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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Phase Switching as the Origin of Large Piezoelectric Response in Organic-Inorganic Perovskites: A First-Principles Study

Piezoelectrics are critical functional components of many practical applications such as sensors, ultrasonic transducers, actuators, medical imaging, telecommunications. So far, the best performing piezoelectrics are ferroelectric ceramics, many of which are toxic, heavy, hard, and cost-ineffective. Recently, a groundbreaking discovery of extraordinarily large piezoelectric coefficients in the family of organic-inorganic perovskites gave a hope for a cheaper, environmentally friendly, inexpensive, light-weight and flexible alternative. However, the origin of such response in organic-inorganic ferroelectrics whose spontaneous polarization is an order of magnitude smaller than for inorganic counterparts remains unclear. In this study we employ first-principles simulations to predict that the mechanism associated with large piezoelectric constants is of extrinsic origin and associated with switching between the stable phase and previously overlooked energetically competitive metastable phase that can be stabilized by the external stress. Here, the phase switching changes the polarization direction and, therefore, gives origin to large piezoelectric response, similar to PbZr$_{1-x}$Ti$_x$O$_3$ near morphotropic phase boundary. Existence of such metastable phases is likely to manifest as the dynamical molecular disorder above the Curie temperature and, therefore, could be intrinsic to the entire family of organic-inorganic ferroelectrics with such disorder.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Chemistry↗
DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗
DOE OSTI2022

MXene-Based Porous Monoliths

In the past decade, a thriving family of 2D nanomaterials, transition-metal carbides/nitrides (MXenes), have garnered tremendous interest due to its intriguing physical/chemical properties, structural features, and versatile functionality. Integrating these 2D nanosheets into 3D monoliths offers an exciting and powerful platform for translating their fundamental advantages into practical applications. Introducing internal pores, such as isotropic pores and aligned channels, within the monoliths can not only address the restacking of MXenes, but also afford a series of novel and, in some cases, unique structural merits to advance the utility of the MXene-based materials. Here, a brief overview of the development of MXene-based porous monoliths, in terms of the types of microstructures, is provided, focusing on the pore design and how the porous microstructure affects the application performance.

2D nanomaterials↗
DOE OSTI2024

Time trend analysis of rare cancer incidence 2011–2018: Nationwide population-based cancer registries in Japan

Rare cancers collectively account for a significant proportion of the overall cancer burden in Japan. We aimed to describe and examine the incidence of each rare cancer and the temporal changes using the internationally agreed rare cancer classification. Cancer cases registered in regional population-based cancer registries from 2011 to 2015 and the National Cancer Registry (NCR) from 2016 to 2018 were classified into 18 families, 68 Tier-1 cancer groupings, and 216 single cancer entities based on the RARECAREnet list. Crude incidence rates and age-standardized incidence rates (ASR) were calculated for Tier-1 and Tier-2 cancers. The annual percent change and the 95% and 99% confidence limits for annual ASR for each of the 68 Tier-1 cancers were estimated using the log-linear regression of the weighted least squares method. The differences in ASRs between 2011 and 2018 were evaluated as an absolute change. A total of 5,640,879 cases were classified into Tier-1 and Tier-2 cancers. The ASRs of 18 out of 52 Tier-1 cancers in the rare cancer families increased, whereas the ASR for epithelial tumors of gallbladder decreased. The ASRs of 6 out of the 16 Tier-1 cancers in the common cancer families increased, whereas those of epithelial tumors of stomach and liver decreased. There was no significant change in the incidence of the other 40 Tier-1 cancers. The incidence of several cancers increased due to the dissemination of diagnostic concepts, improved diagnostic techniques, changes in coding practice, and the initiation of the NCR.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

BEopt™ (Building Energy Optimization Tool) version 3.0.1 3/16/2023 [SWR-05-41]

The BEopt™ (Building Energy Optimization Tool) software provides capabilities to evaluate residential building designs and identify cost-optimal efficiency packages at various levels of whole-house energy savings along the path to zero net energy. The BEopt software can be used to analyze both new construction and existing home retrofits, as well as single-family detached and multi-family buildings, through evaluation of single building designs, parametric sweeps, and cost-based optimizations. The BEopt software provides detailed simulation-based analysis based on specific house characteristics, such as size, architecture, occupancy, vintage, location, and utility rates. Discrete envelope and equipment options, reflecting realistic construction materials and practices, are evaluated. The BEopt software uses EnergyPlus®, the Department of Energy's flagship simulation engine. Simulation assumptions are based on ANSI/RESNET/ICC Standards and the Building America Housing Simulation Protocols. The sequential search optimization technique used by the BEopt software: Finds minimum-cost building designs at different target energy-savings levels Identifies multiple near-optimal designs along the path, allowing for equivalent solutions based on builder or contractor preference. Download A BEopt™ Version 3.0.1 Beta release (Mar. 16, 2023) is now available. Download the BEopt 3.0.1 Beta setup file.

Christensen, Craig↗
DOE OSTI2023

Measurement-induced entanglement phase transitions in variational quantum circuits

Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many-body systems and improve machine learning algorithms using near-term quantum computers. Prominent challenges associated with this family of quantum-classical hybrid algorithms are the control of quantum entanglement and quantum gradients linked to their classical optimization. Known as the barren plateau phenomenon, these quantum gradients may rapidly vanish in the presence of volume-law entanglement growth, which poses a serious obstacle to the practical utility of VQAs. Inspired by recent studies of measurement-induced entanglement transition in random circuits, we investigate the entanglement transition in variational quantum circuits endowed with intermediate projective measurements. Considering the Hamiltonian Variational Ansatz (HVA) for the XXZ model and the Hardware Efficient Ansatz (HEA), we observe a measurement-induced entanglement transition from volume-law to area-law with increasing measurement rate. Moreover, we provide evidence that the transition belongs to the same universality class of random unitary circuits. Importantly, the transition coincides with a “landscape transition” from severe to mild/no barren plateaus in the classical optimization. Our work may provide an avenue for improving the trainability of quantum circuits by incorporating intermediate measurement protocols in currently available quantum hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Surrogate modelling the Baryonic Universe II: On forward modelling the colours of individual and populations of galaxies

ABSTRACT Among the properties shaping the light of a galaxy, the star formation history (SFH) is one of the most challenging to model due to the variety of correlated physical processes regulating star formation. In this work, we leverage the stellar population synthesis model fsps, together with SFHs predicted by the hydrodynamical simulation IllustrisTNG and the empirical model universemachine, to study the impact of star formation variability on galaxy colours. We start by introducing a model-independent metric to quantify the burstiness of a galaxy formation model, and we use this metric to demonstrate that universemachine predicts SFHs with more burstiness relative to IllustrisTNG. Using this metric and principal component analysis, we construct families of SFH models with adjustable variability, and we show that the precision of broad-band optical and near-infrared colours degrades as the level of unresolved short-term variability increases. We use the same technique to demonstrate that variability in metallicity and dust attenuation presents a practically negligible impact on colours relative to star formation variability. We additionally provide a model-independent fitting function capturing how the level of unresolved star formation variability translates into imprecision in predictions for galaxy colours; our fitting function can be used to determine the minimal SFH model that reproduces colours with some target precision. Finally, we show that modelling the colours of individual galaxies with per cent-level precision demands resorting to complex SFH models, while producing precise colours for galaxy populations can be achieved using models with just a few degrees of freedom.

Chaves-Montero, Jonás (ORCID:0000000295534261)↗
DOE OSTI2022

Large-Scale Simulation of Regional Demand Flexibility Implementation and Customer Economic Impact

The Distribution System Operator with Transactive (DSO+T) study conducted a large-scale simulation of over 60,000 customers in a region the size of Texas to demonstrate the effective coordination of distributed energy resources (DERs) in commercial and residential buildings. The integrated simulation included both the bulk (wholesale generation and transmission) and distribution systems. The DERs (including batteries, electric vehicles, air conditioning, and water heaters) participated in a transactive energy retail market that was integrated into an existing competitive wholesale market. The engineering and economic performance of the resulting demand flexibility was evaluated over annual simulations for both moderate and high renewable generation scenarios. A detailed parametric cost model was developed to enable detailed economic analysis of key stakeholders. In addition, fixed and dynamic customer tariffs were designed and applied to the customer population. This allowed the impact on annual customer bills to be analyzed for various building types (residential versus commercial; single- versus multi-family). This paper presents results showing the relative flexibility of batteries, electric vehicles, and building loads throughout the year and under different renewable scenarios. This feeds a detailed breakdown of the impact this flexibility has on the operating costs of the grid and the resulting net economic benefit. Finally, the study showed that practically all customer classes (including non-participating customers) save money under the proposed demand flexibility scheme. The study found overall net annual economic savings of $3.3-5.0B for a region the size of Texas equating to average customer bill savings of 10-16%.

Reeve, Hayden M.↗
DOE OSTI2021

In situ XAS study of the local structure of the nano-Li 2 FeSiO 4 /C cathode

Despite the challenges in achieving its full theoretical capacity of reversible extraction of two Li ions, the Li 2 FeSiO 4 (LFS) cathode shows a remarkable cycling stability once its low electronic conductivity is addressed. By studying the local structure around the iron during electrochemical cycling using in situ x-ray absorption spectroscopy (XAS), it is possible to gain insight into the factors which determine the electrochemical properties of this material. In order to practically perform in situ XAS studies, the charge/discharge of LFS was maximized using two approaches: (a) reducing the particle size of LFS samples from micro-scale to nano-scale in order to reduce the diffusion path for intercalating ions; and (b) applying a conductive coating to each nanoparticle to facilitate electron transfer. A family of LFS materials was synthesized and characterized using x-ray diffraction, and scanning electron microscopy with energy dispersive analysis for structural and morphological analysis, as well as cyclic voltammetry and cycling tests for electrochemical performance diagnosis. This material was then characterized by in situ XAS. The results provide insight into the stable electrochemical performance of LFS and suggest new synthetic routes to reaching the theoretical capacity.

25 ENERGY STORAGE↗
DOE OSTI2025

Two-Dimensional Ferroelectric Altermagnets: From Model to Material Realization

Multiferroic altermagnets offer new opportunities for magnetoelectric coupling and electrically tunable spintronics. However, due to intrinsic symmetry conflicts between altermagnetism and ferroelectricity, achieving their coexistence, known as ferroelectric altermagnets (FEAM), remains an outstanding challenge, especially in two-dimensional (2D) systems. Here, we propose a universal, symmetry-based design principle for 2D FEAM, supported by tight-binding models and first-principles calculations. We show that lattice distortions can break spin equivalence and introduce the necessary rotation-related symmetry, enabling altermagnetism with electrically reversible spin splitting. Guided by this framework, we identify a family of 2D vanadium oxyhalides and sulfide halides as promising FEAM candidates. In these compounds, pseudo Jahn-Teller distortions and Peierls-like dimerization cooperatively establish the required symmetry conditions. Here, we further propose the magneto-optical Kerr effect as an experimental probe to confirm FEAM and its electric spin reversal. Furthermore, our findings provide a practical framework for 2D FEAM and advancing electrically controlled spintronic devices.

2D materials↗
DOE OSTI2025

Memristive linear algebra

The advent of memristive devices offers a promising avenue for efficient and scalable analog computing, particularly for linear algebra operations essential in various scientific and engineering applications. This paper investigates the potential of memristive crossbars in implementing matrix inversion algorithms. We explore both static and dynamic approaches, emphasizing the advantages of analog and in-memory computing for matrix operations beyond multiplication. In particular, we demonstrate that the electrical properties of memristive crossbars uniquely suit them for the evolution of a family of matrix exponentials, which can be exploited for the efficient computation of matrix inverses and online solutions for linear problems. Our results demonstrate that memristive arrays can reduce computational complexity. We also study power consumption and show a tradeoff between precision and energy. Furthermore, we address the challenges of device variability, precision, and scalability, providing insights into the practical implementation of these algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

An Energy Codes Gap Analysis Field Study in the Southwest (Final Technical Report)

The primary goal of the “An Energy Codes Gap Analysis Field Study in the Southwest” project was to improve housing through strengthened building energy code implementation in two states, Colorado and Nevada, leading to greater energy and utility bill savings for households. Colorado is a home rule state. Nevada adopts a statewide code, but cities and counties must also adopt the code, making the state function like a home rule state. To broaden the impact of the project, an additional goal was to share and amplify project experiences, findings, and lessons that can be applied in other states. To achieve these goals, the project’s key objectives were to examine how residential energy codes are implemented in partner states; to use these findings to strengthen codes implementation through enhanced education, training, and outreach; and to enhance energy code technical assistance capabilities in the two partner states. In both partner states, the project led to the creation of a quantitative baseline understanding of compliance with high-impact energy code requirements, such as foundation, wall, and ceiling R-value; window U-factor and SHGC; envelope air tightness; duct tightness; and high-efficiency lighting. It then applied this baseline information to directly inform training in geographic areas with high levels of building and construction activity. The project has placed Colorado and Nevada among 23 states across the country that have conducted a single-family residential field study based on the established U.S. Department of Energy (DOE) methodology since 2014. It has also helped each State Energy Office test models for partnership- and relationship-building among government agencies and key stakeholder groups, including the State Energy Office and relevant code administration and professional licensing agencies, building officials, designers, builders, trades and utilities. At the national level, the project helped educate and inform State and Territory Energy Offices on a replicable methodology to assess energy code construction practices, effective stakeholder engagement strategies, and tailored energy code education and training. These findings are particularly pertinent for home-rule states, such as Colorado and Nevada, which rely on local government awareness and action to advance and implement building energy codes. By raising awareness of code adoption and compliance, this project has helped to equip each partner state with data on code compliance, strategies, education, and partnership models to advance building energy codes. This in turn will have an important impact on new construction practices in Colorado and Nevada, leading to more energy efficient and affordable housing. Currently, more than 62 percent of Colorado’s population lives in one of the 55 jurisdictions that have adopted the 2021 IECC to date, while nearly 95 percent of the state's population lives in one of the 208 jurisdictions that have adopted a code at 2015 IECC levels or higher. In Nevada, the 2018 IECC was adopted by the Governor’s Office of Energy in July 2018. By July 2020, 47 percent of the adopted energy code in the state was 2018 IECC which covers 96.5 percent of Nevada’s population. The training materials funded by this project focus on the 2021 IECC and will continue to be relevant in the two states as additional jurisdictions update their building energy codes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Minimizing Auxiliary Heat Use for Cold Climate Operation of Air-Source Heat Pumps

This paper investigates the auxiliary heat use for air-source heat pumps (ASHPs) operating in cold climate conditions. Twelve variable-capacity, central ducted ASHPs installed in single-family homes in cold climate regions (eleven in northwest United States and one in Denver suburb) were monitored for an entire winter season to collect data at cold temperatures. The methodology employed airside and power measurements that were taken every five-seconds, to calculate heat pump's capacity, coefficient of performance (COP), and auxiliary heat energy consumption. This paper provides valuable insights into the practical implications of auxiliary heat utilization in centrally ducted ASHPs and suggests opportunities to mitigate the usage of auxiliary heat, improving the overall system efficiency during cold climate operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Multi-angle quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we investigate a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters. Even though the number of parameters increases, our results indicate that good parameters can be found in polynomial time for a test dataset we consider. This new ansatz gives a 33% increase in the approximation ratio for an infinite family of MaxCut instances over QAOA. The optimal performance is lower bounded by the conventional ansatz, and we present empirical results for graphs on eight vertices that one layer of the multi-angle anstaz is comparable to three layers of the traditional ansatz on MaxCut problems. Similarly, multi-angle QAOA yields a higher approximation ratio than QAOA at the same depth on a collection of MaxCut instances on fifty and one-hundred vertex graphs. Many of the optimized parameters are found to be zero, so their associated gates can be removed from the circuit, further decreasing the circuit depth. These results indicate that multi-angle QAOA requires shallower circuits to solve problems than QAOA, making it more viable for near-term intermediate-scale quantum devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Minimizing Auxiliary Heat Use for Cold Climate Operation of Air Source Heat Pumps: Preprint

This paper investigates the auxiliary heat use for air-source heat pumps (ASHPs) operating in cold climates. Twelve variable-capacity, central ducted ASHPs installed in single-family homes in cold climate regions (eleven in the northwest United States and one in a Denver suburb) were monitored for an entire winter season to collect data at cold temperatures. The methodology employed airside and power measurements that were taken every five seconds, to calculate the heat pump’s capacity, coefficient of performance (COP), and auxiliary heat energy consumption. This paper provides insights into the practical implications of auxiliary heat utilization in centrally ducted ASHPs and suggests opportunities to mitigate the usage of auxiliary heat, improving overall system efficiency during cold climate operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Best Practices for Measuring the Modulation Transfer Function of Video Endoscopes

Endoscopes are crucial for assisting in surgery and disease diagnosis, including the early detection of cancer. The effective use of endoscopes relies on their optical performance, which can be characterized with a series of metrics such as resolution, vital for revealing anatomical details. The modulation transfer function (MTF) is a key metric for evaluating endoscope resolution. However, the 2020 version of the ISO 8600-5 standard, while introducing an endoscope MTF measurement method, lacks empirical validation and excludes opto-electronic video endoscopes, the largest family of endoscopes. Measuring the MTF of video endoscopes requires tailored standards that address their unique characteristics. This paper aims to expand the scope of ISO 8600-5:2020 to include video endoscopes, by optimizing the MTF test method and addressing parameters affecting measurement accuracy. We studied the effects of intensity and uniformity of image luminance, chart modulation compensation, linearity of image digital values, auto gain control, image enhancement, image compression and the region of interest dimensions on images of slanted-edge test charts, and thus the MTF based on these images. By analyzing these effects, we provided recommendations for setting and controlling these factors to obtain accurate MTF curves. Our goal is to enhance the standard’s relevance and effectiveness for measuring the MTF of a broader range of endoscopic devices, with potential applications in the MTF measurement of other digital imaging devices.

47 OTHER INSTRUMENTATION↗