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

Microbial inoculants and invasions: a call to action

Microbial inoculants are increasingly used for beneficial purposes in agriculture, bioremediation, and medicine, but they can carry risks of generating invasive microbes. Here, we present a roadmap for guarding against these invasions, proposing developing (i) coherent mechanistic understandings of how microbial inoculants can effect invasions, (ii) predictive models forecasting microbial invasion risks, and (iii) effective management strategies. To guide mechanistic understandings, we distill 17 guiding hypotheses. For predictive modeling, we highlight data collection needs and qualitative approaches. For management strategies, we stress the importance of accurately weighing the risks against benefits. The unified approach presented here provides a route toward an effective research and management infrastructure for microbial inoculants in order to avoid potentially catastrophic microbial invasions.

invasive species↗
DOE OSTI2015

Wetting properties of molecularly rough surfaces

We employ molecular dynamics simulations to study the wettability of nanoscale rough surfaces in systems governed by Lennard-Jones (LJ) interactions. We consider both smooth and molecularly rough planar surfaces. Solid substrates are modeled as a static collection of LJ particles arranged in a face-centered cubic lattice with the (100) surface exposed to the LJ fluid. Molecularly rough solid surfaces are prepared by removing several strips of LJ atoms from the external layers of the substrate, i.e., forming parallel nanogrooves on the surface. We vary the solid-fluid interactions to investigate strongly and weakly wettable surfaces. We determine the wetting properties by measuring the equilibrium droplet profiles that are in turn used to evaluate the contact angles. Macroscopic arguments, such as those leading to Wenzel’s law, suggest that surface roughness always amplifies the wetting properties of a lyophilic surface. However, our results indicate the opposite effect from roughness for microscopically corrugated surfaces, i.e., surface roughness deteriorates the substrate wettability. Adding the roughness to a strongly wettable surface shrinks the surface area wet with the liquid, and it either increases or only marginally affects the contact angle, depending on the degree of liquid adsorption into the nanogrooves. For a weakly wettable surface, the roughness changes the surface character from lyophilic to lyophobic due to a weakening of the solid-fluid interactions by the presence of the nanogrooves and the weaker adsorption of the liquid into the nanogrooves.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Catalytic properties of model supported nanoparticles

Nanoparticles of late transition metals are used as catalysts and electrocatalysts for industrial chemical reactions that produce fuels, convert chemical energy to electricity and clean up pollution associated with the generation and use of these fuels. For such applications, metals are usually bonded onto the surfaces of oxide or carbon support materials. To provide the energy needed for sustained economic development, we must develop new and improved solid catalysts and electrocatalysts for a variety of reactions that take better advantage of traditional and alternate energy sources (solar, wind, biomass, etc.) and avoid serious environmental problems. Additionally, since these metals are often expensive and in limited supply (e.g., Pt), we also need to develop catalysts that do not require precious metals. As a consequence, recent years have seen a dramatic increase in research aimed at providing the basic understanding needed to develop new and improved catalysts and electrocatalysts for a variety of reactions that involve nanoparticles of late transition metals supported on oxide or carbon materials. It is therefore timely to present a special issue that focusses on Catalytic Properties of Model Supported Nanoparticles. The collection highlights some of the most exciting new directions for research in this area, and provides in depth examples of some of the most cutting-edge methods, both experimental and theoretical, for studying this class of catalysts, which provide insights into structure, mechanisms, kinetics and energetics with unprecedented accuracy and detail. Finally, as Guest Editors, we present below a few of the many excellent papers published in the collection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Non-equilibrium rate theory for polariton relaxation dynamics

We derive an analytic expression of the non-equilibrium Fermi’s golden rule (NE-FGR) expression for a Holstein–Tavis–Cumming Hamiltonian, a universal model for many molecules collectively coupled to the optical cavity. These NE-FGR expressions capture the full-time-dependent behavior of the rate constant for transitions from polariton states to dark states. The rate is shown to be reduced to the well-known frequency domain-based equilibrium Fermi’s golden rule (E-FGR) expression in the equilibrium and collective limit and is shown to retain the same scaling with the number of sites in non-equilibrium and non-collective cases. We use these NE-FGR to perform population dynamics with a time-non-local and time-local quantum master equation and obtain accurate population dynamics from the initially occupied upper or lower polariton states. Furthermore, NE-FGR significantly improves the accuracy of the population dynamics when starting from the lower polariton compared to the E-FGR theory, highlighting the importance of the non-Markovian behavior and the short-time transient behavior in the transition rate constant.

Chemical dynamics↗
DOE OSTI2025

New generation bunch shape monitor for ion accelerators

Measuring longitudinal beam parameters is important for operation and development of high intensity linear accelerators, but it is notoriously difficult for proton and ion beams at non-relativistic energies. The Bunch Shape Monitor (BSM) is a device used for measuring the longitudinal bunch distribution in ion linacs. The existing BSM models have poor electron collection efficiency from the wire and are limited to one-dimensional measurements of the phase coordinate. In response to this problem, we have developed a new generation BSM with improved performance. The proposed design incorporates three major innovations: First, the collection efficiency was improved by adding a focusing field between the wire and the entrance slit, which will also allow measurements over a much wider dynamic range. Second, an improvement in the measurement speed was achieved by sampling longitudinal profiles of multiple energy slices simultaneously, where the BSM wire is placed at the exit of an ion spectrometer so that ions with different energies hit the wire at different horizontal coordinates along the wire. Finally, the design incorporates a motion system that can shift the wire and deflecting cavity together, enabling transverse profile measurements like a wire scanner. Here, in this paper, we will provide the design of the new BSM and report on its beam test results at the Spallation Neutron Source facility in Oak Ridge National Laboratory.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Measurements of the electron energy distribution function in partially magnetized low temperature plasmas

While Langmuir probes (LPs) are relatively simple and inexpensive plasma diagnostics for the electron density, temperature, and the electron energy distribution function (EEDF), the interpretation of the measured current–voltage (I–V) characteristic is complicated considerably by the presence of a magnetic field. In regimes where the electron gyroradius is comparable to the probe radius, the electron flux to the probe surface is retarded by reduced mobility across field lines and can no longer be described by a thermal model. Predicting the current collected by the probe in these regimes requires accurate estimates of the plasma diffusion coefficients, which are usually difficult to obtain. In this work, we measure electron energy distribution functions in E×B plasmas with magnetized electrons and non-magnetized ions in argon and krypton gases, using both a LP and laser Thomson scattering (LTS) at various magnetic fields. Using the LTS measurements to provide a robust benchmark for comparison, we compare existing theories describing the flux to the probe under magnetized conditions. We find that even when the electron gyroradius associated with the effective electron temperature is small compared to the probe radius, the EEDF computed using classical probe theory is still robust at energies higher than the energy at which the gyroradius becomes larger than the probe size. For plasmas that are not strongly non-Maxwellian, we formulate a method to extract robust density and temperature measurements using physics-informed fitting techniques to analyze EEDFs computed using classical theory.

Devin, E. G. [Princeton Plasma Physics Laboratory ↗
DOE OSTI2013

LINE EMISSION FROM RADIATION-PRESSURIZED H II REGIONS. II. DYNAMICS AND POPULATION SYNTHESIS

Optical and infrared emission lines from H II regions are an important diagnostic used to study galaxies, but interpretation of these lines requires significant modeling of both the internal structure and dynamical evolution of the emitting regions. Most of the models in common use today assume that H II region dynamics are dominated by the expansion of stellar wind bubbles, and have neglected the contribution of radiation pressure to the dynamics, and in some cases also to the internal structure. However, recent observations of nearby galaxies suggest that neither assumption is justified, motivating us to revisit the question of how H II region line emission depends on the physics of winds and radiation pressure. In a companion paper we construct models of single H II regions including and excluding radiation pressure and winds, and in this paper we describe a population synthesis code that uses these models to simulate galactic collections of H II regions with varying physical parameters. We show that the choice of physical parameters has significant effects on galactic emission line ratios, and that in some cases the line ratios can exceed previously claimed theoretical limits. Our results suggest that the recently reported offset in line ratio values between high-redshift star-forming galaxies and those in the local universe may be partially explained by the presence of large numbers of radiation-pressure-dominated H II regions within them.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Region-based convolutional neural network for wind turbine wake characterization from scanning lidars

A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.

17 WIND ENERGY↗
DOE OSTI2021

efam: an e xpanded, metaproteome-supported HMM profile database of viral protein fam ilies

Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases. Here, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from ‘conservative’ to ‘eXtremely Conservative’ resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by ~24% on average (up to ~42%) and especially improved the recovery of often-missed shorter contigs (<5 kb). Additionally, to help elucidate lesser-known viral protein functions, we annotated the profiles using multiple databases from the DRAM pipeline and virion-associated metaproteomic data, which doubled the number of annotations obtainable by standard, single-database annotation approaches. Together, these marine resources (efam and efam-XC) are provided as searchable, compressed HMM databases that will be updated bi-annually to help maximize viral sequence discovery and study from any ecosystem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2010

Qubit-induced high-order nonlinear interaction of the polar molecules in a stripline cavity

By the exchange of virtual microwave photon induced by a stripline cavity, we present a collective spin-qubit model for the indirect interaction between a superconducting charge-phase qubit and an ensemble of the polar molecules. More importantly, the high-order nonlinear interaction among the polar molecules is generated successfully by using the SU(2) commutation relations of the qubit. Numerical simulation shows that this high-order nonlinear interaction can lead to a strong macroscopic quantum coherent effect, even if the parameters including the molecular number are small. For a large molecular number, the Kerr nonlinear interaction is also obtained. Moreover, it is found that this Kerr nonlinear parameter depends linearly on the molecular number, and as a result, has a giant value, which is 2 to 3 orders of magnitude larger than that in the other proposal schemes of the solid-state systems. Our results present a way to investigate the quantum nonlinear dynamics of the polar molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Field space geometry and nonlinear supersymmetry

We propose a geometric formulation of effective field theories via nonlinear supersymmetry. Nonsupersymmetric particles are embedded in constrained superfields governed by a nonlinear σ model, and operators are collected into potentials on the target space. The use of chiral superfields standardizes the treatment of flavor across scalars and fermions, and the minimal jet bundle extension makes invariance under derivative field redefinitions manifest. Published by the American Physical Society 2025

Lee, Yu-Tse (ORCID:0009000190324206)↗
DOE OSTI2010

Powder diffraction by fixed incident angle reflection using a curved position-sensitive detector

As curved position-sensitive detectors improve in angular resolution, the effects that fixed incident angle reflection have on X-ray diffraction peaks become more apparent. In this study the effects of sample transparency, incident beam height, detector resolution and sample displacement on the intensity, location, width and shape of powder diffraction peaks were examined. The functions describing each of these phenomena are presented and were successfully used to quantitatively model the diffraction peaks collected in this geometry. Three distinct regimes of diffraction peak resolution were identified from the phenomena that limit the peak variance. Pertinent criteria based on experimental parameters have been outlined to classify fixed incident angle reflection experiments into each regime. Guidelines for improvement of experimental resolution and for conducting analysis of data acquired using fixed incident angle reflection geometry and curved position-sensitive detectors are also provided.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep residual networks for crystallography trained on synthetic data

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2013

A MULTI-MODEL ANALYSIS OF POST-2020 MITIGATION EFFORTS OF FIVE MAJOR ECONOMIES

This paper looks into the regional mitigation strategies of five major economies (China, EU, India, Japan, and USA) in the context of the 2°C target, using a multi-model comparison. In order to stay in line with the 2°C target, a tripling or quadrupling of mitigation ambitions is required in all regions by 2050, employing vigorous decarbonization of the energy supply system and achieving negative emissions during the second half of the century. In all regions looked at, decarbonization of energy supply (and in particular power generation) is more important than reducing energy demand. Some differences in abatement strategies across the regions are projected: In India and the USA the emphasis is on prolonging fossil fuel use by coupling conventional technologies with carbon storage, whereas the other main strategy depicts a shift to carbon-neutral technologies with mostly renewables (China, EU) or nuclear power (Japan). Regions with access to large amounts of biomass, such as the USA, China, and the EU, can make a trade-off between energy related emissions and land related emissions, as the use of bioenergy can lead to a net increase in land use emissions. After supply-side changes, the most important abatement strategy focuses on end-use efficiency improvements, leading to considerable emission reductions in both the industry and transport sectors across all regions. Abatement strategies for non-CO2 emissions and land use emissions are found to have a smaller potential. Inherent model, as well as collective, biases have been observed affecting the regional response strategy or the available reduction potential in specific (end-use) sectors.

abatement↗
DOE OSTI2017

Pinyon, Version 0

Pinyon is a tool that stores steps involved in creating a model derived from a collection of data. The main function of Pinyon is to store descriptions of calculations used to analyze or visualize the data in a database, and allow users to view the results of these calculations via a web interface. Additionally, users may also use the web interface to make adjustments to the calculations and rerun the entire collection of analysis steps automatically.

Ward, Logan↗
DOE OSTI2021

Whetstone v.0.9.2

Whetstone is a deep learning software library for spiking/binary threshold neuromorphic hardware. Built to integrate with Keras models, Whetstone provides a collection of layers, callbacks, and utility functions designed to allow the training of integrate-and-fire neurons rather than traditional analog activation functions (e.g. rectified linear units). Generally, training a network for neuromorphic hardware is challenging due to the discontinuity of the activation function. Whetstone overcomes this challenge by using ever-closer continuous approximations of a threshold activation, modified during training time. This allows rapid and consistent training for ultra-low powered neuromorphic hardware.

Severa, William↗
DOE OSTI2022

Framework For Optimization Of Resources And Economics Ecosystem

The Framework for Optimization of ResourCes and Economics is a collection of software tools, models, and datasets acquired and developed under the Integrated Energy Systems (IES) program to enable analysis of technical and economic viability of myriad IES configurations. FORCE is the consolidating interface and data repository for all the IES toolsets ranging from macro technoeconomic analysis to transient process modeling and experimental validation for integrated energy systems.

Frick, KonorL↗
DOE OSTI2011

Automated Auditing Tool for Retrofit Building Projects [SWR-11-07]

Building energy auditors regularly use notepads, physical forms, or simple spreadsheets to inventory energy consuming devices in buildings and audit overall performance. Mobile computing devices such as smart phones or tablet computers with camera inputs may be used to automatically capture relevant information and format audit input in a way that streamlines work flows and reduces the likelihood of error. As an example. an auditor could walk through a space holding a mobile device, which automatically identifies and appliances, windows, etc. This information would automatically be added to a mobile database associated with the building for later integration with a larger building audit database. The user experience would require little or no manual input, and could integrate with tools to automate used to automate data collection for building energy modeling.

Parker, Andrew↗