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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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Most patients with metastatic melanoma show variable responses to radiation therapy and do not benefit from immune checkpoint inhibitors. Improved strategies for combination therapy that leverage potential benefits from radiation therapy and immune checkpoint inhibitors are critical.
This dataset was generated using an iterative active learning strategy with the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials (MLIPs) for aqueous nitric acid. Each active-learning cycle consisted of three stages: (1) training, (2) exploration, and (3) labeling. The initial training set comprised approximately 800 randomly selected configurations from a previous study by Lewis et al. (https://doi.org/10.1021/jp205510q), which investigated nitric acid solutions at 2, 3, 4, and 5 mol/L. For all configurations, single-point calculations of atomic forces and total energies were performed at the quantum density functional theory BLYP-D2 and PBE-D3 levels of theory using the CP2K Quickstep module. Valence electrons were treated explicitly, while core electrons on all atoms were represented by norm-conserving Goedecker–Teter–Hutter (GTH) pseudopotentials. Long-range dispersion interactions were accounted for using Grimme dispersion corrections. Wave functions were expanded in a mixed Gaussian-and-plane-wave scheme using TZV2P-MOLOPT basis sets for all elements and an 800 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent field convergence was accelerated using orbital transformation and Direct Inversion in the Iterative Subspace, with a convergence threshold of 10^{-6}. All single-point calculations were carried out in periodic orthorhombic cells whose dimensions match those of the molecular configurations sampled from earlier trajectories. The CELL_REF keyword in CP2K was used to define a fixed reference cell, ensuring consistency in the reference data used for MLIP training, particularly when cell fluctuations are present in NpT simulations. The resulting high-fidelity energies and forces constitute the ground-truth labels used to train the MLIPs contained in this dataset.
Quantum computation has the potential to revolutionize quantum chemistry through major speedups in computation times and an exponential reduction in computational resources. Here, we combine the symmetry-adapted Jordan–Wigner encoding based on the full Boolean symmetry group $\mathbb{Z}$$^{k}_{2}$ with our new implementation of the Xia–Bian–Kais (XBK) method for improving the efficiency of electronic structure theory calculations on quantum annealers, particularly by reducing the number of qubits needed to achieve the same accuracy. By providing a more extensive symmetry-adapted encoding (SAE) than previous work, we are able to simulate molecules larger than those previously reported that have been studied using methods developed for quantum annealers and without using an active space. We calculated the potential energy surfaces of H 2 , LiH, He 2 , H 2 O, O 2 , N 2 , Li 2 , F 2 , CO, BH 3 , NH 3 , and CH 4 , with the largest molecule in the STO-6G basis set requiring 16 qubits with our SAE, and compared them with full configuration interaction results. The application of SAE to the XBK method provides an exponential reduction in the size of the Hilbert space and scales well with the size of the problem. It does not introduce significant additional errors for even or large values of a key variational parameter that determines the number of ancilla qubits used in the XBK method’s Hamiltonian embedding, or for certain molecules such as He 2 and H 2 O. Here, we provide an explanation for this behavior and a recommendation on the usage of our method. In addition, we briefly discuss the potential of extracting electronic excited states from our method.
Refractory high-entropy alloys (HEAs) are promising candidates for next-generation nuclear applications, particularly fusion reactors, due to their excellent high-temperature mechanical properties and irradiation resistance. Here, the microstructure and mechanical behavior were investigated for an equimolar WTaTiVZr HEA, designed from a palette of low-activation elements. The as-cast alloy exhibited a dendritic microstructure composed of W-Ta rich dendrites and Zr-Ti-V rich inter-dendritic regions, both possessing a body-centered cubic (BCC) crystal structure. Room temperature bulk compression tests showed ultra-high strength of around 1.6 GPa and plastic strain ~6%, with fracture surfaces showing cleavage facets. The alloy also demonstrated excellent high-temperature strength of ~650 MPa at 500 °C. Scratch-based fracture toughness was ~38 MPa√m for the as-cast WTaTiVZr HEA compared to ~25 MPa√m for commercially used pure tungsten. This higher value of fracture toughness indicates superior damage tolerance relative to commercially used pure tungsten. These results highlight the alloy’s potential as a low-activation structural material for high-temperature plasma-facing components (PFCs) in fusion reactors.
Atmospheric ice nucleating particles (INPs) influence global climate by altering cloud formation, lifetime, and precipitation efficiency. The role of secondary organic aerosol (SOA) material as a source of INPs in the ambient atmosphere has not been well defined. Here, we demonstrate the potential for biogenic SOA to activate as depositional INPs in the upper troposphere by combining field measurements with laboratory experiments. Ambient INPs were measured in a remote mountaintop location at –46°C and an ice supersaturation of 30% with concentrations ranging from 0.1 to 70 L –1 . Concentrations of depositional INPs were positively correlated with the mass fractions and loadings of isoprene-derived secondary organic aerosols. Compositional analysis of ice residuals showed that ambient particles with isoprene-derived SOA material can act as depositional ice nuclei. Laboratory experiments further demonstrated the ability of isoprene-derived SOA to nucleate ice under a range of atmospheric conditions. We further show that ambient concentrations of isoprene-derived SOA can be competitive with other INP sources. This demonstrates that isoprene and potentially other biogenically-derived SOA materials could influence cirrus formation and properties.
Summary While traditionally considered important mainly in hypoxic roots during flooding, upregulation of fermentation pathways in plants has recently been described as an evolutionarily conserved drought survival strategy, with acetate signaling mediating reprograming of transcription and cellular carbon and energy metabolism from roots to leaves. The amount of acetate produced directly correlates with survival through potential mechanisms including defense gene activation, biosynthesis of primary and secondary metabolites, and aerobic respiration. Here, we review root ethanolic fermentation responses to hypoxia during saturated soil conditions and summarize studies highlighting acetate fermentation under aerobic conditions coupled with respiration during growth and drought responses. Recent work is discussed demonstrating long‐distance transport of acetate via the transpiration stream as a respiratory substrate. While maintenance and growth respiration are often modeled separately in terrestrial models, here we propose the concept of ‘Defense Respiration’ fueled by acetate fermentation in which upregulation of acetate fermentation contributes acetate substrate for alternative energy production via aerobic respiration, biosynthesis of primary and secondary metabolites, and the acetylation of proteins involved in defense gene regulation. Finally, we highlight new frontiers in leaf‐atmosphere emission measurements as a potential way to study acetate fermentation responses of individual leaves, branches, ecosystems, and regions.
The coupling of electric fields to the mechanics of lipid membranes gives rise to intriguing electromechanical behavior, as, for example, evidenced by the deformation of lipid vesicles in external electric fields. Electromechanical effects are relevant for many biological processes, such as the propagation of action potentials in axons and the activation of mechanically gated ion channels. Currently, a theoretical framework describing the electromechanical behavior of arbitrarily curved and deforming lipid membranes does not exist. Purely mechanical models commonly treat lipid membranes as two-dimensional surfaces, ignoring their finite thickness. While holding analytical and numerical merit, this approach cannot describe the coupling of lipid membranes to electric fields and is thus unsuitable for electromechanical models. In a sequence of articles, we derive an effective surface theory of the electromechanics of lipid membranes, called the (2 + δ)-dimensional theory, which has the advantages of surface descriptions while accounting for finite thickness effects. The present article proposes a generic dimension reduction procedure relying on low-order spectral expansions. This procedure is applied to the electrostatics of lipid membranes to obtain the (2 + δ)-dimensional theory that captures potential differences across and electric fields within lipid membranes. Finally, this model is tested on different geometries relevant for lipid membranes, showing good agreement with the corresponding three-dimensional electrostatics theory.
Lithium metal batteries offer superior volumetric and gravimetric specific capacities compared to those based on traditional graphite anodes. Although advancements in solid-state electrolytes address safety concerns, challenges remain, particularly regarding interphase formation in lithium metal anodes. This work presents a computational framework based on high-throughput first-principles density functional theory and machine-learning interatomic potentials (MLIPs) including automated iterative, active learning to enable robust computational exploration of interphase formation between lithium metal anodes and an inorganic solid-state electrolyte. As a demonstration, we apply the framework to a Li/Li7P3S11 interface and find that it accurately identifies the experimentally observed, thermodynamically stable interphase products as well as their overall spatial arrangement within a heterogeneous, amorphous layered structure, with Li2S domains of nanocrystallinity. Our simulations show two stages, a fast and slow diffusion reaction regime, that corroborate the relative phase formation rate of Li x P, Li2S, and Li3P. Using the Onsager transport theory, we capture time-dependent ionic diffusion within the reacting interface, including cross-correlation effects. We found that cross-correlation effects between Li-P and P-S ionic motion significantly influence P-ion diffusion, making it highly sensitive to the local environment and potentially leading to "kinetic trapping" of Li-P phases. The passivation of the interface is shown as the ionic fluxes all approach zero, effectively halting interphase growth.
Abstract Organic solar cells (OSCs) process fascinating solution‐printing capability to achieve low‐cost and large‐scale manufacture. However, the rapid power conversion efficiency (PCE) decay with active layer thickness enlargement inhibits the implement of OSCs’ potential advantages. To overcome the bottlenecks of PCE decay in thick active layer OSCs, the electrical doping with componential selectivity in bulk heterojunction (BHJ) film is achieved by introducing a solid solvation additive. Benefiting from the higher exciton splitting efficiency together with the longer drift (L dr ) and diffusion (L diff ) lengths, an OSC with 100 nm BHJ film demonstrates a PCE increment from 16.44% to 18.24% with prolonged dark and illuminated storage stabilities. Applying the solid solvation assisted (SSA) doping method in the OSCs with 500 nm active layer, the PCE significantly increases by 31.9%, from the original value of 11.79% to 15.55%. It further improves to 15.84% in a ternary blend thick‐film device, which is the record value to the best of our knowledge. Besides, the SSA doping narrows the PCE gap between the 0.04 and 1 cm 2 devices. All improvements demonstrate the great potential of SSA doping for OSC commercial manufacture, since it optimizes the photovoltaic performance under all practical conditions of long‐term, thick‐film, and large‐area.
Using measurements of the [O iii], Hα, and [N ii] emission line fluxes originating in the cool (T ~10 4 K) gas that populates the halos of massive early-type galaxies with stellar masses greater than 10 10.4 M ⊙ , we explore the recent conjecture that active galactic nucleus (AGN) activity preferentially removes the circumgalactic medium (CGM) along the polar (minor-axis) direction. We find deficits in the mean emission line flux of [O III] and Hα (65% and 43%, respectively) along the polar versus planar directions, although due to the large uncertainties in these difficult measurements the results are of marginal statistical significance (1.5σ). More robustly (97%–99.9% confidence depending on the statistical test), diagnostic line ratios show stronger AGN ionization signatures along the polar direction at small radii than at other angles or radii. Our results are consistent with the conjecture of an anisotropic CGM in massive early-type galaxies, suggested on independent grounds, that is tied to AGN activity and begin to show the potential of CGM mapping using emission lines.
Machine learning (ML) of interatomic potentials show great promise to accelerate scientific simulation, e.g., by emulating expensive computations at a high accuracy but much reduced computational cost. Training datasets are calculated from computationally expensive ab initio quantum mechanics methods, density functional theory (DFT). Trained on this data, an ML model can be very successful in predicting energy and forces for new atomic configurations. A critical factor is the quality and diversity of the training dataset. Thus, a highly automated approach to dataset construction based on active learning framework is designed suitable for material physics. The active learning scheme begins with fully randomized atomic configurations. Then, many Molecular Dynamics (MD) trajectories are simulated using current ML potentials, where each MD trajectory is initialized to a random disordered configuration. The temperature is varied in order to diversify the sampled configuration during these simulations. The variance of predictions for eight neural networks within an ensemble is analyzed to determine whether the model is operating as expected. This helps in determining whether collecting more data would be helpful to the model by checking the ensemble variance is greater than the threshold. In this case, the MD trajectory is terminated and the final atomic configuration is placed on a queue (SQL database) for DFT calculations and added to training dataset. Periodically, ML model is retrained to the updated training model. This Active Learning loop is iterated until the cost of MD simulations becomes prohibitively expensive. The MD simulations will hopefully be sufficiently robust to support nucleation after many active learning iterations. In this sense, active learning scheme must automatically discover the important low energy and nonequilibrium physics.
Abstract Biocontrol services are widely recognized to provide key incentives for bat conservation. However, we have virtually no information on whether and how disruptions in bat‐mediated biocontrol services are driven by mismatches between the temporal activity patterns of insectivorous bats and insect pests. We investigated the temporal relationship between the nightly activity patterns of the common pipistrelle bat ( Pipistrellus kuhlii ) and the olive fruit moth ( Prays oleae ). Temporal mismatches between species pairs were estimated as the time difference (expressed as a percentage of the night) at which P. kuhlii and P. oleae reached 50% of their abundance. The study was carried out during spring, summer, and fall between 2017 and 2019 in 60 olive farms representing increasing levels of structural simplification (as a surrogate of agricultural intensification). Olive farms were classified as exhibiting high (i.e. HIGH olive farms; n = 27), intermediate (MID; n = 18), and low (LOW; n = 15) structural complexity. Temporal mismatches between the activity levels of P. kuhlii and P. oleae varied between seasons and types of olive farms, being comparatively lower in summer than in spring and fall. Furthermore, summer was the only season in which temporal mismatches between species pairs differed between types of olive farms, with higher temporal mismatches found in LOW than in HIGH and MID olive farms. Overall, our work demonstrates the existence of temporal mismatches between the nightly activity patterns of P. kuhlii and P. oleae . Furthermore, it demonstrates that the structural simplification of olive farms increases temporal mismatches between species pairs, particularly in summer when bat‐mediated biocontrol services are most needed. Synthesis and applications . Future research should consider mismatches between the temporal activity patterns of insectivorous bats and insect pests. Otherwise, the actual impact of agricultural intensification on bat‐mediated biocontrol services as well as the economic impact of their loss on the agriculture industry might be underestimated. To enhance biocontrol services, we propose increasing the availability of suitable roosting and foraging sites as well as conserving areas of remnant native woodland and scattered hollow‐bearing trees.
Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO 2 surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine-learning interatomic potentials (MLIPs) integrated with an active-learning workflow to efficiently explore reduced rutile TiO 2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high-resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO 2 , with potential implications for catalyst design.
Utilizing the remaining capacity in retired lithium-ion (Li-ion) batteries from electric vehicles (EVs) for second-life applications has shown economic and environmental benefits. However, achieving homogeneity among the capacities of cells before their second life is critical to exploit the benefits. This article proposes a new active reconditioning approach with the potential to make short-term reconditioning of batteries before second life feasible. A control objective map determines each cell's state-of-charge (SOC) operating window based on its capacity relative to other cells. The SOC reference translates into distinct differential currents through the cells, wherein the higher-capacity cells undergo more frequent and deep charge and discharge cycles than their lower-capacity counterparts. The proposed solution achieves capacity homogeneity within the battery pack with low reconditioning time and minimal fade in the overall pack capacity. The feasibility of the reconditioning approach under varying load and environmental conditions is assessed through simulations, encompassing factors such as the number of cycles per day, depth-of-discharge, battery pack temperature, and cell resting time at different SOCs. Furthermore, the simulation model employs a battery pack with sixteen series-connected 75 Ah Kokam lithium nickel manganese cobalt oxide (NMC) cells with a 3.6% initial capacity imbalance. A reconditioning time of 1.3 months is achieved with a final capacity imbalance of 0.1% and an overall capacity fade of 0.005%, thereby confirming the viability of the reconditioning process. Moreover, experimental validation using eight retired battery cells from a Nissan Leaf demonstrates a substantial decrease in the capacity imbalance of cells from 9.4% to 2.15% within 78 days, effectively affirming the efficacy of the proposed reconditioning scheme.
We have investigated the rapid-reaction kinetics of the NAD+-dependent NADPH:ferredoxin oxidoreductase II (NfnII) from Pyrococcus furiosus, permitting a comparison with recent work done with the paralog NfnI from the same organism. The half-potentials of the electron-bifurcating L-FAD are highly crossed in both NfnI, meaning the potential for the quinone/semiquinone couple is significantly lower than that for the semiquinone/hydroquinone couple so that the semiquinone oxidation state is thermodynamically unstable. The same appears to be the case with NfnII on the basis of its similar behavior in transient absorption spectroscopy experiments and the absence of any evidence for FAD*- accumulation in the course of reductive titrations (which would be manifested as a transient increase in absorbance at ~380 nm)1. Reductive titrations with both the one-electron donor sodium dithionite and the obligate two-electron donor NADPH demonstrate that, in contrast to NfnI, little FADH* accumulates in the high-potential pathway of NfnII in the course of reduction, as reflected in the absence of a transient increase in absorbance in the 500-600 nm region. This indicates that the half-potentials of the S-FAD are crossed in NfnII by a minimum of 120 mV. Rapid-reaction experiments mixing oxidized NfnII with NADPH also show no evidence of S-FADH* accumulation. Furthermore, the enzyme is only partially reduced at the end of the reaction with NADPH, indicating that there is little electron transfer into the high-potential pathway of NfnII. When the reaction is carried out in the presence of the one-electron carrier ferredoxin, only approximately one equivalent of ferredoxin becomes reduced, in contrast to the maximum of three equivalents seen with NfnI. This is consistent with only a single electron bifurcation event taking place under these conditions with NfnII, with only a single high-potential electron passing into the [2Fe-2S] cluster of the high-potential pathway of NfnII but not progressing further to the S-FAD. This accounts for the observation that NfnII has greatly reduced bifurcating activity compared to NfnI. The lack of electron transfer into the S-FAD of NfnII due to its crossed half-potentials prevents successive bifurcation activity and indicates that electron transfer within the high-potential branch gates bifurcation activity in NfnI.
The 2016–2017 central Italy seismic sequence occurred on an 80 km long normal-fault system. The sequence initiated with the Mw 6.0 Amatrice event on 24 August 2016, followed by the Mw 5.9 Visso event on 26 October and the Mw 6.5 Norcia event on 30 October. We analyze continuous data from a dense network of 139 seismic stations to build a high-precision catalog of ~900,000 earthquakes spanning a 1 yr period, based on arrival times derived using a deep-neural-network-based picker. Our catalog contains an order of magnitude more events than the catalog routinely produced by the local earthquake monitoring agency. Aftershock activity reveals the geometry of complex fault structures activated during the earthquake sequence and provides additional insights into the potential factors controlling the development of the largest events. Activated fault structures in the northern and southern regions appear complementary to faults activated during the 1997 Colfiorito and 2009 L’Aquila sequences, suggesting that earthquake triggering primarily occurs on critically stressed faults. Delineated major fault zones are relatively thick compared to estimated earthquake location uncertainties, and a large number of kilometer-long faults and diffuse seismicity were activated during the sequence. These properties might be related to fault age, roughness, and the complexity of inherited structures. The rich details resolvable in this catalog will facilitate continued investigation of this energetic and well-recorded earthquake sequence.
Extreme light confinement observed in periodic photonic structures, such as the vortex singularities in momentum (k) space, has been associated with their topological nature. Consequently, by exploiting and tuning their topological properties, optical metasurfaces have been demonstrated as an attractive platform for active photonics. However, given the fact that most active media under external excitations can only provide limited refractive index change, the potential advancements offered by the topological character of active metasurfaces have remained mostly unexplored. Zinc oxide (ZnO), which has recently exhibited optically-induced extraordinarily large permittivity modulations at visible and near-infrared frequencies, is an excellent active material for dynamic metasurfaces exhibiting strong tuning. This work demonstrates that a hybrid metasurface consisting of an array of ZnO nanodisks on a silver backplane displays broadly tunable topological properties. In particular, by performing k-space scattering simulations using measured pump-fluence-dependent material properties of ZnO, we study in detail the light reflection from the hybrid metasurface. Our results validate that the large k-space topology tuning of the metasurface can result in enormously strong polarization manipulation of near-infrared light in the vicinity of the topological features. The observed polarization switching effect is highly sensitive to the polarization and wavelength of an incident wave, owing to the symmetry and dispersion characteristics of the proposed system. Our study indicates that leveraging a combination of the extraordinary material properties and the k-space topology, hybrid metasurfaces based on ZnO may open new avenues for creating all-optical switchable metadevices.
It has been of great interest to understand relationships between the potential of a metal catalyst and its thermochemical catalytic activity, especially in an aqueous phase environment. In the literature, there are correlations of open circuit potential with reaction rates or surface concentrations of reaction intermediates. In this study, we measured the open circuit potential (OCP) of a Au gauze during selective oxidation of glycerol to glyceric acid and hydrogen peroxide in a basic solution, as a function of hydroxide and alcoholate concentrations. It is found that applying a potential to the Au catalyst has no influence on the reaction rate. Although a rough correlation appears to exist between OCP and reaction rate, the data are better fit to an equation which assumes that the potential of the metal (i.e. the OCP) is in equilibrium with the electrochemical potential of the solution, which is defined by the thermodynamic activities of the oxidizing and reducing species. The equation: OCP = constant + Σ ox [(RT/FZ ox )ln[Ox]] - Σ red [(RT/FZ red )ln[Red]] implicitly assumes that the Au metal functions as a probing electrode. It is further found that this equation also applies to the Au-catalyzed H 2 O 2 oxidation/decomposition reaction in a basic medium, and possibly to formic acid dehydrogenation. Here, we postulate that the apparent correlation between OCP and reaction rate is due to the fact that the reaction rate is proportional to the concentrations of the reaction products and/or reactants that define the electrochemical potential of the solution.