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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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Crystallographic, spectroscopic, and computational studies on diiron( ii ) hydride complexes reveal rapid hydride motions that cause major changes in the electronic structures of the iron sites.
Polymers of intrinsic microporosity (PIM) fibers featuring amidoxime and amine functionalities were formulated into high-performance CO 2 adsorbents. Fiber sorbents were composed of a single porous polymer component functionalized with molecular amines to increase the CO 2 capture uptake and kinetics. Here we report the highest CO 2 uptake capacity (3.3 wt% (0.8 mmol g –1 ) CO 2 at 400 ppm) among all PIM-based and single polymeric sorbents to date. Moreover, the sorbents exhibited rapid CO 2 adsorption rates (capturing 90% of its total capacity within 7 min.) and cost efficient and reproducible regeneration (at 70 °C) which are essential for reducing the cost of direct air capture.
Tuning the chemical and physical crosslinks of poly(dimethyl siloxane)-based elastomers enables significantly enhanced puncture-resistant property with rapid self-healing capability.
A rapid solidification strategy was developed for simultaneously avoiding the Marangoni effect and suppressing molecular aggregation. The resultant 15.64 cm 2 large-area OSC module exhibited a record power conversion efficiency of 16.03%.
The multireference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to offer a balanced treatment of both static and dynamic correlation while retaining polynomial scaling, although application to large systems with significant strong correlation remained impractical. In this paper, we document recent algorithmic advances that enable rapid convergence and a more black-box approach to the multireference problem. These include a logarithmically scaling metric-tree-based excitation acceptance algorithm to search for determinants connected to the reference space at the desired excitation level and a symmetry-screening procedure for the reference space. We show that, for moderately sized reference spaces, the new search algorithm brings about an approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry screening procedure reduces the number of active reference space determinants with essentially no loss of accuracy. We also introduce a stochastic implementation of an approximate wall projector, which is the infinite imaginary time limit of the exponential projector, using a truncated expansion of the wall function in Chebyshev polynomials. Notably, this wall-Chebyshev projector can be used to accelerate any projector-based QMC algorithm. We show that it requires significantly fewer applications of the Hamiltonian to achieve the same statistical convergence. We benchmark these acceleration methods on the beryllium and carbon dimers, using initiator FCIQMC and MR-CCMC with basis sets up to cc-pVQZ quality.
Matrix-assisted ionization (MAI) of inorganic analytes is a nascent research domain that holds promise for rapid, potentially facility-deployable analytical applications. Here, we present results of MAI uranium isotopic analysis ( 235 U/ 238 U) obtained on the timescale of minutes utilizing simple sample preparation and an ambient ionization time-of-flight mass spectrometer (ToF MS). Experimental MAI-ToF MS characterization of uranium Certified Reference Materials (CRMs) was used to establish method calibration and validate quantitative 235 U/ 238 U determination spanning depleted, natural, and low-enriched uranium isotopic compositions. Secondary standard analyses with total uranium mass loadings of 5–500 ng per analysis yield accurate calibrated 235 U/ 238 U results and relative uncertainties of 4.7–17.2% (approx. ±95% confidence level), with weighted-mean uncertainties approaching 1.5%. This method permits accurate determination of uranium isotopic composition in a sample with uranium content as low as 200 pg for equal atom 235 U: 238 U. Instrument detection limits constrain the minimum uranium mass required to identify the presence of highly enriched uranium (HEU ≥20% 235 U) as only 500 pg using the method presented here. MAI-ToF MS quantitation of relatively extreme isotope ratios ( 235 U/ 238 U ≤ 0.01) is limited by detection of minor 235 U (LoD 100 pg 235 U/analysis ≈ 10 ng total U/analysis), and subsequent method optimization is anticipated to further reduce these limits. These findings underscore the potential of MAI-ToF MS for isotopic characterization of uranium and other inorganic species for both basic and applied science.
Laser-induced breakdown spectroscopy (LIBS) mapping enables rapid elemental and spatial analysis of solid samples. In this work, surrogate TRi-structural ISOtropic (TRISO) particles with various layers consisting of Zr, W, C, and Si were used to demonstrate the use of a new thickness measurement tool developed to analyze elemental images generated from LIBS maps. Zr particles with varying outer-layer thicknesses ranging from 16 to 32 μm were measured with scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and LIBS with both a complementary metal-oxide-semiconductor (CMOS) detector and an intensified charged coupled device (ICCD) detector. LIBS maps of particles were completed using CMOS and ICCD spectrometers with effective spatial resolutions of 4 and 2 μm, respectively. The novel thickness measurement tool identified layer regions within a LIBS map and then identified the locations of the boundaries between these layers. The tool then generated up to 1000 random profiles stemming radially from the center region, which were used to measure the layer thickness/radius. This image analysis tool demonstrated LIBS's ability to provide values comparable with SEM-EDS (3.7% relative difference) along with a 95% reduction in measurement time. Furthermore, the precision of these measurements was on par with the SEM-EDS measurements at <15% relative standard deviation. Following the analysis of the Zr particles, W particles with increased complexity (e.g., five versus three layers) were analyzed using both the CMOS and ICCD spectrometers simultaneously. The spatial dimensions were extracted with an average relative difference of 2.7% and an average relative standard deviation of 9%.
Accurate and precise isotopic analysis of actinides collected on environmental sample swipes from within nuclear facilities is an important safeguarding measure for detecting undeclared materials and activities. Traditional isotope ratio (IR) analysis of actinides using a bulk digestion approach can be laborious and time-consuming. Recently, direct analysis of environmental swipe samples using a microextraction approach has been explored as an alternative to conventional bulk digestion methods. The present study further evaluates this approach for accurate and precise isotopic analysis of uranium (U) in cotton swipe samples using the liquid sampling-atmospheric pressure glow discharge (LS-APGD)/Orbitrap-FTMS Booster detection system. The instrumental parameters were optimized, and quantitative capabilities were demonstrated through excellent linearity (R2 = 0.99) across a deposited mass range of 1 to 500 ng, with a limit of detection of 90 pg (in a 2 × 4 mm region) for total U; well below typical U concentrations (ng–mg range) in environmental sample swipes. The 235U/238U ratio showed excellent accuracy (−0.3% relative difference from the certificate value), with ∼20× improvement over previous microextraction-Orbitrap methods. Precision (∼1% relative standard deviation) was also greatly enhanced by ∼5×. This study also demonstrates that figures of merit achieved with neat U solution were not highly degraded when various external interfering elements/matrices were introduced. Finally, the developed method was successfully applied to analyze practical swipe samples representative of laboratory and outdoor environments. The presented figures of merit and applicability to practical samples further validate the capability of the platform for rapid analysis (<7 minutes) of environmental swipe samples.
Recent observations of the Milky Way and galaxies at high redshifts suggest that galaxy discs were already in place soon after the Big Bang. While the gas infall history of the Milky Way in the inner disc has long been assumed to be characterised by a short accretion timescale, this has not been directly constrained using observations. Using data for the inner regions of the Milky Way recently produced by APOGEE and Gaia and of unprecedented quantity and quality, we aim to derive strong constraints on the infall history of the inner (< 6 kpc) Galaxy (with a focus on stars between 4 and 6 kpc, which we show is an appropriate proxy for the entire inner disc). We implemented gas infall into a chemical evolution model of the Galaxy disc, and used a Schmidt–Kennicutt law to connect the infall to the star formation. We explore a number of models, and two different formulations of the infall law. In one formulation, the infall is non-parametric, and in the other the infall has an explicitly exponential form. We fit the model parameters to the time–[Si/Fe] distribution of solar vicinity stars, and the metallicity and [Si/Fe] distribution function of stars with a galactocentric radius of between 4 and 6 kpc from APOGEE. Our results point to a fast, early gas accretion, and an upper limit on the accretion timescale of around 2 Gyr in the inner disc of the Milky Way. This suggests that at least half the baryons were in place within 2–3 Gyr of the Big Bang, and that half the stars of the inner disc formed within the first 5 Gyr, during the thick disc formation phase. This implies that the stellar mass of the inner disc is dominated by the thick disc, supporting our previous work, and that the gas accretion onto the inner disc was rapid and early.
The space density of X-ray-luminous, blindly selected active galactic nuclei (AGN) traces the population of rapidly accreting super-massive black holes through cosmic time. It is encoded in the X-ray luminosity function, whose bright end remains poorly constrained in the first billion years after the Big Bang as X-ray surveys have thus far lacked the required cosmological volume. With the eROSITA Final Equatorial-Depth Survey (eFEDS), the largest contiguous and homogeneous X-ray survey to date, X-ray AGN population studies can now be extended to new regions of the luminosity–redshift space (L 2 - 10 keV > 10 45 erg s -1 and z > 6). The current study aims at identifying luminous quasars at z > 5.7 among X-ray-selected sources in the eFEDS field in order to place a lower limit on black hole accretion well into the epoch of re-ionisation. A secondary goal is the characterisation of the physical properties of these extreme coronal emitters at high redshifts. Cross-matching eFEDS catalogue sources to optical counterparts from the DESI Legacy Imaging Surveys, we confirm the low significance detection with eROSITA of a previously known, optically faint z = 6.56 quasar from the Subaru High-z Exploration of Low-luminosity Quasars (SHELLQs) survey. We obtained a pointed follow-up observation of the source with the Chandra X-ray telescope in order to confirm the low-significance eROSITA detection. Using new near-infrared spectroscopy, we derived the physical properties of the super-massive black hole. Finally, we used this detection to infer a lower limit on the black hole accretion density rate at z > 6. The Chandra observation confirms the eFEDS source as the most distant blind X-ray detection to date. The derived X-ray luminosity is high with respect to the rest-frame optical emission of the quasar. With a narrow MgII line, low derived black hole mass, and high Eddington ratio, as well as its steep photon index, the source shows properties that are similar to local narrow-line Seyfert 1 galaxies, which are thought to be powered by young super-massive black holes. In combination with a previous high-redshift quasar detection in the field, we show that quasars with L 2 - 10 keV > 10 45 erg s -1 dominate accretion onto super-massive black holes at z ~ 6.
In November 2020, a new, bright object, eRASSt J234402.9-352640, was discovered in the second all-sky survey of SRG/eROSITA. The object brightened by a factor of at least 150 in 0.2–2.0 keV flux compared to an upper limit found six months previous, reaching an observed peak of 1.76 -0.24 +0.03 × 10 -11 erg cm -2 s -1 . The X-ray ignition is associated with a galaxy at z = 0.10, making the peak luminosity log 10 (L 0.2-2 keV /[erg s -1 ]) = 44.7 ± 0.1. Around the time of the rise in X-ray flux, the nucleus of the galaxy brightened by approximately 3 mag. in optical photometry, after correcting for the host contribution. We present X-ray follow-up data from Swift, XMM-Newton, and NICER, which reveal a very soft spectrum as well as strong 0.2–2.0 keV flux variability on multiple timescales. Optical spectra taken in the weeks after the ignition event show a blue continuum with broad, asymmetric Balmer emission lines, and high-ionisation ([OIII]λλ4959,5007) and low-ionisation ([NII]λ6585, [SII]λλ6716,6731) narrow emission lines. Following the peak in the optical light curve, the X-ray, UV, and optical photometry all show a rapid decline. The X-ray light curve shows a decrease in luminosity of ~0.45 over 33 days and the UV shows a drop of ~0.35 over the same period. eRASSt J234402.9-352640 also shows a brightening in the mid-infrared, likely powered by a dust echo of the luminous ignition. We find no evidence in Fermi-LAT γ-ray data for jet-like emission. The event displays characteristics of a tidal disruption event (TDE) as well as of an active galactic nucleus (AGN), complicating the classification of this transient. Based on the softness of the X-ray spectrum, the presence of high-ionisation optical emission lines, and the likely infrared echo, we find that a TDE within a turned-off AGN best matches our observations.
Disruptive robotic construction technologies such as additive deposition of cementitious materials like concrete (or "3D concrete printing") require the synchronous operation of multiple pieces of equipment in the production setup. In such an environment, it is crucial to simulate the robotic motions (for toolpath clashes) and the cementitious material behavior (for toolpath failures) to ensure fail-proof constructability of the envisioned building geometry. However, toolpath clash detection requires 4D simulations of the production setup, which are computationally graphics intensive, whereas toolpath failure detection requires actual 3D printing of test parts from the geometry to identify areas prone to failure while 3D printing, which is physically tedious. Both these processes, being computationally and physically intensive, have largely curtailed designers from simulating and exploring large sets of design options with varying geometries and toolpath configurations. To overcome this and allow designers to explore large sets of design possibilities, this paper proposes two novel computational metamodels capable of performing robotic toolpath clash detection and failure detection with significantly reduced times than the earlier approaches. The developed metamodels were used to rapidly simulate large sets of building design options for robotic constructability in the NASA 3D-Printed Mars Habitat Challenge.
We present the design for a rapid proton energy modulator with radiofrequency accelerator cavities, which can deliver the proton radiation dose to varied depth in human tissues much faster than traditional mechanical beam energy degraders. The proton energy modulator is designed as a multi-cell 1-m long accelerator working at 2.856 GHz. Each individual accelerator cavity is powered by a 400 kW compact klystron to provide an accelerating/decelerating gradient of 30 MV/m. The high gradient is enabled by the individual power coupling regime, which provides a high shunt impedance. Beam dynamics simulations were performed, showing that the energy modulator can provide ±30 MeV of beam energy change for a 150 MeV, 7 mm long (full length) proton bunch, and the total energy spread of 3 MeV is satisfactory to clinical needs. A prototype experiment of a single cell has been built and tested, and the low-power microwave measurement results agree very well with simulations. The energy modulator is optimized for the 150 MeV cyclotron proton beam, while this approach can work with different beam energies.
In this work, we describe a machine learning approach to rapidly tune density functional tight binding models for the description of detonation chemistry in organic molecular materials. Resulting models enable simulations on the several 10s of ps scales characteristic to these processes, with “quantum-accuracy.” We use this approach to investigate early shock chemistry in 3,4-bis(3-nitrofurazan-4-yl)furoxan, a hydrogen-free energetic material known to form onion-like nanocarbon particulates following detonation. We find that the ensuing chemistry is significantly characterized by the formation of large C x N y O z species, which are likely precursors to the experimentally observed carbon condensates. Beyond utility as a means of investigating detonation chemistry, the present approach can be used to generate quantum-based reference data for the development of full machine-learned interatomic potentials capable of simulation on even greater time and length scales, i.e., for applications where characteristic time scales exceed the reach of methods including Kohn–Sham density functional theory, which are commonly used for reference data generation.
In a recent study [J. Phys. Chem. B 126, 4584–4598 (2022)], we have used infrared spectroscopy to investigate the solvation and dynamics of solitary water in ionic liquids and dipolar solvents. Complex shapes observed for water OH-stretching bands, common to all high-polarity solvents, were assigned to water in several solvation states. Here, in the present study, classical molecular dynamics simulations of a single water molecule in four ionic liquids and three dipolar solvents were used to test and refine this interpretation. Consistent with past assignments, simulations show solitary water usually donates two hydrogen bonds to distinct solvent molecules. Such symmetrically solvated water produces the primary pair of peaks identified in the OH spectra of water in nearly all solvents. We had further proposed that additional features flanking this main peak are due to asymmetric solvation states, states in which only one OH group makes a hydrogen bond to solvent. Such states were found in significant concentrations in all of the systems simulated. Simulations of the OH stretching spectra using a semiclassical description and the vibrational map developed by Auer and Skinner [J. Chem. Phys. 128, 224511–224512 (2008)] provided semi-quantitative agreement with experiment. Analysis of species-specific spectra confirmed assignment of the additional features in the experimental spectra to asymmetrically solvated water. The simulations also showed that rapid water motions cause a marked motional narrowing compared with the inhomogeneous limit. This narrowing is largely responsible for making the additional features due to minority solvation states manifest in the spectra.
Single-crystal model systems are valuable tools to investigate fundamental material properties. In this work, we use molecular beam epitaxy to deposit in situ arsenic (As) doped single-crystal CdTe films on large area Si substrates to better understand As doping for photovoltaic applications. We found that As incorporation is highly temperature dependent: a substrate temperature difference of 50 °C can lead to several orders of magnitude difference in As concentration. Cd overpressure during in situ doping may limit out-diffusion of As but decrease As incorporation, especially at lower growth temperatures. Carrier concentrations greater than 10 16 cm −3 can be achieved with or without Cd overpressure when annealed at temperatures above 500 °C. However, unlike the low (∼1% to 5%) dopant activation commonly observed in polycrystalline CdTe, our films achieve significantly higher activation ratios—exceeding 50%, and in some cases approaching 80%. These values are consistent with or exceed prior reports in single-crystal CdTe systems. In addition to as-deposited arsenic concentrations, we also consider arsenic distribution after different rapid thermal processing temperatures. We propose a detailed definition and description of how arsenic incorporation is considered and calculated. Due to carrier concentration saturation, As incorporation also needs to be controlled to average levels of 10 17 cm −3 to achieve high activation. These findings suggest that higher annealing temperature regimes may be beneficial to polycrystalline CdTe based PV devices.
Nanobodies bind a target antigen with a kinetic profile similar to a conventional antibody, but exist as a single heavy chain domain that can be readily multimerized to engage antigen via multiple interactions. Presently, most nanobodies are produced by immunizing camelids; however, platforms for animal-free production are growing in popularity. Here, we describe the development of a fully synthetic nanobody library based on an engineered human V H 3-23 variable gene and a multispecific antibody-like format designed for biparatopic target engagement. To validate our library, we selected nanobodies against the SARS-CoV-2 receptor–binding domain and employed an on-yeast epitope binning strategy to rapidly map the specificities of the selected nanobodies. We then generated antibody-like molecules by replacing the V H and V L domains of a conventional antibody with two different nanobodies, designed as a molecular clamp to engage the receptor-binding domain biparatopically. The resulting bispecific tetra-nanobody immunoglobulins neutralized diverse SARS-CoV-2 variants with potencies similar to antibodies isolated from convalescent donors. Subsequent biochemical analyses confirmed the accuracy of the on-yeast epitope binning and structures of both individual nanobodies, and a tetra-nanobody immunoglobulin revealed that the intended mode of interaction had been achieved. This overall workflow is applicable to nearly any protein target and provides a blueprint for a modular workflow for the development of multispecific molecules.
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.