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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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Benchmarking metrics for materials discovery via sequential learning are presented, to assess the efficacy of existing algorithms and to be scientific in our assessment of accelerated science.
Using covariance analysis methods, we study the fragmentation dynamics of multiply ionized 1- and 2-iodopropane. Signatures of isomer-specific nuclear motion occurring during sequential fragmentation pathways are identified.
Semitransparent organic solar cells based on sequentially processed heterojunction (SHJ) structure show a power conversion efficiency (PCE) of 12.5% and an average visible transmittance (AVT) of 25.4%, outperforming the BHJ counterpart (PCE = 11.8%, AVT = 22.9%).
Maximizing iridium utilization while maintaining high oxygen evolution reaction (OER) performance remains a persistent challenge in acidic water electrolysis. Immobilizing Ir on conductive, acid-stable supports is promising, yet simultaneously achieving sub-nanometer size, high area coverage, and strong electronic coupling is difficult. Here, we report a sequential surface synthesis on titanium nitride (TiN) that yields uniformly distributed sub-nanometer Ir arrays (∼0.7 nm). Our method uses ethylenediaminetetraacetic acid (EDTA) as a temporal scaffold: it chemisorbs to TiN to install dense chelating sites, captures Ir 3+ ions, and confines Ir cluster growth. A subsequent thermal treatment at 500 °C in a reducing atmosphere removes the ligand shell, while preserving ultrasmall particle size and establishing direct Ir–TiN electronic coupling. The optimized catalyst exhibits mixed Ir 0 /Ir x+ coordination with low charge-transfer resistance (R ct = 19.2 Ω), delivering a mass activity of 342 A g Ir −1 at 1.54 V in acidic electrolyte. In situ X-ray absorption spectroscopy reveals irreversible surface oxidation as the primary stability-limiting factor. This stepwise strategy provides a general framework for supported catalysts that maximize precious metal utilization via sub-nanometer dispersion.
The formation of carbon via the triple alpha process is a key step in stellar production reactions of light ions and heavily impacts the life-cycle of red giant stars. Here, we propose a novel theoretical approach for sequential mechanism of the helium burning process, based on the Imaginary Time Method (ITM) and semiclassical models. We compare our results to those of the NACRE collaboration, which has set the standard for the past decades. Our calculations differ by several orders of magnitude in the low temperature region, which may seriously affect the early evolution of helium burning stars.
The diffusion behavior of Mg in Mg/N co-implanted GaN is investigated in response to a set of annealing conditions and methodologies, namely, 1000 °C/30 min thermal anneal, by high-temperature pulsed gyrotron microwave annealing at 1420 or 1500 °C, or by thermal and microwave annealing, sequentially. After 1000 °C annealing, the diffusion of Mg in GaN is found to be negligible, as measured by secondary ion mass spectrometry. Annealing by gyrotron microwave annealing alone induces the diffusion of Mg at a rate on the order of 10 −12 cm 2 /s. However, the use of a thermal anneal before microwave gyrotron annealing reduces this rate by an order of magnitude to 10 −13 cm 2 /s. We find that a model that considers Mg diffusion from an inhomogeneous medium that contains a defect-rich implanted region near-surface to a relatively pristine region below the implant range better explains the observed diffusion behavior than a conventional model that assumes a homogeneous medium. By analyzing the diffusion behavior using the Boltzmann–Matano method, we present a discussion of reduction in [V Ga ] by thermal annealing at 1000 °C, leading to a suppressed diffusion coefficient during subsequent high-temperature annealing relative to diffusion after 1420/1500 °C annealing alone. This effect holds potential for improvement in the precision of selectively doped regions for future applications based on the (Al)GaN material system. An improved field profile control in real devices can increase the breakdown and current-handling capabilities in power electronic applications.
The accuracy of iron opacity calculated in stellar interiors has been questioned since the discovery of the “solar problem” and the discrepancies between the measured and modeled iron opacity reported in 2015. Experimental opacity benchmarks require accurate temperature and density measurements, which were inferred by analyzing tracer magnesium spectra in those experiments. Could the observed discrepancy be explained by insufficient accuracy in the inferred temperature, density, and their uncertainties? Previous analyses may have yielded biased results due to three limitations: (1) simultaneous multi-line fitting, (2) approximations in line-shape models, and (3) exclusion of certain spectral lines due to insufficient background characterization. Notably, the first issue is a common concern for many inversion methods, including Bayesian inferences. We present a refined analysis method that overcomes these limitations, applied to three categories of iron opacity experiments (Anchor 1, 2, and 3). In particular, the sequential fitting method yields unbiased results with more realistic uncertainties by accounting for line inconsistencies in the parameter uncertainties. The average electron temperature and density values are 162 ± 6 eV and (7.0 ± 1.9) × 10 21 cm −3 for six Anchor 1 experiments, 189 ± 7 eV and (3.4 ± 0.3) × 10 22 cm −3 for 21 Anchor 2 experiments, and 201 ± 6 eV and (4.8 ± 1.1) × 10 22 cm −3 for nine Anchor 3 experiments. These results show ∼4% temperature and ∼20% density reproducibility over a decade, which also aligns with the inferred parameter uncertainties. In conclusion, the resulting temperature and density uncertainties lead to a quasi-continuum iron opacity variation of ±4%–7% for wavelengths below 9.5 Å, which is insufficient to explain the significant model-data discrepancies reported in 2015.
Determining the requirements for efficient oxygen (O 2 ) activation is key to understanding how enzymes maintain efficacy and mitigate unproductive, often detrimental reactivity. For the α-ketoglutarate (αKG)–dependent nonheme iron enzymes, both a concerted mechanism (both cofactor and substrate binding prior to reaction with O 2 ) and a sequential mechanism (cofactor binding and reaction with O 2 precede substrate binding) have been proposed. Deacetoxycephalosporin C synthase (DAOCS) is an αKG-dependent nonheme iron enzyme for which both of these mechanisms have been invoked to generate an intermediate that catalyzes oxidative ring expansion of penicillin substrates in cephalosporin biosynthesis. Interestingly, spectroscopy shows that, in contrast to other αKG-dependent enzymes (which are six coordinate when only αKG is bound to the Fe II ), αKG binding to Fe II -DAOCS results in ~45% five-coordinate sites that selectively react with O 2 relative to the remaining six-coordinate sites. However, this reaction produces an Fe III species that does not catalyze productive ring expansion. Alternatively, simultaneous αKG and substrate binding to Fe II -DAOCS produces five-coordinate sites that rapidly react with O 2 to form an Fe IV =O intermediate that then reacts with substrate to produce cephalosporin product. These results demonstrate that the concerted mechanism is operative in DAOCS and by extension, other nonheme iron enzymes.
Eukaryotic cells form condensates to sense and adapt to their environment [S. F. Banani, H. O. Lee, A. A. Hyman, M. K. Rosen,Nat. Rev. Mol. Cell Biol.18, 285–298 (2017), H. Yoo, C. Triandafillou, D. A. Drummond,J. Biol. Chem.294, 7151–7159 (2019)]. Poly(A)-binding protein (Pab1), a canonical stress granule marker, condenses upon heat shock or starvation, promoting adaptation [J. A. Ribacket al.,Cell168, 1028–1040.e19 (2017)]. The molecular basis of condensation has remained elusive due to a dearth of techniques to probe structure directly in condensates. We apply hydrogen–deuterium exchange/mass spectrometry to investigate the mechanism of Pab1’s condensation. Pab1’s four RNA recognition motifs (RRMs) undergo different levels of partial unfolding upon condensation, and the changes are similar for thermal and pH stresses. Although structural heterogeneity is observed, the ability of MS to describe populations allows us to identify which regions contribute to the condensate’s interaction network. Our data yield a picture of Pab1’s stress-triggered condensation, which we term sequential activation (Fig. 1A), wherein each RRM becomes activated at a temperature where it partially unfolds and associates with other likewise activated RRMs to form the condensate. Subsequent association is dictated more by the underlying free energy surface than specific interactions, an effect we refer to as thermodynamic specificity. Our study represents an advance for elucidating the interactions that drive condensation. Furthermore, our findings demonstrate how condensation can use thermodynamic specificity to perform an acute response to multiple stresses, a potentially general mechanism for stress-responsive proteins.
Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. Furthermore, the advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.
This study shows that in recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite differencing may be infeasible. In such cases, often a surrogate model is employed, on which finite differencing is performed. When the surrogate model is a Gaussian process (GP), we show that the ASM estimator is available in closed form, rendering the finite-difference approximation unnecessary. We use our closed-form solution to develop acquisition functions focused on sequential learning tailored to sensitivity analysis on top of ASMs. We also show that the traditional ASM estimator may be viewed as a method of moments estimator for a certain class of GPs. We demonstrate how uncertainty on GP hyperparameters may be propagated to uncertainty on the sensitivity analysis, allowing model-based confidence intervals on the active subspace. Our methodological developments are illustrated on several examples.
Abstract Aims Cardiac anxiety, which is cardiac-related fear and avoidance behaviours, is common following myocardial infarction (MI) and has been associated with increased risk for cardiovascular events. However, there are currently no treatments specifically designed to target cardiac anxiety. The aim of the two pilot studies was to evaluate an exposure-based cognitive behavioural therapy protocol (MI-CBT) targeting cardiac anxiety following MI, assessing feasibility, acceptability, and the intervention's potential for reducing cardiac anxiety and improving health-related quality of life (QoL). Methods and results A series of two sequential, uncontrolled pilot studies were conducted. In Pilot Study 1 (n = 15), MI-CBT was delivered via face-to-face videoconference, while Pilot Study 2 (n = 23) was delivered online. Patients with a history of MI (≥6 months before assessment, type 1 ST- or non-ST-segment elevation MI, and elevated cardiac anxiety as per clinical interview) were included. The interventions lasted 8 weeks and were therapist-led, with key components including exposure to cardiac-related symptoms and reduction of avoidance behaviours. Participants completed self-rated assessments, including the Cardiac Anxiety Questionnaire (CAQ) and the 12-Item Short Form Health Survey (SF-12), at baseline, post-treatment, and 6-month follow-up. Treatment adherence and satisfaction were high. Cognitive behavioural therapy led to a large reduction in cardiac anxiety, as measured by the CAQ (P < 0.001), and significant improvements in health-related QoL, as measured by the SF-12 (P < 0.001), in both pilot studies. Conclusion These studies suggest that exposure-based CBT is a feasible, acceptable, and promising approach to reduce cardiac anxiety and improve QoL following MI. A randomized controlled trial should be conducted to evaluate the efficacy of the intervention.
Nested sampling (NS) is the preferred stochastic sampling algorithm for gravitational-wave inference for compact binary coalescences. It can handle the complex nature of the gravitational-wave likelihood surface and provides an estimate of the Bayesian model evidence. However, there is another class of algorithms that meets the same requirements, but has not been used for gravitational-wave analyses: sequential Monte Carlo (SMC), an extension of importance sampling that maps samples from an initial density to a target density via a series of intermediate densities. In this work, we validate a type of SMC algorithm, called persistent sampling (PS), for gravitational-wave inference. We consider a range of different scenarios including binary black holes and binary neutron stars and real and simulated data and show that PS produces results that are consistent with NS whilst being, on average, 2 times more efficient and 2.74 times faster. This demonstrates that PS is a viable alternative to NS that should be considered for future gravitational-wave analyses.
Intense femtosecond infrared (IR) laser pulses have been used in recent years to study the breakup dynamics of molecules. However, observables such as kinetic energy release and branching ratios of molecular fragments are often difficult to predict by theory, making information of the molecular dynamics difficult to retrieve. In this work, we develop a simple model for sequential double ionization of molecules based on a density-matrix approach. The model describes tunneling ionization of the neutral and the ion as well as laser couplings between the ionic states simultaneously. Population of different doubly charged states can be obtained at a low computational cost. Furthermore, we applied our model to N 2 and obtained a good agreement on the kinetic energy release spectrum with a previous experiment. This theoretical development could open up the opportunities to use intense short IR laser pulses with coincidence measurement to probe molecular dynamics.
Understanding of strong-field sequential double ionization (SDI) of molecules by a highly intense infrared laser pulse could be the key to observing electron motion in molecules in the attosecond to femtosecond timescale. Based on a novel density matrix approach for SDI (DM-SDI), a recent theoretical study has shown that SDI can be used as a probe to monitor the changes in vibronic coherence in homonuclear diatomic molecules. In this article, we extend the DM-SDI model to general molecules that could possess permanent dipole moments and arbitrary symmetry. We apply the model to SDI of a water molecule and identify the formation pathway of individual dication states. We further deduce that kinetic energy release spectra from two- and three-body fragments could carry the signature of vibronic coherence between the lowest two states of the water cation. Furthermore, our results suggest that observables from the SDI probe can be interpreted intuitively with only the knowledge of electronic structures of populated ionic states, making the SDI probe to be a highly desirable probing scheme for vibronic coherence in generic molecules.
We compute the mass, radius and tidal deformability of stars containing phase transitions from hadronic to quark phase(s). These quantities are computed for three types of hadronic envelopes: purely nuclear, hyperonic, and Δ-resonance--hyperon admixed matter. We consider either a single first-order phase transition to a quark phase with a maximally stiff equation of state (EOS) or two sequential first-order phase transitions mimicking a transition from hadronic to a quark matter phase followed by a second phase transition to another quark phase. We explore the parameter space which produces low-mass twin and triplet configurations where equal-mass stars have substantially different radii and tidal deformabilities. We demonstrate that while for purely hadronic stiff EOS the obtained maximum mass is inconsistent with the upper limit on this quantity placed by GW170817, the inclusion of the hyperonic and Δ-resonance degrees of freedom, as well as the deconfinement phase transition at sufficiently low density, produces a configuration of stars consistent with this limit. The obtained hybrid star configurations are in the mass range relevant for the interpretation of the GW170817 event. We compare our results for the tidal deformability with the limits inferred from GW170817 showing that the onset of non-nucleonic phases, such as Δ-resonance--hyperon admixed phase and/or the quark phase(s), are favored by this data if the nuclear EOS is stiff. Also, we show that low-mass twins and especially triplets proliferate the number of combinations of possible types of stars that can undergo a merger event, the maximal number being six in the case of triplets. The prospects for uncovering the first-order phase transition(s) to and in quark matter via measurements of tidal deformabilities in merger events are discussed.
Random packings and their properties are a popular and active field of research. Numerical algorithms that can efficiently generate them are useful tools in their study. This paper focuses on random packings produced according to the random sequential adsorption (RSA) protocol. Developing the idea presented by G. Zhang, where saturated random packings built of regular polygons were studied, we create an algorithm that generates strictly saturated packings built of any polygons. Then, the algorithm was used to determine the packing fractions for arbitrary triangles. In this work, the highest mean packing density, 0.552 814 ± 0.000 063, was observed for triangles of side lengths 0.63 : 1 : 1. Additionally, microstructural properties of such packings, kinetics of their growth, as well as distributions of saturated packing fractions and the number of RSA iterations needed to reach saturation were analyzed.
We report on measurements of sequential ϒ suppression in Au+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV with the STAR detector at the Relativistic Heavy Ion Collider (RHIC) through both the dielectron and dimuon decay channels. In the 0%–60% centrality class, the nuclear modification factors (𝑅 𝐴𝐴 ), which quantify the level of yield suppression in heavy-ion collisions compared to 𝑝 + 𝑝 collisions, for ϒ(1𝑆) and ϒ(2𝑆) are 0.40 ± 0.03(stat) ± 0.03(sys) ± 0.09(norm) and 0.26 ± 0.08(stat) ± 0.02(sys) ± 0.06(norm), respectively, while the upper limit of the ϒ(3𝑆) 𝑅 𝐴𝐴 is 0.17 at a 95% confidence level. This provides experimental evidence that the ϒ(3𝑆) is significantly more suppressed than the ϒ(1𝑆) at RHIC. The level of suppression for ϒ(1𝑆) is comparable to that observed at the much higher collision energy at the Large Hadron Collider. Furthermore, these results point to the creation of a medium at RHIC whose temperature is sufficiently high to strongly suppress excited ϒ states.