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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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The General Atomic and Molecular Electronic Structure System (GAMESS): Novel Methods on Novel Architectures
The primary focus of GAMESS over the last 5 years has been the development of new high-performance codes that are able to take effective and efficient advantage of the most advanced computer architectures, both CPU and accelerators. These efforts include employing density fitting and fragmentation methods to reduce the high scaling of well-correlated (e.g., coupled-cluster) methods as well as developing novel codes that can take optimal advantage of graphical processing units and other modern accelerators. Because accurate wave functions can be very complex, an important new functionality in GAMESS is the quasi-atomic orbital analysis, an unbiased approach to the understanding of covalent bonds embedded in the wave function. Finally, best practices for the maintenance and distribution of GAMESS are also discussed.
Confirmation of PNNL Quantitative Infrared Cross-Sections for Isobutane
The Pacific Northwest National Laboratory (PNNL) gas-phase database is a compilation of quantitative experimental (5, 25, 50 °C) infrared spectra of ca. 500 molecules, designed for in situ, standoff or remote sensing of gases and vapors at or near atmospheric pressure. The data are characterized by calibration on both the wavenumber and intensity axes. Recent papers have called into question the PNNL intensity values for isobutane, (2-methylpropane, HC(CH 3 ) 3 ) suggesting discrepancies of 30 to 40%. In this study we re-measure and re-examine the intensity values of isobutane using both similar and alternate methods to those used to generate the original PNNL database spectra. Here, indirect confirmation from literature data of homologous molecules and direct confirmation from new results confirm that for many band integrals across the isobutane spectrum, the original PNNL data are indeed accurate to within the reported 3% experimental uncertainty.
Vibrational Properties of a Naturally Occurring Semiconducting van der Waals Heterostructure
We present vibrational properties of Franckeite, which is a naturally occurring van der Waals heterostructure consisting of two different semiconducting layers. Franckeite is a complex layered crystal composed of alternating SnS 2 -like pseudohexagonal and PbS-like pseudotetragonal layers stacked on top of each other, providing a unique platform to study vibrational properties and thermal transport across layers with mass density and phonon mismatches. Using micro-Raman spectroscopy and first-principles Raman simulations, we found that the PbS-like pseudotetragonal structure is mostly composed of Pb 3 SbS 4 . We also discovered several low-frequency Raman modes that originate from the intralayer vibrations of the pseudotetragonal layer. Using density functional theory, we determined all vibrational patterns of Franckeite, whose signatures are observed in the Raman spectrum. By studying temperature-dependent Raman spectroscopy (300–500 K), we have found different temperature coefficients for both pseudotetragonal and pseudohexagonal layers. Here, we believe that our study will help understand the vibration modes of its complex heterostructure and the thermal properties at the nanoscale.
Nano-sized Metallic Nickel Clusters Stabilized on Dealuminated beta-Zeolite: A Highly Active and Stable Ethylene Hydrogenation Catalyst
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Role of Environment on the Shear-Induced Structural Evolution of MoS 2 and Impact on Oxidation and Tribological Properties for Space Applications
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Cobalamin-Dependent Radical S -Adenosylmethionine Enzymes: Capitalizing on Old Motifs for New Functions
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Computer-Aided Engineering of a Non-Phosphorylating Glyceraldehyde-3-Phosphate Dehydrogenase to Enable Cell-Free Biocatalysis
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Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training
Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu] 2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu] 2+ -catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.
Subventricular Accumulation of Cu in the Aging Mouse Brain Does Not Associate with Anticipated Increases in Markers of Oxidative Stress
Natural aging is associated with mild memory loss and cognitive decline, and age is the greatest risk factor for neurodegenerative diseases, such as Alzheimer’s disease. There is substantial evidence that oxidative stress is a major contributor to both natural aging and neurodegenerative disease, and coincidently, levels of redox active metals such as Fe and Cu are known to be elevated later in life. Recently, a pronounced age-related increase in Cu content has been reported to occur in mice and rats around a vital regulatory brain region, the subventricular zone of lateral ventricles. In our study herein, we have characterized lateral ventricle Cu content in a unique murine model of accelerated aging, senescence accelerated mouse-prone 8 (SAMP8) mice. Our results confirm an age-related increase in ventricle Cu content, consistent with the studies by others in wild-type mice and rats. Specifically, we observed Cu content to increase over the time frame 1 to 5 months and 5 to 9 months, but interestingly, no significant increase occurred between 9 and 12 months (although brain Cu content at 12 months was significantly elevated relative to 1 and 5 month-old animals). Despite the magnitude of Cu increase observed within the cells that comprise the subventricular zone of lateral ventricles (average 3 mM Cu, with isolated subcellular concentrations of 17 mM), we did not detect spectroscopic markers of thiol oxidation, protein aggregation, or lipid oxidation. The lack of evidence for oxidative stress in ex vivo animal tissue is in contrast to in vitro studies demonstrating that thiol, protein, and lipid oxidation is pronounced at these Cu concentrations. We suggest that our findings most likely indicate that the Cu ions in this brain region are sequestered in an unreactive form, possibly extended chains of Cu-thiolate complexes, which do not readily redox cycle in the aqueous cytosol. In conclusion, these results also appear to partially challenge the long-held view that age-related increases in brain metal content drive oxidative stress as we did not observe a concomitant association between age-related Cu increase and markers of oxidative stress, nor did we observe a net increase in Cu content between mice aged 9 and 12 months.
All-Perovskite Tandems Enabled by Surface Anchoring of Long-Chain Amphiphilic Ligands
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Comparing Advanced Bipolar Membranes for High-Current Electrodialysis and Membrane Electrolysis
Advanced bipolar membranes (BPMs) with low water-dissociation overpotential (ηwd) may enable new electrochemical technologies for electrolysis, fuel cells, acid–base synthesis, brine remediation, lithium-battery recycling, and cement production. However, these advanced BPMs have only been demonstrated in BPM water electrolysis (BPMWE) configurations where the BPM is under static compression by the porous-transport layers. It is important to study these BPMs in applications like electrodialysis where large degrees of static compression are not possible. We present a BPM electrodialysis (BPMED) platform to measure water-dissociation overpotential (ηwd) and compare BPMWE and BPMED systems. We show advanced BPMs with half the ηwd compared to commercial BPMs for BPMED while maintaining ∼90% current efficiency from 0.05–0.5 A cm–2. The BPMED ηwd values are, however, about 0.2 V higher at 0.5 A cm–2 than those for BPMWE. Regardless, these results show that BPMs developed and optimized in BPMWE applications are well-suited for next-generation high-current-density BPMED technologies.
X-ray Micro-Computed Tomography for Structural Analysis of All-Solid-State Battery at Pouch Cell Level
Characterizing the microstructure of all-solid-state batteries (ASSBs) during fabrication and operation is vital for their advancement, particularly as scaling to pouch cell levels introduces challenges in probing large-scale microstructural evolution. This work highlights the potential of synchrotron X-ray micro-computed tomography (sXCT) as a nondestructive, rapid (<30 min), and high-resolution technique for visualizing and quantifying key microstructural features, including overhang, porosity, contact loss, active surface area, and tortuosity, in all-solid-state pouch cells. The large field of view (up to millimeters) of sXCT enables detailed analysis at an industry-relevant scale, bridging the gap between laboratory research and commercial applications. Furthermore, integrating realistic sXCT-derived 3D models into multiphysics simulations could provide insights into chemo-mechanical degradation, particularly at the edges of the pouch cells, offering a pathway for designing robust, high-performance ASSBs. This perspective establishes sXCT as an indispensable tool for advancing both the understanding and the engineering of next-generation energy storage systems.
Scalable Route to Colloidal Ni x Co 3– x S 4 Nanoparticles with Low Dispersity Using Amino Acids
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Uncovering the Effects of Metal Contacts on Monolayer MoS 2
Metal contacts are a key limiter to the electronic performance of two-dimensional (2D) semiconductor devices. In this work, we present a comprehensive study of contact interfaces between seven metals (Y, Sc, Ag, Al, Ti, Au, Ni, with work functions from 3.1 to 5.2 eV) and monolayer MoS 2 grown by chemical vapor deposition. We evaporate thin metal films onto MoS 2 and study the interfaces by Raman spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction, transmission electron microscopy, and electrical characterization. We uncover that (1) ultrathin oxidized Al dopes MoS 2 n-type (>2 × 1012 cm –2 ) without degrading its mobility, (2) Ag, Au, and Ni deposition causes varying levels of damage to MoS 2 (e.g. broadening Raman E' peak from <3 to >6 cm –1 ), and (3) Ti, Sc, and Y react with MoS 2 . Reactive metals must be avoided in contacts to monolayer MoS 2 , but control studies reveal the reaction is mostly limited to the top layer of multilayer films. Finally, we find that (4) thin metals do not significantly strain MoS 2 , as confirmed by X-ray diffraction. These are important findings for metal contacts to MoS 2 and broadly applicable to many other 2D semiconductors.