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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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By employing 3,5-bis(trifluoromethyl) pyrazole (TFMP) as an electrolyte additive in both aqueous and non-aqueous mediums, a versatile interphase strategy is achieved. This facilitates stable Zn anodes with improved efficiency and longer cycling life.
Supernova remnant observations show a high degree of asymmetry, mixing, and inhomogeneity. These asymmetries are seeded during the early seconds of the explosion and are further enhanced and modified as the shock and ejecta move through the stellar progenitor and into the circumstellar medium. We present simulations of a 9.6 M⊙ zero-metallicity progenitor initialized after shock revival and evolved for several years when the ejecta is in the circumstellar medium. A suite of 1D and 2D simulations examines the effects of neutron-star wind and radioactive decay heating. In 1D, decay heating forms a low-density bubble that suppresses the reverse shock. While in 2D, the heating is localized to metal-rich pockets, inflating them and compressing the surrounding material into dense shells. In 3D, the neutron-star wind and decay heating modify the plume morphology, producing more large-scale structures. The extended plume morphology leads to an asymmetrical shock breakout. After breakout, the leading plumes cannot keep up with the shock front, resulting in deceleration and fragmentation by the reverse shock while retaining the large-scale asymmetry. The projected ejecta morphology and velocities are strongly viewing angle dependent. The relatively uniform metal-rich distribution does not resemble the strongly inhomogeneous ejecta structure of Cas A. The 160-isotope decay network shows that 24.4% of the radioactive heating comes from decay chains other than the canonical 56Ni chain. The low explosion energy, low 56Ni yield, and Ni/Fe ratio greater than unity suggest an observational signature similar to an electron capture supernova.
Heavy quarks, and the hadrons containing them, are excellent probes of the QCD medium formed in high-energy heavy-ion collisions, as they provide essential information on the transport properties of the medium and how quarks color-neutralize into hadrons. Large theoretical and phenomenological efforts have been dedicated thus far to assess the diffusion of charm and bottom quarks in the quark–gluon plasma and their subsequent hadronization into heavy-flavor (HF) hadrons. However, the fireball formed in heavy-ion collisions also features an extended hadronic phase, and therefore any quantitative analysis of experimental observables needs to account for the rescattering of charm and bottom hadrons. This is further reinforced by the presence of a QCD cross-over transition and the notion that the interaction strength is maximal in the vicinity of the pseudo-critical temperature. We review existing approaches for evaluating the interactions of open HF hadrons in a hadronic heat bath and the pertinent results for scattering amplitudes, spectral functions and transport coefficients. While most of the work to date has focused on D -mesons, we also discuss excited states as well as HF baryons and the bottom sector. Both the HF hadro-chemistry and bottom observables will play a key role in future experimental measurements. We also conduct a survey of transport calculations in heavy-ion collisions that have included effects of hadronic HF diffusion and assess its impact on various observables.
Understanding the dynamics of cluster formation and network percolation provides the mechanistic link between microscopic solvation structure and transport in concentrated electrolytes, including localized high-concentration electrolytes (LHCEs). Although recent studies have shown LHCEs to form micelle-like aggregates at specific compositions, a quantitative understanding of how solvation structures and transport properties depend on salt–solvent–diluent ratios remains limited. Here, we integrate molecular dynamics with Onsager transport analyses to chart the evolution of solvation microstructure and associated ionic transport in LiFSI/DMC/TTE, achieving good agreement with experimental conductivities across composition. We show that micelle formation arises from a dynamic instability of the cation–anion network, and that network percolation is the primary determinant of conductivity. Two compositional thresholds emerge: A critical network concentration (CNC) and a critical micelle concentration (CMC) that delineate transitions from extended percolating networks to micelle-like clusters and then to fragments. These structural transitions rationalize the nonintuitive conductivity decrease at intermediate dilution despite monotonically decreasing viscosity and provide composition-level design rules for LHCEs.
Fine-grained spectral properties of quantum Hamiltonians, including both eigenvalues and their multiplicities, provide useful information for characterizing many-body quantum systems as well as for understanding phenomena such as topological order. Extracting such information with small additive error is #BQP-complete in the worst case. In this work, we introduce QFAMES (quantum filtering and analysis of multiplicities in eigenvalue spectra), a quantum algorithm that efficiently identifies clusters of closely spaced dominant eigenvalues and determines their multiplicities under physically motivated assumptions, which allows us to bypass worst-case complexity barriers. QFAMES also enables the estimation of observable expectation values within targeted energy clusters, providing a powerful tool for studying quantum phase transitions and other physical properties. We validate the effectiveness of QFAMES through numerical demonstrations, including its applications to characterizing quantum phases in the transverse-field Ising model and estimating the ground-state degeneracy of a topologically ordered phase in the two-dimensional toric code model. We also generalize QFAMES to the setting of mixed initial states. Our approach offers rigorous theoretical guarantees and significant advantages over existing subspace-based quantum spectral analysis methods, particularly in terms of the sample complexity and the ability to resolve degeneracies.
Here, we present the results of our search through the early data release (EDR) of the Dark Energy Spectroscopic Instrument (DESI) for extreme coronal line emitters (ECLEs) – a rare classification of galaxies displaying strong, high-ionization iron coronal emission lines within their spectra. With the requirement of a strong X-ray continuum to generate the coronal emission, ECLEs have been linked to both active galactic nuclei (AGNs) and tidal disruption events (TDEs). We focus our search on identifying TDE-linked ECLEs. We identify three such objects within the EDR sample, highlighting DESI’s effectiveness for discovering new nuclear transients, and determine a galaxy-normalized TDE-linked ECLE rate of RG=5 −3+5×10−6 galaxy−1 yr−1 at a median redshift of z=0.2 – broadly consistent with previous work. Additionally, we identify more than 200 AGNs displaying coronal emission lines, which serve as the primary astrophysical contaminants in searches for TDE-related events. We also include an outline of the custom python code developed for this search.
Hydrogen wide area monitoring refers to the temporal and quantitative 3-dimenasional spatial profiling of hydrogen plumes following either intentional or unintentional hydrogen releases. A hydrogen wide area monitor (HyWAM) would have applications as a research tool, for example to provide empirical data on the behavior of hydrogen dispersions following a release, which in turn can be used to validate modelling studies. Support of modeling studies and commercial applications are interrelated, since modeling can serve to guide HyWAM deployment for enhanced safety within medium to large scale hydrogen operations, such as those envisioned by H2@Scale.
Deploying advanced building control strategies in small- and medium-sized commercial buildings (SMBs) is critical but constrained by two major challenges: (1) the lack of sensor and control infrastructure (SCI) and (2) the significant engineering effort required for implementation and configuration. While these barriers limit large-scale adoption in SMBs, they remain underexplored in the literature, which has largely emphasized feasibility over scalability.
In this study, we developed a photocurable hydrogel resin incorporating a polyelectrolyte complex (PEC) for 3D printing. Acrylamide-based monomers were formulated with varying PEC contents (0–15 wt %) in an aqueous KBr medium to fabricate patterned porous hydrogel structures. The morphology of printed hydrogels was characterized by field-emission scanning electron microscopy and energy-dispersive X-ray spectroscopy. Notably, the PEC5 and PEC10 formulations exhibited optimal thermal stability and compressive properties, attributed to the homogeneous distribution of PEC domains within the hydrogel matrix. Furthermore, dye adsorption experiments demonstrated excellent removal efficiency, highlighting the potential of PEC-containing hydrogels for environmental remediation applications, particularly in the treatment of dye-contaminated wastewater.
We present simulation-based cosmological wcold dark matter (wCDM) inference using dark energy survey year 3 weak-lensing maps, via neural data compression of weak-lensing map summary statistics: power spectra, peak counts, and direct map-level compression/inference with convolutional neural networks (CNN). Using simulation-based inference, also known as likelihood-free or implicit inference, we use forward-modelled mock data to estimate posterior probability distributions of unknown parameters. This approach allows all statistical assumptions and uncertainties to be propagated through the forward-modelled mock data; these include sky masks, non-Gaussian shape noise, shape measurement bias, source galaxy clustering, photometric redshift uncertainty, intrinsic galaxy alignments, non-Gaussian density fields, neutrinos, and non-linear summary statistics. We include a series of tests to validate our inference results. This paper also describes the Gower Street simulation suite: 791 full-sky pkdgrav3 dark matter simulations, with cosmological model parameters sampled with a mixed active-learning strategy, from which we construct over 3000 mock dark energy survey lensing data sets. For wCDM inference, for which we allow –1 < w < –$\frac{1}{3}$, our most constraining result uses power spectra combined with map-level (CNN) inference. Using gravitational lensing data only, this map-level combination gives Ω m = 0.283$^{+0.020}_{–0.027}$, S 8 = 0.804$^{+0.025}_{–0.017}$, and w < –0.80 (with a 68 per cent credible interval); compared to the power spectrum inference, this is more than a factor of two improvement in dark energy parameter (Ω DE , w) precision.
Conventional tomographic reconstruction typically depends on centralized servers for both data storage and computation, leading to concerns about memory limitations and data privacy. Distributed reconstruction algorithms mitigate these issues by partitioning data across multiple nodes, reducing server load and enhancing privacy. However, these algorithms often encounter challenges related to memory constraints and communication overhead between nodes. In this paper, we introduce a decentralized Alternating Directions Method of Multipliers (ADMM) with configurable quantization. By distributing local objectives across nodes, our approach is highly scalable and can efficiently reconstruct images while adapting to available resources. To overcome communication bottlenecks, we propose two quantization techniques based on K-means clustering and JPEG compression. Numerical experiments with benchmark images illustrate the tradeoffs between communication efficiency, memory use, and reconstruction accuracy.
A typical Bayesian inference on the values of some parameters of interest q from some data D involves running a Markov Chain (MC) to sample from the posterior $p$($q$,$n$|$D$) $\propto$ $\mathcal{L}$($D$|$q$,$n$)$p$(q)$p$($n$), where n are some nuisance parameters with a separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior p(n) is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of p(n) at arbitrary values of n, i.e., one needs to provide a density estimator over the full n space from the provided samples. But the high dimensionality of n hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the n space into a much lower-dimensional space u, which projects away directions in n space that cannot appreciably alter $\mathcal{L}$. The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on p(n) than other proposed solutions to this issue. We demonstrate this “mode projection” technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the Dark Energy Survey, where n is a binned representation of the redshift distribution n(z) of the galaxies.
The bulky diphosphine DTBM-SEGPHOS is widely employed in CuH-catalyzed transformations as it provides remarkably active catalyst systems. The transient (DTBM-SEGPHOS)CuH monomer (LCuH) is the often-invoked active species. However, its instability has prevented spectroscopic characterization and mechanistic elucidation, hindering mechanistic understanding. We report low-temperature NMR spectroscopic characterization of LCuH, enabling quantitative kinetic analysis of the stoichiometric hydrocupration and catalytic hydroboration of cyclopentene, as well as the structural identification of two CuH clusters. LCuH inserts cyclopentene at −43°C, reaffirming its high reactivity toward olefins. LCuH deactivates to form L 2 Cu 3 H 3 and L 2 Cu 4 H 4 clusters, in which LCuH dimerization initiates aggregation. Kinetic analysis of reactions of unactivated alkenes indicates that competing on-cycle alkene hydrocupration and LCuH dimerization impact performance, as catalyst deactivation and turnover occur on comparable timescales. Structure–activity analysis using atomistic simulations shows that the steric profile of DTBM-SEGPHOS increases the CuH dimerization barrier by ∼7.7 kcal mol−1 compared to that of SEGPHOS, rationalizing the unique ability of DTBM-SEGPHOS to stabilize a reactive monomer for hydrocupration of broader alkene substrates. These findings illustrate the fundamental design principle that steric control of aggregation governs CuH catalyst performance, explaining both the exceptional activity of (DTBM-SEGPHOS)CuH and the limitations imposed by competing deactivation.
IceCube has recently detected a diffuse population of high-energy neutrinos arising from the Milky Way. We use this high-significance detection to place new limits on dark matter (DM) annihilation to neutrinos with two complementary approaches. The first method uses the background-subtracted Galactic longitude distribution of shower events to place a conservative bound on the DM annihilation cross section that does not rely on any assumed Galactic cosmic ray emission model; the resulting limits on the velocity-averaged annihilation cross section improve upon existing bounds by factors of a few. The second method uses the template-dependent neutrino energy spectra from the Inner Galaxy, inferred under different Galactic cosmic ray emission models. This complementary approach shows that the inferred Galactic neutrino intensities are already sensitive to DM contributions near the thermal-relic benchmark for a range of TeV-scale DM masses, though this comparison is more model-dependent. Our results demonstrate that measurements of diffuse Galactic neutrino emission can be used as a powerful probe of DM annihilation into neutrinos. Future observations with IceCube-Gen2 and KM3NeT will substantially extend this sensitivity, potentially allowing a decisive test of the thermal freeze-out mechanism with Galactic neutrino observations.
Heliostat designs have undergone a widespread and eclectic development process, with many unique designs demonstrated. However, recent developments do not show the overall cohort converging toward a globally accepted universal design. Here, this study characterizes heliostats by breaking down and evaluating design traits based on emergent patterns from a comprehensive compilation of known heliostat designs spanning several decades. Four main categories for evaluation emerged: heliostat base, heliostat primary axis, heliostat drive, and facet support. Each of these four categories is further defined by four subtypes so that all heliostats fall into a single subtype within each category. The classified heliostats are ranked, yielding several view slices into the heliostat compilation or a breakdown of heliostats by type. An analysis of the breakdown shows several trends: a scale-up of established designs, new approaches at small to medium scales, and a movement toward greater adoption of linear drives. These trends reflect the most meaningful contributing factors to a proposed trajectory for a new era of heliostat designs striving to meet widely considered cost targets of $\$$50/m 2 or $\$$75/m 2 .
Controlling the dynamic mobility of catalyst surface active sites and their interactions with the surrounding environment is critical in generating active surfaces that directly influence the catalytic activity and selectivity. Here, we report a strategy for tailoring the dispersion and electronic environment of single atom Rh catalysts by decorating the alumina support with highly dispersed (HD) cerium and molybdenum oxides. The resulting catalysts exhibit markedly different behavior in the Reverse Water Gas Shift (RWGS) reaction. In particular, Rh/MoOx(HD)/Al2O3 maintains atomically dispersed Rh even at elevated temperatures (up to 400 °C), achieving CO selectivity of up to 100% and resists sintering via the formation of a newly developed structure featuring Rh single atoms embedded in MoOx clusters. In situ spectroscopy and microscopy analyses confirm the stabilization of Rh and the dynamic evolution of Rh–Mo coordination under reaction conditions. Our findings highlight the power of support modification in steering active site structure and activity, offering a pathway toward enhanced and tunable single atom catalysts for CO2 valorization.
We describe a first-principles statistical mechanics method to calculate the free energies of crystalline alloys that depend on temperature, composition, and strain. The approach relies on an extension of the alloy cluster expansion to include an explicit dependence on homogeneous strain in addition to site occupation variables that track the degree of chemical ordering. The method is applied to the Si-Ge binary alloy and is used to calculate free energies that describe phase stability under arbitrary epitaxial constraints. We find that while the incoherent phase diagram (in which coexisting phases are not affected by coherency constraints) hosts a miscibility gap, coherent phase equilibrium predicts ordering and negative enthalpies of mixing. Instead of chemical instability, the chemomechanical free energy exhibits instabilities along directions that couple the composition of the alloy with a volumetric strain order parameter. Furthermore, this has fundamental implications for phase field models of spinodal decomposition as it indicates the importance of gradient energy coefficients that couple gradients in composition with gradients in strain.
Intrinsic antisite defects pose a major challenge to understanding and predicting the exotic properties of the layered topological magnetic insulator MnBi2Te4 (MBT). In this work, we study the origin of the abundance of intrinsic defects in MBT, including many-body defect–defect interactions and many-body electronic correlations. Until now, ab initio methods have struggled to explain thermodynamic stability and properties influenced by defect behavior in MBT. We model native Mn–Bi antisite defects in MBT at finite temperatures using a cluster expansion that includes defect–defect interactions. To overcome the limitations of conventional density functional theory (DFT), we introduce a hybrid approach that incorporates high-accuracy quantum Monte Carlo (QMC) calculations, introducing missing correlations. This strategy allows for accurate estimation of defect energetics and finite-temperature properties. We compute the configurational free energy, defect concentration, and configurational heat capacity, revealing a second-order order–disorder phase transition near the experimental synthesis temperature. Our study provides the first theoretical insight into the thermodynamics of intrinsic defects in MBT. The negative free energy relative to pristine MBT at synthesis temperatures indicates that Mn–Bi antisite formation is thermodynamically spontaneous. We also present a broadly applicable general framework for correcting low-level theoretical theories using highly accurate many-body corrections from QMC.