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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Magnetic anisotropy and GGG substrate stray field in YIG films down to millikelvin temperatures

Abstract Quantum magnonics investigates the quantum-mechanical properties of magnons, such as quantum coherence or entanglement for solid-state quantum information technologies at the nanoscale. The most promising material for quantum magnonics is the ferrimagnetic yttrium iron garnet (YIG), which hosts magnons with the longest lifetimes. YIG films of the highest quality are grown on a paramagnetic gadolinium gallium garnet (GGG) substrate. The literature has reported that ferromagnetic resonance (FMR) frequencies of YIG/GGG decrease at temperatures below 50 K despite the increase in YIG magnetization. We investigated a 97 nm-thick YIG film grown on 500 μm-thick GGG substrate through a series of experiments conducted at temperatures as low as 30 mK, and using both analytical and numerical methods. Our findings suggest that the primary factor contributing to the FMR frequency shift is the stray magnetic field created by the partially magnetized GGG substrate. This stray field is antiparallel to the applied external field and is highly inhomogeneous, reaching up to 40 mT in the center of the sample. At temperatures below 500 mK, the GGG field exhibits a saturation that cannot be described by the standard Brillouin function for a paramagnet. Including the calculated GGG field in the analysis of the FMR frequency versus temperature dependence allowed the determination of the cubic and uniaxial anisotropies. We find that the total crystallographic anisotropy increases more than three times with the decrease in temperature down to 2 K. Our findings enable accurate predictions of the YIG/GGG magnetic systems behavior at low and ultralow millikelvin temperatures, crucial for developing quantum magnonic devices.

Serha, Rostyslav O.↗
DOE OSTI2021

An overview of continuous and discrete phasor analysis of binned or time-gated periodic decays

Time-resolved analysis of periodically excited luminescence decays by the phasor method in the presence of time gating or binning is revisited. Analytical expressions for discrete configurations of square gates are derived and the locus of the phasors of such modified periodic single-exponential decays is compared to the canonical universal semicircle. The effects of IRF offset, decay truncation and gate shape are also discussed. Finally, modified expressions for the phase and modulus lifetimes are provided for some simple cases. A discussion of a modified phasor calibration approach is presented.

42 ENGINEERING↗
DOE OSTI2022

Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data

The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time.

17 WIND ENERGY↗
DOE OSTI2021

Quantum Sensing of Electron Transfer Pathways in Natural Photosynthesis Using Time-Resolved High-Field Electron Paramagnetic Resonance/Electron–Nuclear Double Resonance Spectroscopy

Photosynthetic integral membrane reaction center (RC) proteins capture and convert sunlight into chemical energy via efficient charge separation achieved through a series of rapid, photoinitiated electron transfer steps. These fast electron transfers create an entangled spin qubit (radical) pair that contains detailed information about the weak magnetic interactions, structure, and dynamics of localized protein environments involved in charge separation events. In this work, we investigate how these entangled electron spin qubits interact with nuclear spins of the protein environment using the high spectral resolution of 130 GHz electron paramagnetic resonance (EPR) and electron-nuclear double resonance (ENDOR). Spectroscopic interrogation enabled the observation and probing of protons located in the electron transfer pathway between the membrane-spanning electron pair P + Q A - (where P + is the primary donor, a special pair of bacteriochlorophylls, and Q A is the primary quinone acceptor) in the bacterial RC. Spectroscopic analysis reveals hydrogen-bonding interactions involved in regulating the route that light-induced electrons travel through the RC protein during charge separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

First Measurement of Inclusive Muon Neutrino Charged Current Triple Differential Cross Section on Argon

The field of accelerator neutrino experiments is entering an era of precision oscillation measurements where the remaining unknown neutrino measurements will be determined. The upcoming DUNE and Hyper-K experiments aim to determine the neutrino mass hierarchy and degree of Charge-Parity (CP) violation in the neutrino sector, providing potential insight on the matter-antimatter imbalance observed in the universe. However, these experiments require highly accurate measurements, and neutrino cross section modeling uncertainties may limit their capabilities. Cross section measurements at current- generation experiments can aid the development of neutrino interaction models to reduce these uncertainties. This is especially true for measurements of neutrino energy, as it drives neutrino oscillations and is of key importance to oscillation experiments. The MicroBooNE experiment uses a Liquid Argon Time Projection Chamber (LArTPC) to produce neutrino-argon cross sections as one of its physics goals. The MicroBooNE detector’s fully active volume, precision reconstruction, and calorimetry information are leveraged in the Wire-Cell analysis to produce a muon neutrino selection that is 92% pure while maintaining 68% efficiency. A reconstruction chain featuring a fully 3D charge reconstruction and a graph-based particle trajectory fit are used to produce accurate measurements of lepton kinematics as well as visible hadronic energy produced in a neutrino interaction. This thesis presents the first neutrino-argon triple-differential cross section measurement, targeting inclusive charged-current final states. Wiener SVD unfolding is used to produce a measurement over neutrino energy, muon momentum, and muon scattering angle. A series of constrained goodness of fit tests are used to demonstrate the validity of MicroBooNE’s model in describing the distribution of reconstructed kinematics seen in data to ensure the accuracy of unfolding. The validated unfolding to neutrino energy represents a step forward in the field of neutrino cross sections, and demonstrates the capabilities of the LArTPC detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

The value of in-reservoir energy storage for flexible dispatch of geothermal power

Geothermal systems making use of advanced drilling and well stimulation techniques have the potential to provide tens to hundreds of gigawatts of clean electricity generation in the United States by 2050. With near-zero variable costs, geothermal plants have traditionally been envisioned as providing “baseload” power, generating at their maximum rated output at all times. However, as variable renewable energy sources (VREs) see greater deployment in energy markets, baseload power is becoming increasingly less competitive relative to flexible, dispatchable generation and energy storage. Herein we conduct an analysis of the potential for future geothermal plants to provide both of these services, taking advantage of the natural properties of confined, engineered geothermal reservoirs to store energy in the form of accumulated, pressurized geofluid and provide flexible load-following generation. We develop a linear optimization model based on multi-physics reservoir simulations that captures the transient pressure and flow behaviors within a confined, engineered geothermal reservoir. We then optimize the investment decisions and hourly operations of a power plant exploiting such a reservoir against a set of historical and modeled future electricity price series. Further, we find that operational flexibility and in-reservoir energy storage can significantly enhance the value of geothermal plants in markets with high VRE penetration, with energy value improvements of up to 60% relative to conventional baseload plants operating under identical conditions. Across a range of realistic subsurface and operational conditions, our modeling demonstrates that confined, engineered geothermal reservoirs can provide large and effectively free energy storage capacity, with round-trip storage efficiencies comparable to those of leading grid-scale energy storage technologies. Optimized operational strategies indicate that flexible geothermal plants can provide both short- and long-duration energy storage, prioritizing output during periods of high electricity prices. Sensitivity analysis assesses the variation in outcomes across a range of subsurface conditions and cost scenarios.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Long-term socioeconomic trends and climate variability as drivers of virtual water scarcity in China

Water scarcity can have far-reaching sectoral impacts beyond its physical location through the propagation of virtual water flows. Socioeconomic and hydroclimatic changes affect local and virtual water scarcity by altering availability and demand. Yet most studies of this phenomenon focus on volumetric footprints, and the few on water scarcity risk have not examined hydroclimatic variability beyond long-term trends. In this study, we ask how gross domestic product (GDP) and population changes, long-term meteorological trends, sea surface temperature (SST) patterns, and interannual meteorological variability affect water scarcity in China, both locally (through the local water scarcity risk index, LWSR) and remotely (through the virtual water scarcity risk index, VWSR). Counterfactual scenarios were compared in a regression-and-simulation framework, with the socioeconomic and meteorological drivers varying over 1923–2019 and the multi-regional input–output structure staying fixed at 2017. Relative to a 5 year baseline centered on 2017, GDP and population changes have induced a cumulative 17%–50% increase in LWSR and 13%–21% increase in VWSR, outweighing the effect of long-term meteorological trends. phase change in one of two examined SST patterns induce 4%–13% differences in LWSR and 1%–4% differences in VWSR. Interannual meteorological variability induces 10%–20% standard deviations in LWSR and 3%–7% in VWSR. The findings highlight the importance of using longer time series to accurately assess local and virtual water scarcity situations. Water scarcity management should prioritize socioeconomic factors when planning at century-long timescales and prioritize hydroclimatic factors at multidecadal or shorter timescales. water managers should consider interannual variabilities in LWSR and VWSR and plan for potential occurrences of extreme conditions.

climate variability↗
DOE OSTI2022

Sampling-based Sublinear Low-rank Matrix Arithmetic Framework for Dequantizing Quantum Machine Learning

We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang’s breakthrough quantum-inspired algorithm for recommendation systems [STOC’19]. Motivated by quantum linear algebra algorithms and the quantum singular value transformation (SVT) framework of Gilyén et al. [STOC’19], we develop classical algorithms for SVT that run in time independent of input dimension, under suitable quantum-inspired sampling assumptions. Our results give compelling evidence that in the corresponding QRAM data structure input model, quantum SVT does not yield exponential quantum speedups. Since the quantum SVT framework generalizes essentially all known techniques for quantum linear algebra, our results, combined with sampling lemmas from previous work, suffice to generalize all prior results about dequantizing quantum machine learning algorithms. In particular, our classical SVT framework recovers and often improves the dequantization results on recommendation systems, principal component analysis, supervised clustering, support vector machines, low-rank regression, and semidefinite program solving. We also give additional dequantization results on low-rank Hamiltonian simulation and discriminant analysis. Our improvements come from identifying the key feature of the quantum-inspired input model that is at the core of all prior quantum-inspired results: ℓ 2 -norm sampling can approximate matrix products in time independent of their dimension. We reduce all our main results to this fact, making our exposition concise, self-contained, and intuitive.

Computer Science↗
DOE OSTI2026

Heavy-Duty Nonroad Material Handler Electrification Part 2: Energy Efficiency and Performance Evaluation

Decarbonization efforts achieved through electrification in nonroad mobile machinery can realize a reduction in fuel consumption of more than 20%, thanks to concepts familiar to light-duty passenger vehicles. This case study compares the results of a hybrid-electric material handler to its conventional counterpart, utilizing machine-specific drive cycles presented in part one of this paper series. The hybrid prototype features an extended-range electric vehicle (EREV) powertrain that demonstrated substantial energy efficiency improvements. Specifically, there was a reduction in equivalent fuel consumption of 75% when operating in electric-only mode, and 33% when maintaining the battery by charging with an on-board generator. Together, the efficiency improvements can be extrapolated over a low-intensity, 8-h shift characterized by significant idle time and highly dynamic engine load for a 47% reduction in net energy consumption. Key technologies that led to this improvement included engine downsizing and decoupling, regenerative braking, and an electrohydraulic pump unit with advanced controls. Finally, his study explains details of the powertrain architecture and subsystems that were implemented on a demonstration vehicle, control strategies used to meet project goals, and an analysis of energy consumption from testing on a closed course. Also included in this study is a discourse on comparison metrics that can be used for quantifying the energy consumption differences between hybrid-electric and conventional diesel powertrains in nonroad mobile machinery.

25 ENERGY STORAGE↗
DOE OSTI2021

Photocatalytic Transfer Hydrogenation in Water: Insight into Mechanism and Catalyst Speciation

Organometallic catalysts with strong metal-to-ligand charge-transfer (MLCT) optical transitions and reactive metal-hydride bonds provide opportunities for new excited-state reactivity. Toward this end, we have investigated photoinduced transfer hydrogenation reactions for a series of Cp*IrH complexes with functionalized bipyridyl ligands. Overcoming the baseline reactivity of this class of catalyst toward hydrogen evolution in aqueous media presents a major challenge for focusing this photochemical reactivity into desired transformations of more complex organic substrates. In this work, a photochemically induced aqueous ketone reduction catalyzed by [Cp*Ir(bpy)H][Cl] is reported and benchmarked to the competing hydrogen evolution reaction (HER). A detailed mechanistic study is conducted of the reduction of cyclohexanone with sodium formate delivering the reducing equivalent. Examining the kinetic profile of the cyclohexanone reduction revealed an unexpected induction period, suggesting a more complex underlying reaction mechanism where product selectivity was not simply a function of direct competition from a common excited-state reactive intermediate. The reaction kinetics were analyzed by variable time normalization analysis (VTNA), and the catalyst resting state was interrogated through operando light-emitting diode (LED) illumination H-1 NMR spectroscopy. Overall, these and other mechanistic studies provide insight into a photochemical reaction pathway to ketone reduction that is distinct from that previously reported for HER.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Green Needle Coke Production from Pyrolysis Biocrude toward Bio-based Anode Material Manufacture: Biochar Fines Addition Effect as “Physical Template” on the Crystalline Order

Here, a new method for producing green needle coke (GNC) is developed by replacing the “heavy fraction” of petroleum pitch delayed coking with fast pyrolysis biocrude. A series of alternative biocrude distillation, carbonization, and calcination conditions were investigated to determine the influence of these processing parameters onto the crystalline structure of the resulting graphitized material. For the first time, the addition of biochar fines was found to serve as a “physical template” to increase the graphitic nature of the final product. During the initial biocrude carbonization (350–450 °C), volatile compounds are released, and aromatics in pyrolysis biocrude experience condensation, resulting in GNC solids with carbon contents above 95 wt % and some early lamellar structure. In the second stage of the thermal process (25–1500 °C), there are additional thermal decomposition reactions with an increase in the aromatic nature of the graphitized solid. It was found that systematic addition of biochar fines induces a nucleating effect during the GNC development. Thermogravimetric analysis suggests that biochar fines promote polycondensation reactions by modifying the biopitch structure and molecular weight, while elemental analysis (CHN) shows a reduction in both H/C and O/C ratios which are consistent with the increase in aromaticity and removal of oxygenated compounds as thermal treatment evolves. The effects of different bio-based pitch materials (after distillation) and GNC intermediates were evaluated by pyrolysis-gas chromatography mass spectrometry and Fourier transform infrared, displaying slight changes on product yields and quality. X-ray diffraction patterns taken after graphitization evidence an increase in the graphitic order with the addition of biochar fines. Transmittance electron microscopy depicts an improvement on graphitic morphology as biochar fine content increases. The use of biochar fines showed a significant increase in graphitic ordering at addition levels above 0.01 wt %. These results show that thermally treated biocrude/biochar fine systems can produce graphitic structures (hard carbon-like) that might be suitable for the manufacture of sodium-ion batteries.

09 BIOMASS FUELS↗
DOE OSTI2022

Comparison of All Solid Cancer Mortality and Incidence Dose-Response in the Life Span Study of Atomic Bomb Survivors, 1958–2009

Recent analysis of all solid cancer incidence (1958–2009) in the Life Span Study (LSS) revealed evidence of upward curvature in the radiation dose response among males but not females. Upward curvature in sex-averaged excess relative risk (ERR) for all solid cancer mortality (1950–2003) was also observed in the 0–2 Gy dose range. As reasons for non-linearity in the LSS are not completely understood, we conducted dose response analyses for all solid cancer mortality and incidence applying similar methods (1958–2009 follow-up, DS02R1 doses, including subjects notin-city (NIC) at the time of the bombing) and statistical models. Incident cancers were ascertained from Hiroshima and Nagasaki cancer registries, while cause of death was ascertained from death certificates over entire Japan. The study included 105,444 LSS subjects who were alive and not known to have cancer before Jan 1, 1958 (80,205 with dose estimates and 25,239 NIC subjects). Between 1958 and 2009, there were 3.1 million person-years (PY) and 22,538 solid cancers for incidence analysis and 3.8 million PY and 15,419 solid cancer deaths for mortality analysis. We fitted sex-specific ERR models adjusted for smoking to both types of data. Over the entire range of doses, solid cancer mortality dose response exhibited a borderline significant upward curvature among males (P=0.062) and significant upward curvature among females (P=0.010); for solid cancer incidence, as before, we found a significant upward curvature among males (P=0.001) but not among females (P=0.624). The sex difference in magnitude of dose response curvature was statistically significant for cancer incidence (P=0.017) but not for cancer mortality (P=0.781). The results of analyses in the 0–2 Gy range and restricted lower dose ranges generally supported inferences made about the sex-specific dose response shape over the entire range of doses for each outcome. Patterns of sex-specific curvature by calendar period (1958–1987 vs 1988–2009) and age at exposure (0–19 vs 20–83) varied between mortality and incidence data, particularly among females, although for each outcome there was an indication of curvature among 0–19 year old male survivors in both calendar periods and among 0–19 year old female survivors in the recent period. Collectively, our findings indicate that the upward curvature in all solid cancer dose response in the LSS is neither specific to males nor to incidence data; it appears to depend on composition of case series and age at exposure or time. Further follow-up and site-specific analyses of cancer mortality and incidence will be important to confirm the emerging trend in dose response curvature among young survivors and unveil the contributing factors and sites.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

1 kV, 10-kW SiC-Based Quadruple Active Bridge DCX Stage in a DC to Three-Phase AC Module for Medium-Voltage Grid Integration

Interfacing low voltage dc to medium voltage three-phase ac grid is often based on series-stackable modular converter architectures. To minimize energy storage requirements, it is advantageous to employ a quadruple active bridge (QAB) stage operating as a "dc transformer" in each stackable module. The QAB stage offers three isolated dc link voltages, which then allow for flexible stacking of three single-phase dc-to-ac inverter stages. Each of the module phases processes a pulsating power having a component at twice the line frequency. This presents a challenge in maintaining zero voltage switching (ZVS) on the secondary sides of the QAB during low-power portions of the line cycle. This article is focused on the design of the QAB stage. A detailed analysis of ZVS switching waveforms is presented, including effects of nonlinear device capacitances. It is shown how ZVS can be achieved at all times using a relatively small circulating current provided by the magnetizing inductance of the high-frequency transformer. Analytical expressions are given for the optimal values of the magnetizing inductance and the dead times of the QAB primary and secondary bridges. This approach is verified by experimental results on a 1 kV, 10-kW SiC-based prototype, demonstrating a relatively flat efficiency curve with a peak efficiency of 97.1% at 75% load.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

The development of Nanosota-1 as anti-SARS-CoV-2 nanobody drug candidates

Combating the COVID-19 pandemic requires potent and low-cost therapeutics. We identified a series of single-domain antibodies (i.e., nanobody), Nanosota-1, from a camelid nanobody phage display library. Structural data showed that Nanosota-1 bound to the oft-hidden receptor-binding domain (RBD) of SARS-CoV-2 spike protein, blocking viral receptor angiotensin-converting enzyme 2 (ACE2). The lead drug candidate possessing an Fc tag (Nanosota-1C-Fc) bound to SARS-CoV-2 RBD ~3000 times more tightly than ACE2 did and inhibited SARS-CoV-2 pseudovirus ~160 times more efficiently than ACE2 did. Administered at a single dose, Nanosota-1C-Fc demonstrated preventive and therapeutic efficacy against live SARS-CoV-2 infection in both hamster and mouse models. Unlike conventional antibodies, Nanosota-1C-Fc was produced at high yields in bacteria and had exceptional thermostability. Pharmacokinetic analysis of Nanosota-1C-Fc documented an excellent in vivo stability and a high tissue bioavailability. As effective and inexpensive drug candidates, Nanosota-1 may contribute to the battle against COVID-19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Luminescent Bimetallic Two-Coordinate Gold(I) Complexes Utilizing Janus Carbenes

A series of bimetallic carbene-metal-amide (cMa) complexes have been prepared with bridging biscarbene ligands to serve as a model for the design of luminescent materials with large oscillator strengths and small energy differences between the singlet and triplet states (ΔE ST ). The complexes have a general structure (R 2 N)Au(:carbene—carbene:)Au(NR 2 ). The bimetallic complexes show solvation-dependent absorption and emission that is analyzed in detail. It is found that the molar absorptivity of the bimetallic complexes is correlated with the energy barrier to rotation of the metal–ligand bond. The bimetallic cMa complexes also exhibit short emission lifetimes (τ = 200–300 ns) with high photoluminescence efficiencies (Φ PL > 95%). The radiative rates of bimetallic cMa complexes are 3–4 times faster than that of the corresponding monometallic complexes. Analysis of temperature-dependent luminescence data indicates that the lifetime for the singlet state (τS 1 ) of bimetallic cMa complexes is near 12 ns with a ΔE ST of 40–50 meV. Here the presented compounds provide a general design for cMa complexes to achieve small values for ΔE ST while retaining high radiative rates. Solution-processed organic light-emitting devices (OLEDs) made using two of the complexes as luminescent dopants show high efficiency and low roll-off at high luminance.

14 SOLAR ENERGY↗