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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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ErIn is Tetraauricupride structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Er is bonded in a body-centered cubic geometry to eight equivalent In atoms. All Er–In bond lengths are 3.23 Å. In is bonded in a body-centered cubic geometry to eight equivalent Er atoms.
The position of mobile active and inactive ions, specifically ion insertion sites, within organic crystals, significantly affects the properties of organic materials used for energy storage and ionic transport. Identifying the positions of these atomic (and ionic) sites in an organic crystal is challenging, especially when the element has low X-ray scattering power, such as lithium (Li) and hydrogen, which are difficult to detect by powder X-ray diffraction. First-principles calculations, exemplified by density functional theory (DFT), are very practical for confirming the relative stability of ion positions in materials. However, the lack of effective strategies to identify ion sites in these organic crystalline frameworks renders this task extremely challenging. This work presents two algorithms: the (i) efficient location of ion insertion sites from extrema in electrostatic local potential and charge density (ELIISE), and the (ii) ElectRostatic InsertioN (ERIN), which leverage charge density and electrostatic potential fields accessed from first-principles calculations, combined with the Simultaneous Ion Insertion and Evaluation (SIIE) workflow. SIIE inserts all ions simultaneously—to determine ion positions in organic crystals. We demonstrate that these methods accurately reproduce known ion positions in 16 organic materials and identify previously overlooked low-energy sites in tetralithium 2,6-naphthalenedicarboxylate (Li4NDC), an organic electrode material, highlighting the importance of inserting all ions simultaneously, as in the SIIE workflow. These algorithms are also integrated with off-the-shelf machine learning potentials, yielding promising results comparable to first-principles findings.
Produced water data processed into the NEWTS data format for easy input into aqueous chemistry modeling software, including oil & gas and coal bed methane produced waters, and geothermal waters. Includes information on rare earth elements (REEs) and critical minerals (CMs). Case studies are included to demonstrate solved streams using aqueous chemistry software. An input template is provided for modeling stream data in OLI Studio. Original data from: Quillinan, Scott, Nye, Charles, Engle, Mark, Bartos, Timothy T., Neupane, Ghanashyam, Brant, Jonathan, Bagdonas, Davin, McLing, Travis, McLaughlin, J. Fred, Phillips, Erin, Hallberg, Laura L., Shahabadi, Mahdi, and Johnson, Matthew. Assessing rare earth element concentrations in geothermal and oil and gas produced waters: A potential domestic source of strategic mineral commodities (Final Report). United States: N. p., 2018. Web. doi:10.2172/1509037.
DOME Ecosystem – Troy Burnett DOME Construction Update – Scott Smith/Brandon Hill Supplemental Shielding Design – Chance Price/ Kyle Reno (Enercon) Polar Crane Status Update – Chance Price Fuel Storage at INL – Todd Sherman DOME User Guide – Marvin Fielding FEEED Overview and Update –Troy Burnett Westinghouse – Erin Orga USNC – Wes Deason Radiant – Elliott Korb
To address our climate emergency, “we must rapidly, radically reshape society”—Johnson & Wilkinson, All We Can Save. In science, reshaping requires formidable technical (cloud, coding, reproducibility) and cultural shifts (mindsets, hybrid collaboration, inclusion). We are a group of cross-government and academic scientists that are exploring better ways of working and not being too entrenched in our bureaucracies to do better science, support colleagues, and change the culture at our organizations. We share much-needed success stories and action for what we can all do to reshape science as part of the Open Science movement and 2023 Year of Open Science.
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Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.
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Abstract Understanding protein structure and function relationships in cellulose synthase (CesA), including divergent isomers, is an important goal. Here, we report results from mutant complementation assays that tested the ability of sequence variants of AtCesA7, a secondary wall CesA of Arabidopsis thaliana , to rescue the collapsed vessels, short stems, and low cellulose content of the irx3‐1 AtCesA7 null mutant. We tested a catalytic null mutation and seven missense or small domain changes in and near the AtCesA7 FTVTSK motif, which lies near the catalytic domain and may, analogously to bacterial CesA, exist within a substrate “gating loop.” A low‐to‐high gradient of rescue occurred, and even inactive AtCesA7 had a small positive effect on stem cellulose content but not stem elongation. Overall, secondary wall cellulose content and stem length were moderately correlated, but the results were consistent with threshold amounts of cellulose supporting particular developmental processes. Vibrational sum frequency generation microscopy allowed tissue‐specific analysis of cellulose content in stem xylem and interfascicular fibers, revealing subtle differences between selected genotypes that correlated with the extent of rescue of the collapsing xylem phenotype. Similar tests on PpCesA5 from the moss Physcomitrium (formerly Physcomitrella ) patens helped us to synergize the AtCesA7 results with prior results on AtCesA1 and PpCesA5. The cumulative results show that the FTVTxK region is important for the function of an angiosperm secondary wall CesA as well as widely divergent primary wall CesAs, while differences in complementation results between isomers may reflect functional differences that can be explored in further work.
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Enzymatic depolymerization of poly(ethylene terephthalate) (PET) has emerged as a promising approach for polyester recycling, and, to date, many natural and engineered PET hydrolase enzymes have been reported. For industrial use, PET hydrolases must achieve high depolymerization extent and exhibit excellent thermostability. Here, we engineered a natural PET hydrolase, Polyester Hydrolase Leipzig #7 (PHL7), through rational design and directed evolution using a high-throughput screening platform. Four new enzymes were engineered with enhanced properties compared with the parent enzyme, wild-type PHL7 (PHL7-WT), and other benchmark PET hydrolases, under the tested conditions. In bioreactors, the exemplary engineered enzyme, PHL7-Jemez, exhibited improved ability to depolymerize amorphous PET film compared with PHL7-WT at 2.9% and 20% substrate loadings, with 37% and 270% higher hydrolysis, respectively, after 48 h. This study develops several state-of-the-art PET hydrolases and demonstrates a directed evolution platform to engineer high-performance enzymes, which can accelerate enzyme discovery toward improved biocatalytic recycling.
Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using matrix-assisted laser desorption/ionization and tandem mass spectrometry (MALDI-MS/MS), and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic N-butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations. Conformational distributions of three compositionally identical library sequences were corroborated through atomistic simulations and ion mobility spectrometry coupled with liquid chromatography. A data-driven strategy was developed using existing sequence variables and data-derived “motifs” to inform a machine-learning algorithm toward conformation prediction. Here, this multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery of materials with conformational control.
Developing robust numerical models of dynamic surface tension dominated multiphase systems is an ongoing challenge, especially in scenarios with large density and viscosity ratios. This is critical to the design and understanding of various physical and engineering systems, such as fluidized beds, fuel injectors, and drug delivery schemes. Much of the computational work in surface tension dominated multiphase flows has employed the continuum surface force method (CSF) of Brackbill et al. [1], which recasts surface tension from a surface force to a volumetric force that can be imposed in the vicinity of an interface. The CSF method produces accurate results across a variety of systems, however it relies on the identication of surface normals, which can be unreliable under certain conditions. Alternative methods of simulating surface tension have been proposed. Here the advantages and disadvantages of the CSF method in comparison to a pairwise forces (PF) method proposed by Tartakovsky and Panchenko [2] are explored. The CSF and PF methods are used in a smoothed particle hydrodynamics (SPH) framework to model dynamic systems. Results are compared to existing test cases from the literature and to analytic solutions derived from fundamental normal mode behavior of bubbles and droplets. A physical system for which the PF method is more stable and physically appropriate than the CSF method is identied.
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A new platinum group metal (PGM)-free proton exchange membrane fuel cell (PEMFC) cathode catalyst materials, synthesized using the VariPore™ method by Pajarito Powder, LLC, are characterized for their structure and activity. The physico-chemical analysis of the iron-nitrogen-carbon (Fe-N-C) electrocatalysts show mesoporous carbon material effectively doped with iron and nitrogen. The materials have an average pore size of 7-8 nm and high specific surface area. The Fe-N-C catalysts exhibit good oxygen reduction reaction (ORR) activity in 0.5 M H2SO4 electrolyte with high half-wave potential and sustainable electrochemical stability over 10,000 repeated potential cycles with insignificant losses in their activities. As cathode catalysts in a PEMFC, the Fe-N-C materials deliver remarkably good fuel cell performance at low overpotential approaching that of the commercial Pt catalyst. The high ORR electrocatalytic activity of these Fe-N-C catalysts is credited to the synergy between nitrogen-moieties, specifically pyrrolic-N, pyridinic-N, and graphitic-N, and iron in addition to the high mesoporosity that facilitate an effective reaction path in boosting the electrocatalytic activity and stability.