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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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An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
A suite of experimental infrastructure projects has been developed by the National Reactor Innovation Center to accelerate advanced reactor demonstrations and facilitate their development, addressing crucial gaps in data, materials characterization, and modeling. First, the Molten Salt Thermophysical Examination Capability (MSTEC) provides a specialized platform for post-irradiation characterization of molten salt reactor fuel, coolant salts, and structural materials, essential for supporting the design and operation of advanced reactors and future commercial molten salt reactor development and licensing. The Virtual Test Bed (VTB) complements these efforts by leveraging advanced modeling and simulation tools to evaluate reactor performance and safety. Serving as a library of reference models, the VTB offers a database of multiphysics reactor models, facilitating rapid safety evaluations and includes continuous software quality assurance, crucial for accelerating deployment while maintaining reliability. Additionally, the Helium Component Test Facility (HeCTF) addresses the need for high-temperature helium-cooled reactor component testing. As the first-of-its-kind facility in the United States, HeCTF emulates high-temperature gas reactor conditions, reducing time and cost associated with component validation, thereby accelerating reactor development. Finally, In-cell Thermal Creep Frames provide a unique solution for obtaining thermal creep data from irradiated materials, critical for materials qualification and licensing. Developed by the National Reactor Innovation Center, these compact frames enable the examination of previously irradiated materials, overcoming traditional limitations and enhancing the understanding of mechanical properties crucial for reactor development. Collectively, these experimental infrastructure projects form a comprehensive framework aimed at expediting advanced reactor demonstrations, fostering innovation, and ensuring the viability of next-generation nuclear energy solutions.
Here, the relationship between the composition of the internal defect states, spectral properties, and correlated electronic structures of wurtzite zinc oxide (ZnO) crystals under 645 MeV Xe 35+ irradiation is systematically investigated, employing experimental characterizations combined with first-principle calculations. Based on the ion irradiation-induced thermal expansion and relaxation processes, the high concentration of vacancy/interstitial defects produced from the transient disordered phase in molten track states trigger photoelectric changes, as follows: i) the generation of internal defect states effectively reduces the intrinsic bandgap (3.25 eV → 2.66 eV); ii) a large number of defective active sites inhibits the recombination between electron–hole pairs, causing dark conductance and photoconductance to increase with increasing damage levels until optimal fluence is achieved. Based on the density functional theory (DFT) with the GGA + U (GGA = generalized gradient approximation) method, the defective models associated with the different electronic structures, density of states, formation energy, and the nature of the chemical bonding are established. The narrowing of the bandgap observed experimentally and the enhancement of carrier concentration originating from the internal electron defect states are qualitatively verified, therefore laying the foundation for designing future nanoscale photoelectronic devices and microelectronics applications.
Plutonium's radioactivity provides functionality for nuclear batteries, nuclear reactors, etc., but its complex electronic properties harbor strongly correlated behavior giving rise to a host of interesting phenomena including the presence of a ca. 25% volume collapse between δ-Pu and α-Pu. The complex bonding environments of the ground state allotrope, α-Pu, serve as a unique testing ground for new computational and experimental approaches within the Pu science community. For the first time, a combination of novel ansatzes is used in all-electron density functional theory (DFT) and pair distribution functions (PDF) obtained from high-Q X-ray diffraction to study the bonding behavior in α-Pu. This first experimental and theoretical co-informed description of local bonding behavior for α-Pu reveals covalent bonds, which is a topic that remains of interest in this allotrope. The covalent bonding present at the atomistic level accounts for several of α-Pu's macropscopic properties (e.g., Poisson's ratio) that in turn explains its physical functionalities relative to other allotropic phases like δ-Pu.
Abstract Porous graphene and other atomically thin 2D materials are regarded as highly promising membrane materials for high‐performance gas separations due to their atomic thickness, large‐scale synthesizability, excellent mechanical strength, and chemical stability. When these atomically thin materials contain a high areal density of gas‐sieving nanoscale pores, they can exhibit both high gas permeances and high selectivities, which is beneficial for reducing the cost of gas‐separation processes. Here, recent modeling and experimental advances in nanoporous atomically thin membranes for gas separations is discussed. The major challenges involved, including controlling pore size distributions, scaling up the membrane area, and matching theory with experimental results, are also highlighted. Finally, important future directions are proposed for real gas‐separation applications of nanoporous atomically thin membranes.
Abstract Lasing at the nanoscale using plasmonic nanoparticles offers the prospect of strong field concentration, hence strong light−matter interactions, low lasing thresholds, and ultrafast operation. However plasmonic nanoparticles suffer from high dissipative and radiative losses, the latter being rigidly tied to the shape of the nanoparticles and symmetry of their localized plasmon resonant modes. To overcome this limitation, recent theoretical work proposes using direct lasing into dark surface states to construct lasers that conceptually allow for independent implementation of the lasing state and its coherent radiation output. Here, lasing in dark resonant states of a metasurface laser and controllable coherent outcoupling of radiation with the aid of a tightly coupled, weakly radiative metasurface are demonstrated experimentally. The laser is implemented using a thin, low‐loss dielectric film supporting surface mode‐like dark dielectric bound states and the coupling metasurface is composed of small non‐resonant scatterers that controllably but weakly perturb the dark mode and turn it partially bright. Distinct far‐field signatures that can be observed experimentally for both dark and bright lasing are identified and demonstrated. The scalability of this design, here implemented for lasing in the near‐infrared, enables large‐aperture ultra‐thin coherent light sources with controllable emission from the infrared to the visible.
Abstract An increasing demand for buildings, transportation systems and civil infrastructure development has driven expansion of cement consumption world‐wide, producing a significant increase in related global energy demand. With approximately 7% of the world‐wide industrial energy consumption (10.7 exajoules [EJ]), the cement industry is the third most energy intensive industrial processes and a key component for concrete, the most consumed composite material in the global construction industry. In cement manufacturing, the cement kiln accounts for most of the energy consumption in the production process. As the heart of a cement plant, the cement kiln is where the kiln feed primarily containing calcium oxide (CaO), silica (SiO 2 ), alumina (Al 2 O 3 ), and iron (Fe 2 O 3 ) are thermally and chemically transformed into clinker minerals. The presented work developed a multiphysics model, designed and built a laboratory‐scale rotary cement clinker kiln, and produced cement clinker at laboratory‐scale. The model was developed to study the interaction between the various thermal, fluid dynamic and chemical interactions involved in the sintering process used to form Portland cement clinker in an effort to reduce energy use. The analytical model was validated through experimental testing using a unique laboratory‐scale rotary cement kiln developed during the investigation. Also demonstrated was the feasibility of producing clinker at laboratory scale. This modeling and lab scale tests were designed to better understand the clinker sintering process so that operational and quality decisions can be made to optimize energy consumption without compromising cement clinker quality. The computational fluid dynamics modeling was developed in COMSOL Multiphysics 6.0. The characteristics of the combustion fluid flow, concentration of species, temperature and heat transfer were studied for a turbulent flow of methane (CH 4 ) gas and oxygen (O 2 ). Theory suggests that heat transfer impacts the cement production process but the multiphysics model more accurately describes the convection, conduction, and radiant heat transfer in the kilning process and thus allows for a better understanding of the energy exchange driving the chemical reactions that produce Portland cement. Clinker minerals were formed because of appropriate burning conditions implemented during experimental model validation.
This article reports a comparative experimental study of the hygroscopic and mechanical behaviors of electrospun polybenzimidazole (PBI) nanofiber membranes and solution-cast PBI films. As-electrospun nonwoven PBI nanofiber mats (with the nanofiber diameter of ~250 nm) were heat-pressed under controlled temperature, pressure and duration for the study; lab-made solution-cast PBI films and commercially available PBI films (the PBI Performance Product Inc., Charlotte, NC) were used as the control samples. Thermogravimetric and microtensile tests were utilized to characterize the hygroscopic (moisture absorption) and mechanical properties of the PBI nanofiber membranes at varying heat-pressing conditions, which were further compared to those of solution-cast PBI films. Experimental results indicated that the PBI nanofiber membranes carried slightly higher thermal stability and less hygroscopic properties than those of solution-cast PBI films. In addition, heat-pressing conditions significantly influenced the mechanical properties of the resulting PBI nanofiber membranes. The stiffness and tensile strength increase with increasing either the heat-pressing pressure or duration, and relevant mechanisms were explored. Here, the present study provides a rational understanding of the hygroscopic and mechanical behaviors of electrospun PBI nanofiber membranes and solution-cast PBI films that are beneficial to their reliable cutting-edge applications in high-temperature filtration, polymer electrolyte membranes (PEMs), etc.
Here, we present a new data set from the Marcell Experimental Forest (MEF) that compiles water isotope measurements from multiple research catchments, some of which have been studied since the 1960s. The MEF is located in northern Minnesota, USA, and is home to heavily studied and monitored forests, streams, bogs, and fens. Peat-forming systems (bogs and fens) are an important component of the MEF landscape and have a profound impact on the water cycle in these catchments. Within the last decade, analysis of stable isotopes of water (expressed as δD and δ 18 O) has been implemented to characterize the different components of the water budget, and to allow researchers to look at catchment and peatland-specific hydrologic effects in the watershed. This δD and δ 18 O data set of natural waters from MEF catchments is primarily composed of measurements from three peatlands (S1, S2, S6) during an 11-year period. More recently collection and analysis were expanded to also include samples from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) project in the S1 bog, peatlands S3, S4, S5, as well as nearby lakes. We establish a local meteoric water line by analyzing the isotopic composition of precipitation, which fills a void in regional meteoric water lines for Minnesota. Furthermore, we establish baseline isotopic composition for bog outlet streams, bog porewater, aquifer groundwater, overland flow, subsurface stormflow, and snowpack, as well as runoff from the SPRUCE experimental chambers. These data are publicly available and will be expanded upon in the future.
Chemical kinetic models of gasoline, jet, and diesel fuels and their mixtures with bioderived fuels are needed to assess fuel property effects on efficiency, emissions, and other performance metrics in internal combustion and gas turbine engines. As these real fuels have too many fuel components to be included in a chemical kinetic model, surrogate fuels containing fewer components are used to represent them. These surrogate fuels mimic the chemical classes or molecular structures contained in the real fuel. One of the important chemical classes in gasoline, jet, and diesel fuels comprises cyclohexanes. Cyclohexanes comprise about 30% or more by weight in diesel fuel. Also, Mueller et al proposed n-butylcyclohexane (nBCH) as a component in a nine-component surrogate palette to represent the ignition properties, distillation curve, density, and molecular structures of a diesel certification fuel. Herein, experimental measurements of the ignition delay times (IDTs) of nBCH in a shock tube and in a rapid compression machine are reported over a wide range of temperature, pressure, and equivalence ratio important for enabling the validation of a chemical kinetic model for nBCH for combustion in diesel engines. The range of conditions are temperatures of 630–1420 K, pressures of 10, 30, and 50 bar, and equivalence ratios of 0.3, 0.5, 1.0, and 2.0 in ‘air’. A detailed chemical model is developed for nBCH to simulate its ignition at both low and high-temperature conditions and at relevant elevated pressures. The experimentally measured IDTs are used to improve and validate the chemical kinetic model.
Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.
Abstract The potential advantage of machine learning in quantum computers is a topic of intense discussion in the literature. Theoretical, numerical, and experimental explorations will most likely be required to understand its power. There have been different algorithms proposed to exploit the probabilistic nature of variational quantum circuits for generative modeling. In this paper, a hybrid architecture for quantum generative adversarial networks (QGANs) is employed and their robustness in the presence of noise is studied. A simple way of adding different types of noise to the quantum generator circuit is devised, and the noisy hybrid QGANs (HQGANs) are simulated numerically to learn continuous probability distributions, and to show that the performance of HQGANs remains unaffected. The effect of different parameters on the training time is also investigated to reduce the computational scaling of the algorithm and simplify its deployment on a quantum computer. The training on Rigetti's Aspen‐4‐2Q‐A quantum processing unit is then performed, and the results from the training are presented. The authors' results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate‐scale quantum devices.