$\vartheta$$\rangle$ $≈ 0.3 – 0.4$ for both confined and deconfined REs during instability onset in the low-Z case; this increase is not observed in the high-Z case.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Candidacy Renewal Report for ICERR Designation

The International Atomic Energy Agency (IAEA) has established a designation for an International Centre based on Research Reactors (ICERR). The intention of this designation is to provide a vehicle for IAEA member states to access international research-reactor and ancillary nuclear research and development infrastructure. The U.S. Department of Energy (DOE) has made a commitment to world leadership in the development of advanced nuclear energy, science, and technology. To this end, DOE has established programs and initiatives to enhance this leadership role. ICERR designation constitutes an important step in achieving the DOE vision. DOE, represented by Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL), is submitting this application for re-designation as an IAEA ICERR. Both INL and ORNL have a decades-long and storied history that supports nuclear research, development, and deployment both nationally and internationally. Both have a history of safe and efficient nuclear operations and have demonstrated a track record of international collaboration and cooperation. INL and ORNL are home to two primary and two secondary research reactors. The primary reactors are Advanced Test Reactor at INL, and High-Flux Isotope Reactor at ORNL. Secondary reactors include: Neutron Radiography Reactor at INL, Transient Reactor Test (TREAT) Facility at INL, and INL Advanced Test Reactor: Critical Facility (ATRC). ICERR designation will also include numerous ancillary facilities at both laboratories, including the following: Multiple post-irradiation examination facilities at both INL and ORNL, Radiochemistry hot-cell facilities at ORNL, INL nuclear-fuel-fabrication and materials-science facilities, and INL and ORNL radiological and nuclear analytical capabilities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

SOLAr Critical Infrastructure Energization (SOLACE): Leveraging Distributed Energy Resources to Provide Local Power

This document is a technical report based on a large-scale DOE-funded project conducted between 2019 and 2022. The project, called SOLAr Critical infrastructure Energization (SOLACE), is aimed at leveraging distributed energy resources (DERs) to provide local power to communities, feeders, or other regions. The project included a particular focus on high-value critical loads such as municipal water supply, telecommunication hubs, and disaster shelters. The key developments of the project were: (1) A comprehensive pre-event power system analysis methodology that identifies and characterizes the viable local power options. (2) A DMS-based example control system for activating and operating the local power solution during a time of crisis. (3) Grid-forming inverter technology to enable isolated local power operation and black-starting. (4) Cyber-security considerations for isolated systems when wider-area communication may be offline. This report describes the overall process that identifies and assesses the viable DER-based local power solutions for a given facility, region, or community. The report documents the process, including the key analysis steps, tools required, data requirements, and recommended pass/fail criteria for each step. It also provides a sample implementation of this process through a test case. Finally, it provides details of how a viable pre-event plan can be selected and activated at go-time when an event has occurred.

14 SOLAR ENERGY↗
DOE OSTI2023

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Protocol for Engineered Compositional Asymmetry Within Nanodiscs

Membrane proteins remain the most challenging targets for structural characterization, yet their elucidation provides valuable insights into protein function, disease mechanisms, and drug specificity. Structural biology platforms have advanced rapidly in recent years, notably through the development and implementation of nanodiscs—discoidal lipid–protein complexes that encapsulate and solubilize membrane proteins within a controlled, native-like environment. While nanodiscs have become powerful tools for studying membrane proteins, faithfully reconstituting the compositional asymmetry intrinsic to nearly all biological membranes has not yet been achieved. Proper membrane leaflet lipid distribution is critical for accurate protein folding, stability, and insertion. Here, we share a protocol for reconstituting tailored compositional asymmetry within nanodiscs through membrane extraction from giant unilamellar vesicles (GUVs) treated with a leaflet-specific methyl-β-cyclodextrin (mβCD) lipid exchange. Nanodisc asymmetry is verified through a geometric approach: biotin-DPPE-preloaded mβCD engages in lipid exchange with the outer leaflet of POPC GUVs solubilized by the lipid-free membrane scaffold protein (MSP) Δ49ApoA-I to form nanodisc structures. Once isolated, nanodiscs are introduced to the biotin-binding bacterial protein streptavidin. High-speed atomic force microscopy imaging depicts nanodisc–dimer complexes, indicating that biotin-DPPE was successfully reconstituted into a single leaflet of the nanodiscs. This finding outlines the first step toward engineering tailored nanodisc asymmetry and mimicking the native environment of integral proteins—a potentially powerful tool for accurately reconstituting and structurally analyzing integral membrane proteins whose functions are modulated by lipid asymmetry.

Biological and medical sciences↗
DOE OSTI2020

Mitigating the Impact of Thermal Binder Removal for Direct Li-Ion Battery Recycling

Li-ion battery recycling will become critical to the management of end-of-life batteries from electric vehicles. Currently, it is a challenge to create a profitable recycling process, which is made more difficult by the reduction in cathode cobalt content. Maintenance of the cathode structure throughout the recycling process can yield increased revenues that may make recycling profitable. This method will require careful removal of the PVDF binder and carbon black, which can be achieved through thermal processing. Furthermore, it is necessary to mitigate the effects of the fluorine from the PVDF on the cathode material. Herein, we report a process that utilizes excess LiOH•H 2 O to react with this fluorine and thereby prevent lithium removal and doping of the cathode material. In addition, we demonstrate a one-step thermal process that can both remove the binder and relithiate the cathode material.

25 ENERGY STORAGE↗
DOE OSTI2023

Metal oxide barrier layers for terrestrial and space perovskite photovoltaics

Perovskite photovoltaics are attractive for both terrestrial and space applications. Although terrestrial conditions require durability against stressors such as moisture and partial shading, space poses different challenges: radiation, atomic oxygen, vacuum and high-temperature operation. Here we demonstrate a silicon oxide layer that hardens perovskite photovoltaics to critical space stressors. A 1-um-thick silicon oxide layer evaporated atop the device contacts blocks 0.05 MeV protons at fluences of 10 15 cm -2 without a loss in power conversion efficiency, which results in a device lifetime increase in low Earth orbit by x20 and in highly elliptical orbit by x30. Silicon-oxide-protected Cs 0.05 (MA 0.17 FA 0.83 ) 0.95 Pb(I 0.83 Br 0.17 ) 3 ) (MA, methylammonium; FA, formamidinium cation) and CsPbI 2 Br cells survive submergence in water and N,N-dimethylformamide. Furthermore, moisture tolerance of Sn-Pb and CsPbI 2 Br devices is boosted. Devices are also found to retain power conversion efficiencies on exposure to alpha irradiation and atomic oxygen. So, this barrier technology is a step towards lightweight packaging designs for both space and terrestrial applications.

14 SOLAR ENERGY↗
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗