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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data for The Stem Cell-Type Transcriptome of Bioenergy Sorghum Reveals the Spatial Regulation of Secondary Cell Wall Networks

Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum’s 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about stem cell-type gene expression and regulation was available to enable engineering. To obtain this information, Laser Capture Microdissection (LCM) was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type specific gene regulatory networks (GRNs) revealed that unique TF families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with a stem developmental transcriptome dataset to identify the GRN that differentially activates the secondary cell wall (SCW) formation in stem xylem sclerenchyma and epidermal cells. The cell-type transcriptomic dataset provides a valuable source of information about the function of sorghum stem cell types and GRNs that will enable the engineering of bioenergy sorghum stems.

Software↗
DOE OSTI2020

Discerning the thermodynamic feasibility of the spontaneous coexistence of multiple functional vegetation groups

Can the Second Law of Thermodynamics explain why ecosystems naturally organize into a complex structure composed of multiple vegetation species and functional groups? Ecosystem structure, which refers to the number and type of plant functional groups, is the result of self-organization, or the spontaneous emergence of order from random fluctuations. By considering ecosystems as open thermodynamic systems, we model and study these fluctuations of throughput signatures on short timescales to determine the drivers and characteristics of ecosystem structure. This diagnostic approach allows us to use fluxes of energy and entropy to calculate an ecosystem’s estimated work and understand the thermodynamic behavior of the system. We use a multi-layer canopy-root-soil model to calculate the energy and entropy fluxes of different scenarios for field sites across various climates. At each site, scenarios comprised of native individual plant functional groups and a coexisting multi-group composition scenario including all functional groups observed at the site are compared. Ecosystem-scale calculations demonstrate that entropy fluxes and work efficiency—the work performed for the amount of radiation entering the ecosystem—are greatest in the multi-group scenario when its leaf area is significantly larger than each of its individual functional groups. Thus, we conclude that ecosystems self-organize towards the vegetation structure with the greatest outgoing entropy flux and work efficiency, resulting in the coexistence of multiple functional groups and performing the maximum amount of work within the constraints of locally available energy, water, and nutrients.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Grassland ecosystem type drives AM fungal diversity and functional guild distribution in North American grasslands

Nutrient exchange forms the basis of the ancient symbiotic relationship that occurs between most land plants and arbuscular mycorrhizal (AM) fungi. Plants provide carbon (C) to AM fungi and fungi provide the plant with nutrients such as nitrogen (N) and phosphorous (P). Nutrient addition can alter this symbiotic coupling in key ways, such as reducing AM fungal root colonization and changing the AM fungal community composition. However, environmental parameters that differentiate ecosystems and drive plant distribution patterns (e.g., pH, moisture), are also known to impact AM fungal communities. Identifying the relative contribution of environmental factors impacting AM fungal distribution patterns is important for predicting biogeochemical cycling patterns and plant-microbe relationships across ecosystems. Here, to evaluate the relative impacts of local environmental conditions and long-term nutrient addition on AM fungal abundance and composition across grasslands, we studied experimental plots amended for 10 years with N, P, or N and P fertilizer in different grassland ecosystem types, including tallgrass prairie, montane, shortgrass prairie, and desert grasslands. Contrary to our hypothesis, we found ecosystem type, not nutrient treatment, was the main driver of AM fungal root colonization, diversity, and community composition, even when accounting for site-specific nutrient limitations. We identified several important environmental drivers of grassland ecosystem AM fungal distribution patterns, including aridity, mean annual temperature, root moisture, and soil pH. This work provides empirical evidence for niche partitioning strategies of AM fungal functional guilds and emphasizes the importance of long-term, large scale research projects to provide ecologically relevant context to nutrient addition studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

EMISSA (Exploring Millimeter Indicators of Solar-Stellar Activity): I. The initial millimeter–centimeter main-sequence star sample

Due to their wide wavelength coverage across the millimeter to centimeter (mm–cm) range and their increased sensitivity, modern interferometric arrays facilitate observations of the thermal and non-thermal radiation that is emitted from different layers in the outer atmospheres of stars. We study the spectral energy distribution (S obs (ν)) of main-sequence stars based on archival observations in the mm–cm range with the aim to study their atmospheric stratification as a function of stellar type. The main-sequence stars with significant detection in mm bands were identified in the ALMA Science Archive. These data were then complemented with spectral flux data in the extreme ultraviolet to cm range as compiled from various catalogues and observatory archives. We compared the resultant S obs (ν) of each star with a photospheric emission model (S mod (ν)) calculated with the PHOENIX code. The departures of S obs (ν) from S mod (ν) were quantified in terms of a spectral flux excess parameter (ΔS/S mod ) and studied as a function of stellar type. The initial sample consists of 12 main-sequence stars across a broad range of spectral types from A1 to M3.5 and the Sun-as-a-star as reference. The stars with T eff = 3000–7000 K (F–M type) showed a systematically higher S obs (ν) than S mod (ν) in the mm–cm range. Their ΔS/S mod exhibits a monotonic rise with decreasing frequency. The steepness of this rise is higher for cooler stars in the T eff = 3000–7000 K range, although the single fully convective star (T eff ~ 3000 K) in the sample deviates from this trend. Meanwhile, Sobs(ν) of the A-type stars agrees with S mod (ν) within errors. The systematically high ΔS/S mod in F–M stars indicates hotter upper atmospheric layers, that is, a chromosphere and corona in these stars, like for the Sun. The mm–cm ΔS/S mod spectrum offers a way to estimate the efficiency of the heating mechanisms across various outer atmospheric layers in main-sequence stars, and thereby to understand their structure and activity. We emphasise the need for dedicated surveys of main-sequence stars in the mm–cm range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The LISE package: solvers for static and time-dependent superfluid local density approximation equations in three dimensions

Nuclear implementation of the density functional theory (DFT) is at present the only microscopic framework applicable to the whole nuclear landscape. The extension of DFT to superfluid systems in the spirit of the Kohn-Sham approach, the superfluid local density approximation (SLDA) and its extension to time-dependent situations, time-dependent superfluid local density ap- proximation (TDSLDA), have been extensively used to describe various static and dynamical problems in nuclear physics, neutron star crust, and cold atom systems. In this paper, we present the codes that solve the static and time-dependent SLDA equations in three-dimensional coordinate space without any symmetry restriction. These codes are fully parallelized with the message passing interface (MPI) library and take advantage of graphic processing units (GPU) for accelerating execution. The dynamic codes have checkpoint/restart capabilities and for initial conditions one can use any generalized Slater determinant type of wave function. The code can describe a large number of physical problems: nuclear fission, collisions of heavy ions, the interaction of quantized vor- tices with nuclei in the nuclear star crust, excitation of superfluid fermion systems by time dependent external fields, quantum shock waves, domain wall generation and propagation, the dynamics of the Anderson-Bogoliubov-Higgs mode, dynamics of fragmented condensates, vortex rings dynamics, generation and dynamics of quantized vortices, their crossing and recombinations and the incipient phases of quantum turbulence.

Jin, Shi↗
DOE OSTI2020

Quantifying the Tangible Value of Public Electric Vehicle Charging Infrastructure

The lack of an extensive public recharging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Because charging infrastructure is likely to be underutilized during the early stages of market development, it is difficult for decision makers to decide how much to invest in public charging stations. Quantifying the value of public charging infrastructure to current and potential future owners of PEVs is essential for estimating the benefits of charging stations to current PEV owners and for predicting the impact on future PEV sales. This paper estimates consumers’ willingness to pay for public charging infrastructure in the context of utility maximization. The objective is to provide a method for valuing charging infrastructure that can inform investment decisions and be used in forecasting models to predict the impacts on future PEV sales. A basic theory of the tangible value of charging infrastructure is developed as a function of PEV type, range, recharging time and existing infrastructure. Existing simulation studies provide functional relationships that quantify the ability of charging infrastructure to enable additional miles of electrified travel. The enabled travel functions are used to predict impact of infrastructure deployment on incremental electrified travel for 1) plug-in hybrids and 2) intra-regional and 3) inter-regional travel by all-electric vehicles. The willingness to pay for increased electrified miles is derived from the willingness to pay for increased electric driving range, based on econometric studies of plug-in vehicle choice. The result is a set of three functions that can be used to calculate the marginal willingness-to-pay for public charging infrastructure as a function of vehicle attributes, existing charging infrastructure, energy prices and annual vehicle travel.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Structure–Thermodynamic Relationship of a Polysaccharide Gel (Alginate) as a Function of Water Content and Counterion Type (Na vs Ca)

Biofilms are the predominant mode of microbial life on Earth, and so a deep understanding of microbial communities-and their impacts on environmental processes-requires a firm understanding of biofilm properties. Because of the importance of biofilms to their microbial inhabitants, microbes have evolved different ways of engineering and reconfiguring the matrix of extracellular polymeric substances (EPS) that constitute the main non-living component of biofilms. This ability makes it difficult to distinguish between the biotic and abiotic origins of biofilm properties. An important route toward establishing this distinction has been the study of simplified models of the EPS matrix. This study builds on such efforts by using atomistic simulations to predict the nanoscale (≤10 nm scale) structure of a model EPS matrix and the sensitivity of this structure to interpolymer interactions and water content. To accomplish this, we use replica exchange molecular dynamics (REMD) simulations to generate all-atom configurations of ten 3.4 kDa alginate polymers at a range of water contents and Ca–Na ratios. Simulated systems are solvated with explicitly modeled water molecules, which allows us to capture the discrete structure of the hydrating water and to examine the thermodynamic stability of water in the gels as they are progressively dehydrated. Our primary findings are that (i) the structure of the hydrogels is highly sensitive to the identity of the charge-compensating cations, (ii) the thermodynamics of water within the gels (specific enthalpy and free energy) are, surprisingly, only weakly sensitive to cation identity, and (iii) predictions of the differential enthalpy and free energy of hydration include a short-ranged enthalpic term that promotes hydration and a longer-ranged (presumably entropic) term that promotes dehydration, where short and long ranges refer to distances shorter or longer than ~0.6 nm between alginate strands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Determination of Carbon Functional Groups in Pyrolysis Bio-Oils using 13 C NMR: Laboratory Analytical Procedure (LAP)

Pyrolysis is a process that can be used to convert biomass to solid, liquid and gaseous products for use as renewable chemicals and fuels. The liquid fraction, known as “bio-oil” is complex and challenging to characterize, particularly by means of GC/MS, GPC, LC and FT-IR. NMR is capable of analyzing whole bio-oil samples and can provide quantitative results to characterize different functional groups or types of carbon present in bio-oil. This Laboratory Analytical Procedure (LAP) quantifies different carbon functional groups in whole bio-oil samples. This information can be used for comparisons between different pyrolysis experimental conditions or different upgrading processes and catalysts, and also allow for comparisons between bio-oils produced at different facilities.

09 BIOMASS FUELS↗
DOE OSTI2022

Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review

Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. Here, this article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Guided construction of single cell reference for human and mouse lung

Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A linear surrogate for optimising functions of an orthogonal matrix with applications in wave function theory

The technique of surrogate optimisation is to use a simpler function to approximate a complex function that is time-consuming to evaluate. Here we show that the maximum of a special type of surrogate function f(U)=Tr(AU),UϵO(n) is at A T (AA T ) 1/2 , and that there is one and only one local maximum both in SO(n) and O(n)–SO(n). This function f(U) has been found to be useful in various aspects of electronic structure theory, including proving the Carlson-Keller theorem, and localising orbitals. As one other example, we apply it here to optimise the ground state of molecules using the Generalised Valence Bond wavefunction.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Pitting Propagation Behavior on Low Alloy AISI 4130 (UNS G41300) Steel Exposed to Various Alkali and Alkaline Earth Metal Chlorides

Pit propagation studies were conducted to elucidate whether alkali and alkali earth metal cations such as Na+ and Rb+ present in the form of metal chloride salts such as RbCl affect pitting behavior in distinctly different manner than NaCl. Pit propagation studies were conducted on a low alloy steel using one-dimensional (1-D) pit method over pit depths from 300-1000 µm. LSV and EIS of planar electrodes of 4130 in a range of Cl- solutions were conducted and revealed no detected differences in impedance, open circuit, corrosion potential (Ecorr), passive current density (ipass), and pitting potential (Epit) as a function of salt type. In the case of one-dimensional pits during fast downward scan rates, the saturation potential (Esat) varied as a function of cation identity when pit depths were shallow. Mass transported limited current density also differed with cations in shallow pits when various alkali metal and alkaline metal cations were present. The limiting current density was increased for RbCl relative to NaCl The pit surface potential (Esurf) of activated pit surfaces reached Ecorr prior to establishing a condition where the pit electrolyte surface concentration (Csurf) was less than the critical concentration for active acidified pitting (i.e. Csurf<Ccrit) in this marginally passivating steel. For various Esurf and pit current density (ipit) combinations at constant Csurf where Ccrit< Csurf < Csat, E-log(i) plots were constructed using the method of Tianshu to create IR ohmic voltage corrected Tafel plots for various fixed pit solution concentrations. Under these conditions, the influence of cation identity on charge transfer controlled kinetics indicated slight differences in Tafel behavior where RbCl was slightly more aggressive than NaCl for low alloy steel oxidation. Differences in metal cation identity exert no effect on passive or breakdown on planar electrodes and only effect pit propagation stage in shallow pits.

Demarest, Chalres↗
DOE OSTI2020

Influence of Polymer Type and Carbon Nanotube Properties on Carbon Nanotube/Polymer Nanocomposite Biodegradation

The interaction of anaerobic microorganisms with carbon nanotube/polymer nanocomposites (CNT/PNC) will play a major role in determining their persistence and environmental fate at the end of consumer use when these nano-enabled materials enter landfills and encounter wastewater. Motivated by the need to understand how different parameters (i.e., polymer type, microbial phenotype, CNT characteristics) influence CNT/PNC biodegradation rates, we have used volumetric biogas measurements and kinetic modeling to study biodegradation as a function of polymer type and CNT properties. In one set of experiments, oxidized multiwall carbon nanotubes (O-MWCNTs) with a range of CNT loadings 0 – 5% w/w were incorporated into poly-e-caprolactone (PCL) and polyhydroxyalkanoates (PHA) matrices and subjected to biodegradation by an anaerobic microbial community. For each CNT/PNC, complete polymer biodegradation was ultimately observed, although the rate of biodegradation was inhibited above certain critical CNT loadings dependent upon the polymer type. Higher loadings of pristine MWCNTs were needed to decrease the rate of polymer biodegradation compared to O-MWCNTs, an effect ascribed principally to differences in CNT dispersion within the polymer matrices. Above ertain CNT loadings, a CNT mat of similar shape to the initial PNC was formed after polymer biodegradation, while below this threshold, CNT aggregates fragmented in the media. In situations where biodegradation was rapid, methanogen growth was disproportionately inhibited compared to the overall microbial community. Analysis of the results obtained from this study indicates that the inhibitory effect of CNTs on polymer biodegradation rate is greatest under conditions (i.e., polymer type, microbial phenotype, CNT dispersion) where biodegradation of the neat polymer is slowest. This new insight provides a means to predict the environmental fate, persistence and transformations of CNT-enabled polymer materials.

Frank, Benjamin↗
DOE OSTI2025

Resurfacing promotes antibacterial activity of a lipid A–binding nanobody

Nanobodies have been pursued as candidates for antimicrobial design due to their small size and versatile binding capacities, but direct antibacterial activity of a nanobody has yet to be described. Here, we employed a bacterial surface display platform to screen a synthetic library of nanobody variants for antimicrobial potential. We identified a candidate that binds the essential lipid A component of gram-negative lipopolysaccharide. Nonetheless, this nanobody required a weakened outer membrane to access its target and elicit its toxic activity. Borrowing from observations of innate immune proteins, we found that resurfacing nanobodies with positively charged residues enabled them to bind and perturb the gram-negative outer membrane, but this alone was not sufficient for toxic activity. However, when we resurface our lipid A-targeting nanobody, it gained the ability to disrupt the outer membrane and enact its antibacterial function against wild-type bacteria. This development of a dual-function nanobody that can reach and bind previously inaccessible gram-negative targets introduces a route for antimicrobial biologic advancement.

antibacterial↗
DOE OSTI2020

Retrieval of Cloud Liquid Water Using Microwave Signals from LEO Satellites: A Feasibility Study through Simulations

A novel method, using low Earth orbit (LEO) satellite microwave communication links for cloud liquid water measurements, is introduced in this paper. The feasibility of this approach is studied through simulations of the retrieval system including a LEO satellite communicating with a group of ground receivers equipped with signal-to-noise ratio (SNR) estimators, a synthetic cloud attenuation field and a tomographic retrieval algorithm. Rectangular and Gaussian basis functions are considered to define the targeted field. Simulation results suggest that the proposed least-squares based retrieval algorithm produces satisfactory outcomes for both types of basis functions. The root-mean-square error of the retrieved field is around 0.2 dB/km, with the range of the reference field as 0 to 2.35 dB/km. It is also confirmed that the partial retrieval of the cloud field is achievable when a limited number of receivers with restricted locations are available. The retrieval outcomes exhibit properties of high resolution and low error, indicating that the proposed approach has great potential for cloud observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cell‐type‐specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems

SUMMARY Bioenergy sorghum is a low‐input, drought‐resilient, deep‐rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4–5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell‐type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell‐type‐specific and cell‐preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell‐type‐specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell‐specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data ( https://mc‐lab.shinyapps.io/lcm‐dataset/ ).

09 BIOMASS FUELS↗