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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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Lignin-derived aromatics can be efficiently demethylated with a high yield in an acidic lithium bromide solution under moderate conditions.
Ensembles of ab initio parameterized Frenkel-exciton model Hamiltonians for different perylene diimide dimer systems are used, together with various dissipative quantum dynamics approaches, to study the influence of the solvation environment and fluctuations in chromophore relative orientation and packing on the vibronic spectra of two different dimer systems: a π-stacked dimer in aqueous solution in which the relative chromophore geometry is strongly confined by a phosphate bridge and a side-by-side dimer in dichloromethane involving a more flexible alkyne bridge that allows quasi-free rotation of the chromophores relative to one another. These entirely first-principles calculations are found to accurately reproduce the main features of the experimental absorption spectra, providing a detailed mechanistic understanding of how the structural fluctuations and environmental interactions influence the vibronic dynamics and spectroscopy of solutions of these multi-chromophore complexes.
Diffusion of electrons over distances on the order of 100 μm has been observed in crystals of a small tetraheme cytochrome (STC) from Shewanella oneidensis [J. Huang et al . J. Am. Chem. Soc. 142, 10459–10467 (2020)]. Electron transfer between hemes in adjacent subunits of the crystal is slower and more strongly dependent on temperature than had been expected based on semiclassical electron-transfer theory. Here, in this work, we explore explanations for these findings by molecular-dynamics simulations of crystalline and monomeric STC. New procedures are developed for including time-dependent quantum mechanical energy differences in the gap between the energies of the reactant and product states and for evaluating fluctuations of the electronic-interaction matrix element that couples the two hemes. Rate constants for electron transfer are calculated from the time- and temperature-dependent energy gaps, coupling factors, and Franck–Condon-weighted densities of states using an expression with no freely adjustable parameters. Back reactions are considered, as are the effects of various protonation states of the carboxyl groups on the heme side chains. Interactions with water are found to dominate the fluctuations of the energy gap between the reactant and product states. The calculated rate constant for electron transfer from heme IV to heme Ib in a neighboring subunit at 300 K agrees well with the measured value. However, the calculated activation energy of the reaction in the crystal is considerably smaller than observed. We suggest two possible explanations for this discrepancy. The calculated rate constant for transfer from heme I to II within the same subunit of the crystal is about one-third that for monomeric STC in solution.
To meet the increasing demand for rare earth elements (REEs) essential for low carbon-intensity energy technologies, new methods are needed for selective REE extraction from unconventional resources. Only a few REEs have significant economic value, but isolation of target rare earths from co-occurring lanthanides is challenging due to their similar chemical behavior. We present a novel approach to enhance separation of specific REEs from lanthanide mixtures. Escherichia coli cells engineered with lanthanide binding tags (LBTs) were immobilized in nonadsorbing, permeable polyethylene glycol diacrylate beads and packed into continuous flow, fixed-bed columns. Breakthrough of 15 rare earths in the +3 oxidation state resulted in notable differences in adsorption selectivity, with greatest separation between europium (Eu) and lanthanum (La) due to competitive displacement. REE adsorption onto fixed-bed columns was predicted by coupling a surface complexation model to a calibrated one-dimensional dual porosity transport model that accounts for interbead advective and intrabead diffusive transport. A tradeoff between high-abundance, low-affinity native carboxyl sites and low-abundance, high-affinity engineered LBT sites dictates process recovery efficiency and selectivity. Key chemical and operational parameters are identified to maximize selective extraction of high-value lanthanides, achieving a threefold enhancement of Eu recovery relative to La in a mixed REE solution.
Deformation mechanisms in sputter-deposited crystalline Al/amorphous Si nanocomposites with nanolaminate or nanofibrous morphology are characterized by nanoindentation, micropillar compression testing and transmission electron microscopy (TEM). The nanofibrous composite having crystalline Al nanofibers with ~40–50 nm in length and 15–20 nm in diameter embedded in amorphous Si exhibits strain hardening to a maximum flow stress of 2.9 GPa and no shear band (SB) formation in compression up to plastic strain exceeding 24%. On the other hand, nanolaminate composite that is composed of 80 nm crystalline Al layers and 20 nm amorphous Si layers exhibits catastrophic SBs starting at plastic strains in the range of 5–10%. Cross-sectional TEM of the deformed samples reveals a high density of stacking faults and twin boundaries in Al nanofibers and no microshear bands in the nanofibrous composite, suggesting plastic deformation in amorphous Si phase and crystalline Al nanofibers. Further, molecular dynamics simulations revealed that the plastic deformation in amorphous Si phase in the cosputtered films could be favored by the decrease in flow strength of amorphous Si with increasing Al solute concentration trapped in Si.
Nitrate (NO 3 ) pollution in groundwater, caused by various factors both natural and synthetic, contributes to the decline of human health and well-being. Current techniques used for nitrate detection include spectroscopic, electrochemical, chromatography, and capillary electrophoresis. It is highly desired to develop a simple cost-effective alternative to these complex methods for nitrate detection. Therefore, a real-time poly (3,4-ethylenedioxythiophene) (PEDOT)-based sensor for nitrate ion detection via electrical property change is introduced in this study. Vapor phase polymerization (VPP) is used to create a polymer thin film. Variations in specific parameters during the process are tested and compared to develop new insights into PEDOT sensitivity towards nitrate ions. Through this study, the optimal fabrication parameters that produce a sensor with the highest sensitivity toward nitrate ions are determined. With the optimized parameters, the electrical resistance response of the sensor to 1000 ppm nitrate solution is 41.79%. Furthermore, the sensors can detect nitrate ranging from 1 ppm to 1000 ppm. The proposed sensor demonstrates excellent potential to detect the overabundance of nitrate ions in aqueous solutions in real time.
A material includes a porous particle that includes a metal ion imprinted polymer. The metal ion imprinted polymer is formed from a hydrophilic co-monomer, a metal containing polymerizable compound, and a cross-linking agent. The metal containing polymerizable compound includes at least one metal chelating ligand. The metal ion imprinted polymer includes a plurality of metal ion selective binding sites. A method includes flowing brine containing a metal ion through a reactor that includes the material. The method further includes discharging the brine from the reactor, contacting the porous particles with water, and pressurizing the reactor with carbon dioxide. The carbon dioxide reacts with the adsorbed metal ions to form a metal carbonate solution. The method further includes depressurizing the reactor to precipitate metal carbonate from the metal carbonate solution and discharging the metal carbonate solution from the reactor.
A method of producing a functionalized material that extracts metal ions from solution, the method comprising: (i) providing a precursor material having nitrile groups appended to its surface; and (ii) reacting said nitrile groups with hydroxylamine or a derivative thereof in the presence of a polar aprotic solvent at a temperature of 60-80° C. for at least 1 hour, to convert at least a portion of said nitrile groups to amidoxime and imide dioxime groups, followed by reaction with a base capable of hydrolyzing any remaining nitrile groups to carboxylic acid groups; wherein said functionalized material has a higher uranium absorption capacity than a functionalized material produced under same conditions except that the nitrile groups are reacted with hydroxylamine in only a protic solvent. The invention is also directed to functionalized materials produced by the above-described method, and methods for using the functionalized material for extracting metal ions from metal-containing solutions.
The dehydration of aqueous calcium and magnesium cations is the most fundamental process controlling their reactivity in chemical and biological phenomena, such as the formation of ionic solids or passing through ion channels. It holds particular relevance in light of recent advancements in the development of carbon capture techniques that rely on mineralization for long-term carbon storage. Specifically, dehydration of Ca 2+ and Mg 2+ is a key step in proposed carbon capture processes aiming to exploit the relatively high concentration of dissolved carbon dioxide in seawater via the formation of carbonate minerals from solvated Ca 2+ and Mg 2+ cations for sequestration and storage. Nevertheless, atomic-scale understanding of the dehydration of aqueous Ca 2+ and Mg 2+ cations remains limited. Here, we utilize rare event sampling via density functional theory molecular dynamics and embedded wavefunction theory calculations to elucidate the dehydration dynamics of aqueous Ca 2+ and Mg 2+ . Emphasis is placed on the investigation of the effect pH has on the stability of the different coordination environments. Here, our results reveal significant differences in the dehydration dynamics of the two cations and provide insight into how they may be modulated by pH changes.
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Single-cell mass spectrometry (MS) is advancing our understanding of metabolic pathways in heterogeneous cell populations; however, many techniques are slow or require disruptive sample preparations. This study evaluated coupling a modified HP D100 single-cell inkjet dispenser with liquid vortex capture-mass spectrometry (D100/LVC-MS). The D100 is a single-cell inkjet dispenser capable of titrating solutions and isolating single cells via disposable cassettes equipped with microfluidic channels and an impedance sensor. The LVC-MS enables high-throughput capture, lysis, and ionization of analytes for mass spectrometric analysis. The D100/LVC-MS system was characterized through titration and single-cell experiments. Propranolol titration demonstrated linearity across a broad concentration range using the D100/LVC-MS system. Additionally, Hep G2 hepatocarcinoma cells and Chlamydomonas reinhardtii algae were used to showcase the D100’s high-throughput or low-buffer-volume single-cell dispensing strategies. The D100/LVC-MS system’s performance was validated by evaluating tamoxifen-induced steatosis in Hep G2 cells. Tamoxifen, associated with nonalcoholic fatty liver disease in breast cancer patients following long-term use, was tested in Hep G2 cells at 20 µM for 72 h against DMSO-treated controls. High-throughput analysis of 500 cells per condition, completed in 25 min per run, demonstrated the system’s efficiency. The D100/LVC-MS system simultaneously quantified tamoxifen and measured triglycerides and phosphatidylcholines. Triglycerides were upregulated in the tamoxifen-treated cells and the results indicated two distinct cell populations, differing in tamoxifen and phosphatidylcholines levels, suggesting heterogeneity within the treated population. In conclusion, these findings highlight the D100/LVC-MS system as a cost-effective, high-throughput platform for single-cell metabolomics and lipidomics, with significant potential for evaluating metabolic alterations.
We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.
Bloch's theorem is the centerpiece of topological band theory, which itself has defined an era of quantum materials research. However, Bloch's theorem is broken by a perpendicular magnetic field, making it difficult to study topological systems in strong flux. For the first time, moiré materials have made this problem experimentally relevant, and its solution is the focus of this paper. We construct gauge-invariant irreps of the magnetic translation group at 2π flux on infinite boundary conditions, allowing us to give analytical expressions in terms of the Siegel theta function for the magnetic Bloch Hamiltonian, non-Abelian Wilson loop, and many-body form factors. We illustrate our formalism using a simple square lattice model and the Bistritzer-MacDonald Hamiltonian of twisted bilayer graphene, obtaining reentrant ground states at 2π flux under the Coulomb interaction.
The development of an economically competitive domestic supply of rare earth elements and yttrium (REY) is necessary for our nation’s economic growth and national security. The achievement of a secure domestic supply of REY requires not only the development of effective processes for recovery of REY from naturally occurring materials and/or recycled products, but also the development of downstream processes for the ultimate production of high-REY content solids. An impediment to the development of such processes is the scarcity of analytical methods that provide rapid determination of the process stream compositions. In this work, a membrane-based extraction process was used to selectively recover REYs from a dilute solution in the presence of much higher concentrations of Ca and Al. In tandem, the use of a portable spectrometer equipped with an immobilized zinc adeninate benzene tricarboxylate metal-organic framework sensing material makes possible the rapid detection of the presence of ppm concentrations of Tb and Eu in both weakly acidic and strongly acidic process streams within minutes. A solvent extraction processing time of 15-60 min maximized REY selectivity over gangue ions while achieving up to 80% REY and minimal gangue ion recovery. Taken together, these experiments highlight not only an innovative method for REY purification but also the importance of inexpensive, portable characterization methods for near real-time analysis of REY content.
GridGPT, a fine-tuned Generative AI model is designed for on-premise use in grid control rooms. This presentation will demonstrate how eGridGPT can seamlessly integrate with control room solutions to offer operators, engineers, and corporate users enhanced guidance and decision support. It is to show how this innovative AI solution can improve state estimation, boost variable energy forecasting, and optimize grid operations. By leveraging eGridGPT's unique features, audience will learn to unlock new levels of automation, predictive analytics, and reliability within their power systems, ultimately leading to reduced downtime and improved operational efficiency.
The Geothermal Operational Optimization with Machine Learning (GOOML) is a partnership between NREL and Upflow, NZ, awarded in response to the U.S. Department of Energy's Geothermal Technologies Office's Funding Opportunity Announcement (FOA) to expand the role of advanced analytics and automation in geothermal operations through machine learning. Partnering with industry (Contact Energy Limited ("Contact"), Ngati Tuwharetoa Geothermal Assets Limited ("NTGA"), Ormat Technologies Inc. ("Ormat") and Flow State Solutions Limited ("FSS"), GOOML was created to improve the operational efficiency of geothermal power plant steam fields through the analysis of historical operational data and the application of custom machine learning algorithms. NREL's contributions include machine learning, coding, and data management expertise as well as access to high-performance compute solutions. GOOML can increase geothermal operational efficiency through development of a digital system twin that can be utilized to provide optimal geothermal operating conditions for real-world geothermal fields. GOOML allows users to analyze field production histories in detail, develop models, and train machine learning algorithms to identify opportunities for increased geothermal efficiency, detect potential trouble, and allow predictive scenario modeling. Preliminary experiments have demonstrated a potential to increase total generation by as much as 12% through ML optimization of the utilization of existing steam field resources.
The electrochemical reduction of carbon dioxide (CO 2 ) presents an opportunity to close the carbon cycle and obtain sustainably sourced carbon compounds. In recent years, copper has received widespread attention as the only catalyst capable of meaningfully producing multi-carbon (C 2+ ) species. Notably carbon monoxide (CO) can also be reduced to C 2+ compounds on copper, motivating tandem systems that combine copper and CO-producing species, like silver, to enhance overall C 2+ selectivities. In this work, we examine the impact of layered-combinations of bulk Cu and Ag by varying the location and proportion of the CO-producing Ag layer. We report an effective increase in the C 2+ oxygenate selectivity from 23 % with a 100 nm Cu to 38 % for a 100: 15 nm Cu : Ag layer. Notably, however, for all co-catalyst cases there is an overproduction of CO vs Cu alone, even for 5 nm Ag layers. Lastly, due to restructuring and interlayer mobility of the copper layer it is clear that the stability of copper limits the locational advantages of such tandem solutions.