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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Chemical detection system with at least one electronic nose

A system for predicting one or more analytes based on outputs from thin film gas sensors is provided. The system may comprise an electronic nose (e-nose). The e-nose may comprise the gas sensors and a first processor. The system may further comprise a second processor. The second processor may be configured to receive the output from each of the gas sensors, evaluate a prediction accuracy using an evaluation parameter of each of a plurality of models which are trained and tested and select a model from among the plurality of models to deploy based on a comparison of the evaluation parameter for each of the plurality of models and use the same. The second processor may also receive, an output of each of the gas sensors caused by unknown one or more analytes; and predict, using the deployed model, the one or more analytes that causes the output.

Ivanov, Ilia N.↗
DOE OSTI2022

Performance evaluation of a wrapped around condenser for heat pump water heater applications

In this report, accurate performance evaluation of a wrapped-around condenser for heat pump water heaters (WHPWH) is critical since the COP of the system depends heavily on thermal stratification and condenser design. An analytical, quasi-steady state heat balance method has been developed to determine the optimal spacing between adjacent channels, tube diameter, tube shape, and total refrigerant charge amount for the condenser. The heat transfer rate is compared among three approaches: the refrigerant thermodynamic model, the condenser-wrap fin model, and the analytical natural convection model on the inside of the tank for the three regions based on the refrigerant phase (e.g., super-heated, saturated, or subcooled). The heat transfer rate was predicted by a combination of slug-plug and annular flow for a D-shaped helical tube. Experimental data were used as boundary conditions for validating the model. The rate of heat transfer based on tube shape and tank wall temperatures was compared by CFD analysis. Parametric analysis indicates a tradeoff between refrigerant mass, pressure drop, tube diameter, and tube length to maximize the heat transfer rate. The model suggests the saturated region length can be extended by 400%, and the condenser pressure drop can be reduced by 23% with an optimal spacing pattern.

42 ENGINEERING↗
DOE OSTI2020

Multireference Electron Correlation Methods: Journeys along Potential Energy Surfaces

Multireference electron correlation methods describe static and dynamical electron correlation in a balanced way and, therefore, can yield accurate and predictive results even when single-reference methods or multiconfigurational self-consistent field theory fails. One of their most prominent applications in quantum chemistry is the exploration of potential energy surfaces. This includes the optimization of molecular geometries, such as equilibrium geometries and conical intersections and on-the-fly photodynamics simulations, both of which depend heavily on the ability of the method to properly explore the potential energy surface. Because such applications require nuclear gradients and derivative couplings, the availability of analytical nuclear gradients greatly enhances the scope of quantum chemical methods. This review focuses on the developments and advances made in the past two decades. A detailed account of the analytical nuclear gradient and derivative coupling theories is presented. Emphasis is given to the software infrastructure that allows one to make use of these methods. Notable applications of multireference electron correlation methods to chemistry, including geometry optimizations and on-the-fly dynamics, are summarized at the end followed by a discussion of future prospects.

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

Aliphatic Amines from Waste Polyolefins by Tandem Pyrolysis, Hydroformylation, and Reductive Amination

Pyrolysis of waste plastics can produce a product mixture with a high concentration of olefins (>50 wt%). The olefins, as building blocks in the petroleum industry, are potential precursors for valuable commodity chemicals with higher values (>$2000 per ton). In this work, we produce aldehydes by hydroformylation of the olefins present in pyrolysis oil from colored post-consumer recycled high-density polyethylene (PCR-HDPE). The obtained aldehydes in the oil are then converted into aliphatic amines via reductive amination with a Ru/C catalyst. The aminated oil was characterized by multiple analytical chemistry techniques including elemental analysis, nuclear magnetic resonance spectroscopy, high-resolution liquid chromatography-mass spectrometry, and gas chromatography-mass spectrometry with a Polyarc flame ionization detector. The concentration of metals in the PCR-HDPE and oil changes during the tandem processes, showing limited effects of these elements (e.g., Al, Ca, Fe, Mg, Ti, Zn) on hydroformylation and reductive amination. Additionally, we demonstrated that reductive amination of aldehydes with varied carbon numbers and branching properties can be achieved in the presence of a complex mixture, including paraffins and aromatics. The results indicate that waste plastics have the potential to serve as a renewable source for mono and di aliphatic amines, thereby diminishing reliance on fossil feedstocks as the current primary amine source.

aliphatic amines↗
DOE OSTI2024

Production of NBL PO plutonium isotopic standards CRM 136A, CRM 137A, and CRM 138A

Lawrence Livermore National Laboratory (LLNL) produced approximately 1 mg units of purified Pu from the parent New Brunswick Laboratory (NBL) certified reference materials (CRM) 137, 136 and 138 isotopic standards for the NBL Program Office (NBL PO) as detailed in the revised project plan. As stated in the plan, three representative units of each material were dispensed at the same time to be characterized at LLNL and provide informational values for the quantity of Pu and progeny nuclides in each unit. NBL PO intends to collect and evaluate isotopic measurement data from multiple laboratories and certify these materials as new isotopic standards C137A, C136A, and C138A, respectively. Soon after production eleven units of each analytical standard were shipped to other analytical laboratories for the certification effort, and 121, 119, and 119 units of C137A, C136A and C138A, respectively were shipped to Mickey Finch at Y12 NBL distribution center in November 2024 (30-gallon drum, TID 003777 PRL 7000005043). Residual purified Pu from the effort is being stored indefinitely in B332 at LLNL. Detailed methods can be found in the production plan, and this report describes and documents aspects of the production effort that are not in or were modified from the original production plan.

and nuclear chemistry↗
DOE OSTI2026

Advanced Characterization of Wastewaters with a Focus on the Environment & Economics (Final Technical Progress Report)

This project, funded by the U.S. Department of Energy under award DE-FE0032457 and conducted at the University of Illinois at Urbana-Champaign, focused on advancing the characterization of coal combustion residual (CCR) effluents. The primary objectives were to develop analytical methods for detecting major cations, anions, trace metals, and rare earth elements (REEs) in CCR wastewater, assess environmental impacts, and explore opportunities for resource recovery. The project advanced the development of a "One-Shot" analysis system designed to simultaneously analyze multiple analytes in CCR effluents.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Fueling the Future: The Emergence of Self-Powered Enzymatic Biofuel Cell Biosensors

Self-powered biosensors are innovative devices that can detect and analyze biological or chemical substances without the need for an external power source. These biosensors can convert energy from the surrounding environment or the analyte itself into electrical signals for sensing and data transmission. The self-powered nature of these biosensors offers several advantages, such as portability, autonomy, and reduced waste generation from disposable batteries. They find applications in various fields, including healthcare, environmental monitoring, food safety, and wearable devices. While self-powered biosensors are a promising technology, there are still challenges to address, such as improving energy efficiency, sensitivity, and stability to make them more practical and widely adopted. This review article focuses on exploring the evolving trends in self-powered biosensor design, outlining potential advantages and limitations. With a focal point on enzymatic biofuel cell power generation, this article describes various sensing mechanisms that employ the analyte as substrate or fuel for the biocatalyst’s ability to generate current. Technical aspects of biofuel cells are also examined. Research and development in the field of self-powered biosensors is ongoing, and this review describes promising areas for further exploration within the field, identifying underexplored areas that could benefit from further investigation.

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

Current–Induced Ion Concentration Polarization at a Perfect Ion–Exchange Patch in an Infinite Insulating Wall

This research examines theoretically the ion concentration polarization, ion fluxes, and electrostatic fields near an ion exchange patch in the wall of an electrified fluidic channel. These phenomena are important in related microfluidic ion-preconcentration systems. Under an electric field, counterions enter the ion-exchange patch at one side and leave at the other, with salt depletion occurring near the entrance and accumulation near the exit. Furthermore, the high patch conductivity and the concentration profiles lead to local electric field perturbations that may facilitate preconcentration. This study includes analytical expressions of ion concentrations and electrochemical potentials at small to moderate electric fields, as well as numerical simulations. Additionally, a simple matrix of polynomial coefficients (obtained via fitting of numerical data) enables analytical calculation of the 2-dimensional concentration profiles at all electric fields within the range investigated in the numerical simulations. This is possible because a single dimensionless parameter controls this problem.

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

Proton nuclear magnetic resonance (1H NMR) of flammable organic chemicals in radioactive high–level supernatant waste at the Savannah River Site (SRS)

The Savannah River Site stores approximately 36 million gallons of radioactive and hazardous waste that contains approximately 245 million curies. The waste is sent through various chemical processes to reduce its volume and to separate various components. The facility plans to replace formic acid (a chemical used to reduce soluble mercury) with glycolic acid. Recycle solution with glycolate may flow back to the tank farm, where the glycolate can generate hydrogen gas by thermal and radiolytic mechanisms. The current analytical method for detecting glycolate (ion chromatography) in supernatant requires a large dilution to reduce interference from the nitrate anions. Hydrogen nuclear magnetic resonance is an analytical method that requires less sample dilution. It takes advantage of the CH 2 group in glycolate. Liquid samples were spiked with four different levels of glycolate to build a calibration line, as it is recommended in the standard addition method. The detection and quantitation limits determined were 1 and 5 ppm, respectively, for 32 scans, which is well below the process limit of 10 ppm. In one test, 800 scans of a supernatant spiked with 1 ppm glycolate resulted in a -CH 2 peak with a signal-to-noise ratio of 36.

1H↗
DOE OSTI2023

Carbon nanospikes have improved sensitivity and antifouling properties for adenosine, hydrogen peroxide, and histamine

Carbon nanospikes (CNSs) are a new nanomaterial that has enhanced surface roughness and surface oxide concentration, increasing the sensitivity for dopamine detection. However, CNS-modified electrodes (CNSMEs) have not been characterized for other neurochemicals, particularly those with higher oxidation potentials. The purpose of this study was to evaluate CNSMEs for the detection of adenosine, hydrogen peroxide (H 2 O 2 ), and histamine. The sensitivity increased with CNSs, and signals at CNSMEs were about 3.3 times higher than CFMEs. Normalizing for surface area differences using background currents, CNSMEs show an increased signal of 4.8 times for adenosine, 1.5 times for H 2 O 2 , and 2 times for histamine. CNSMEs promoted the formation of secondary products for adenosine and histamine, which enables differentiation from other analytes with similar oxidation potentials. Furthermore, CNSs also selectively enhance the sensitivity for adenosine and histamine compared to H 2 O 2 . A scan rate test reveals that adenosine is more adsorption-controlled at CNS electrodes than CFMEs. CNSMEs are antifouling for histamine, with less fouling because the polymers formed after histamine electrooxidation do not adsorb due to an elevated number of edge planes. CNSMEs were useful for detecting each analyte applied in brain slices. Because of the hydrophilic surface compared to CFMEs, CNSMEs also have reduced biofouling when used in tissue. Therefore, CNSMEs are useful for tissue measurements of adenosine, hydrogen peroxide, and histamine with high selectivity and low fouling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Skin and Proximity Effects in Electrodes and Furnace Shells

A review of two-dimensional (2D) analytical models of skin and proximity effects in large industrial furnaces with three electrodes arranged in an equilateral triangle is given. The models cover three different cases: one electrode only, three electrodes where two are approximated by line currents, and induced shell currents where all electrodes are approximated by line currents. The first two models show how the skin and proximity effects depend on electrode material properties and size, and the distance between the electrodes. The third model shows how the strength of the induced shell currents will depend on electrode position and furnace size. These models are compared to numerical studies including distributed electrodes and shell currents. The analytical models are accurate when induced shell currents can be disregarded. However, strong shell currents may have a significant impact on the current distribution within the electrodes. This electrode-shell proximity effect competes with the electrode-electrode proximity effect. Finally, the 2D models have been compared with three-dimensional (3D) case studies of large industrial furnaces. In 3D, the shell currents are significantly smaller than what are predicted by the 2D models, but they are sufficiently strong to cause a significant correction of the electrode current density.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review

Gas chromatography (GC) is undoubtedly the analytical technique of choice for analyzing the composition of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC×GC) has evolved, fuel analysis has often been highlighted in scientific reports, as their complexity allows for illustration of the impressive peak capacity gains afforded by GC×GC. Indeed, several research groups in recent years have applied GC×GC and chemometrics to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC×GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC×GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC×GC analyses of fuels for compliance and forensic applications, in an effort to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC×GC technology (column stationary phases and modulators) and several chemometric techniques will be provided. More specifically, chemometric techniques will be broken down into (1) signal pre-processing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed, with particular emphasis on the demonstrated success of the employed chemometric techniques. Overall, this review will hopefully make 1D-GC and GC×GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.

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

Enhanced liquid metal wetting on oxide surfaces via patterned particles

Liquid metals typically do not intrinsically wet oxide surfaces due to chemical dissimilarity, preventing the formation of well-bonded interfaces in many applications. This paper investigates how the wetting and spreading of liquid silver (Ag) droplets on oxide surfaces can be enhanced via patterned nickel (Ni) particles using multiscale modeling. First, a force field (FF) for an Ag/yttria-stabilized-zirconia system was developed by fitting the interfacial binding energy to density-functional theory (DFT) calculations. Then, molecular dynamics (MD) simulations using these FF parameters revealed that the good wetting of Ag on Ni caused Ag droplets to be attracted to the Ni particles, promoting spreading. Thus, the optimal hexagonal Ni pattern was shown to enhance the wetting area by 224% compared to a bare oxide surface. Second, an analytical model predicting the maximum possible enhancement in wetting area as a function of the intrinsic wetting angle, size of the Ag droplet, and size of the Ni particles was derived and validated with MD simulations, to generalize this method to other substrates. Here, both the analytical model and MD results suggested that the ability of Ni patterns to enhance the Ag wetting area becomes more significant as the wettability of substrate becomes worse.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of disorder on thermodynamic instability of binary Rare-earth – Nickel – Palladium compounds

In this work, we have investigated the thermodynamic stability of disordered rare-earth phases SmX 2 and Sm 10 X 21 (X=Ni, Pd) using machine-learning based analytical descriptor and first-principles density functional theory methods. The absence of Laves phase compounds in R-Pd binary systems is a longstanding problem of rare earth science: even though Ni and Pd belong to the same group of the periodic table and have similar electronic structure, the Pd compound crystallizes in a monoclinic (C2/m) phase with 10:21 stoichiometry, i.e., Sm 10 Pd 21 , while the Ni compound adopts a cubic Laves phase (MgCu 2 ) structure. To understand this contrasting phase stability, we performed thermodynamic convex hull analysis of Sm x Ni 1-x and Sm x Pd 1-x binary systems, which is experimentally validated using powder X-ray diffraction (PXRD) analyzes of polycrystalline Sm(Ni x Pd 1-x ) 2 samples with x=0, 0.5, and 1. A detailed electronic-structure (band-structure, charge density, and Fermi-surface) analysis of the differences between SmNi 2 /SmPd 2 and Sm 10 Ni 21 /Sm 10 Pd 21 compounds provides the quantum mechanical origin of the unfavorable mixing of Pd with Ni in cubic Laves phase. We show that the stability of Sm-Pd in 10:21 stoichiometry arises from improved intra-/inter-layer 5d-4d bonding compared to the 1:2 stoichiometry. Our work emphasizes the importance of ab-initio methods and computationally inexpensive analytical descriptors for the detailed analysis of thermodynamic and electronic properties of hard-to-prepare rare-earth compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Kinklike structures in an arcsin real scalar dynamics

Highlights: • Kinklike analyses in a real scalar theory with an arcsin dynamic. • First-order formalism implementation and deformation procedure in an arcsin scalar dynamics. In this paper, we analyze kink-like analytical solutions in a real scalar theory with an arcsin dynamics inspired by the arcsin electrodynamics presented in Kruglov (2015). This analysis is done by means of the first-order formalism. This formalism provides a framework where the equations of motion can be simplified by preserving the linear stability of the theory. In this work, the deformation procedure is implemented with the aim of finding exact solutions in systems with generalized dynamics. Along the paper, we explore how the first-order formalism is implemented in the arcsin kinetics and how such a term influences the kink-like solutions. As a part of the result of our paper, we show that the kink-like solutions are similar to the ones obtained in the standard scalar kinetic theory. We also show that the extra parameter, that controls the non-linearities of the model, plays an essential role in the energy densities and stability potentials. These quantities vary according to this parameter. The goals here are to show how the first-order framework is implemented in this arcsin scenario and to present the analytical kink-like solutions that can be found by means of the first-order framework and deformation method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Analysis of convection heat transfer on multiscale rough superhydrophobic and liquid infused surfaces

Multiscale rough superhydrophobic or slippery liquid infused porous surfaces have gained much interest in recent years for their improved transport phenomena properties. While there have been several studies on drag reduction and condensation on non-wetting surfaces, convection heat transfer that is important in many thermal and thermochemical applications has not been addressed systematically. Here, this article utilizes a fractal description of rough surface topographies to develop analytical models for the Nusselt number and the thermal hydraulic factor for fluid flow and heat transfer inside a cylinder with non-wetting surfaces. For air-infused superhydrophobic surfaces, the model considers the dynamic stability of the air/fluid interface in the asperities. Using the analytical formulations and the stability criteria, systematic studies are presented on the effects of the fractal surface parameters, cylinder radius and Reynolds number on the convective heat transfer characteristics, from which surface texture design maps are developed for maximizing the convection heat transfer. It is shown that multiscale non-wetting surfaces are most effective in the range of lower Reynolds number and small cylinder radius for achieving the best convective heat transfer and thermal hydraulic performance. Applying the models to actual non-wetting surface topographies fabricated using electrodeposition and chemical etching, it is shown that contrary to prevailing notion, superhydrophobicity, characterized by the highest contact angles, does not always lead to the maximum convective heat transfer performance, and that under certain fluid flow conditions, hydrophobic surfaces may offer a greater thermal performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Modeling gas release from a Bingham plastic slurry and deconvoluting measured data

Physics-based models for predicting the off-gassing characteristics of agitated slurries are presented. This approach decomposes the system into two separate but coupled slurry and headspace models. The physics driving bubble transport through the slurry and gas mixing within the headspace are discussed. An analytical expression for predicting the time evolution of the headspace concentration using first principles theory is also presented. Predictions from the numerical models, as well as expectations from the analytical solution, both agree with measured off gassing data for two different experimental operating conditions. After benchmarking the numerical model predictions against experimental data, the model was used to make predictions for gas release from a full-scale vessel and to perform sensitivity analyses to examine the sensitivity of gas release to parameters such as yield stress, consistency index, slurry density, bubble size, bubble concentration, and impeller speed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗