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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Detection of z ~ 2.3 Cosmic Voids from 3D Ly α Forest Tomography in the COSMOS Field

We present the most distant detection of cosmic voids ( z ~ 2.3) and the first detection of three-dimensional voids in the Lyα forest. We used a 3D tomographic map of the absorption with an effective comoving spatial resolution of 2.5 h -1 Mpc and a volume of 3.15 × 10 5 h 3 Mpc 3 , which was reconstructed from moderate-resolution Keck I/LRIS spectra of 240 background Lyman-break galaxies and quasars in a 0.16 deg 2 footprint in the COSMOS field. Voids were detected using a spherical overdensity finder calibrated from hydrodynamical simulations of the intergalactic medium (IGM). This allows us to identify voids in the IGM corresponding to voids in the underlying matter density field, yielding a consistent volume fraction of voids in both data (19.5%) and simulations (18.2%). We fit excursion set models to the void radius function and compare the radially averaged stacked profiles of large voids ( r > 5 h -1 Mpc) to stacked voids in mock observations and the simulated density field. Comparing with 432 coeval galaxies with spectroscopic redshifts in the same volume as the tomographic map, we find that the tomography-identified voids are underdense in galaxies by 5.95σ compared to random cells.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basis of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Excited-State Densities from Time-Dependent Density Functional Response Theory

While the variational principle for excited-state energies leads to a route to obtaining excited-state densities from time-dependent density functional theory, relatively little attention has been paid to the quality of the resulting densities in real space obtained with different exchange-correlation functional approximations or how nonadiabatic approximations developed for energies of states of double-excitation character perform for their densities. Here we derive an expression directly in real space for the excited-state density, which includes the case of nonadiabatic kernels and consequently is able, for the first time, to yield densities of states of double-excitation character. Under some well-defined simplifications, we compare the performance of the local-density approximation and exact-exchange approximation, which are in a sense at the opposite extremes of the fundamental functional approximations, on local and charge-transfer excitations in one-dimensional model systems and show that the dressed Time-Dependent Density Functional Theory (TDDFT) approach gives good densities of double excitations.

approximation↗
DOE OSTI2025

Design and synthesis of biobased superhydrophobic biochar catalyst derived from Citrus sinensis for biodiesel production using inedible oil feedstocks

In an one-pot in situ trans/esterification of low-grade feedstocks, the simultaneous presence of triglycerides (TAGs) and free fatty acids (FFAs) presents a dual catalytic challenge. While TAGs undergo transesterification to form biodiesel and glycerol, FFAs present in the feedstocks are esterified simultaneously, producing water as a by-product. This water can deactivate acid catalysts by interacting with their active sites, reducing catalytic efficiency and reusability. To overcome this, we developed a hydrophobic sulfonic acid-functionalized biochar catalyst derived from Citrus sinensis (orange peel) via sulfonation with H 2 SO 4 and subsequent silylation using hexamethyldisilazane (HMDS). The optimized catalyst exhibited a high surface area (355.09 m 2 g −1 ), sulfur content (5.33 wt%), and strong hydrophobicity (water contact angle: 154°). Compared to non-hydrophobic analogs, it showed enhanced activity and reusability (up to 10 cycles). Using response surface methodology with central composite design, a biodiesel yield of 99.1 ± 0.4% was achieved under optimal conditions. Life cycle assessment was performed to evaluate the environmental impacts of biodiesel production utilising the synthesized catalyst, considering 1000 kg of biodiesel produced as 1 functional unit. In conclusion, the recorded results showed the cumulative abiotic depletion of fossil resources over the entire biodiesel production process as 87 243.423 MJ and global warming potential as 4103.494 kg CO 2 equivalent.

Biochar↗
DOE OSTI2024

Defect-engineered monolayer MoS 2 with enhanced memristive and synaptic functionality for neuromorphic computing

Two-dimensional transition metal dichalcogenides (TMDs)-based memristors are promising candidates for realizing artificial synapses in next-generation computing. However, practical implementation faces several challenges, such as high non-linearity and asymmetry in synaptic weight updates, limited dynamic range, and cycle-to-cycle variability. Here, utilizing optimal-power argon plasma treatment, we significantly enhance the performance matrix of memristors fabricated from monolayer MoS 2 . Our approach not only improves linearity and symmetry in synaptic weight updates but also increases the number of available synaptic weight updates and enhances Spike-Time Dependent Plasticity. Notably, it broadens the switching ratio by two orders, minimizes cycle-to-cycle variability, reduces non-linear factors, and achieves an energy consumption of ~30 fJ per synaptic event. Implementation of these enhancements is demonstrated through Artificial Neural Network simulations, yielding a learning accuracy of ~97% on the MNIST hand-written digits dataset. Our findings underscore the significance of defect engineering as a powerful tool in advancing the synaptic functionality of memristors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multiparameter Constraints on Empirical Infrasound Period-Yield Relations for Bolides and Implications for Planetary Defense

How effective are methods for estimating bolide energies from infrasound signal period-yield relationships? A single global period–energy relation can obscure significant variability introduced by parameters such as the atmospheric Doppler wind profile and the bolide’s energy deposition profile as a function of altitude. Bolide speed, entry angle, burst altitude, and multiepisode fragmentation may all play a role in defining the detected period of the shockwave. By leveraging bolide light-curve data from the Center for Near Earth Object Studies, we re-examined the period–energy relation as a function of these parameters. Through a bootstrap approach, we show that various event subsets can deviate from widely cited period–energy models and we identify which specific conditions most strongly reshape the period–energy scaling. The results define both the fidelity and reliability of period–energy relations when no additional data beyond the infrasound record is available and improve the outcome when supporting data from bolide trajectories and light curves are included. Ultimately, these findings expand the scope of earlier models, providing a nuanced and robust framework for infrasound-only yield estimation under a range of bolide scenarios.

Planetary science↗
DOE OSTI2017

Constraint on Matter Power Spectrum on 10{sup 6}–10{sup 9}M{sub ⊙} Scales from τ{sub e}

An analysis of the physics-rich endgame of reionization at z = 5.7 is performed, jointly utilizing the observations of the Lyα forest, the mean free path (mfp) of ionizing photons, the luminosity function of galaxies, and new physical insight. We find that an upper limit on τ {sub e} provides a constraint on the minimum mfp (of ionizing photons) that is primarily due to dwarf galaxies, which in turn yields a new, yet strongest constraint on the matter power spectrum on 10{sup 6}–10{sup 9} M{sub ⊙} scales. With the latest Planck measurements of τ {sub e} = 0.055 ± 0.009, we can place an upper limit of (8.9 × 10{sup 6}, 3.8 × 10{sup 7}, 4.2 × 10{sup 8}) M{sub ⊙} on the lower cutoff mass of the halo-mass function, or equivalently, a lower limit on warm dark matter particle mass m {sub x} ≥ (15.1, 9.8, 4.6) keV or on sterile neutrino mass m {sub s} ≥ (161, 90, 33) keV at the (1, 1.4, 2.2)σ confidence level, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Neutron spectroscopy by thermalization light yield measurement in a composite heterogeneous scintillator

An exothermic neutron capture reaction can be used to uniquely identify neutrons in particle detectors. With the use of a capture-gated coincidence technique, the sequence of scatter events that lead to neutron thermalization prior to the neutron capture can also be used to measure neutron energy. We report on the measurement of thermalization light yield via a time-of-flight technique in a polyvinyl toluene-based scintillator EJ-290 within a heterogeneous composite detector that also includes 6Li-doped glass scintillator. The thermalization light output exhibits a strong correlation with neutron energy because of the preference for near-complete energy deposition prior to the 6Li(n,t)4He neutron capture reaction. The nonproportionality of the light yield from nuclear recoils contributes to the observed broadening of the distribution of thermalization light output. The nonproportional dependence of the scintillation light output in the EJ-290 scintillator as a function of proton recoil energy has been characterized in the range of 0.3–14.1 MeV via the Birks parametrization through a combination of time-of-flight measurement and previously conducted measurements with Monoenergetic neutron sources.

6Li-doped scintillator↗
DOE OSTI2017

Accurate Electron Affinities and Orbital Energies of Anions from a Nonempirically Tuned Range-Separated Density Functional Theory Approach

The treatment of atomic anions with Kohn–Sham density functional theory (DFT) has long been controversial because the highest occupied molecular orbital (HOMO) energy, E HOMO , is often calculated to be positive with most approximate density functionals. We assess the accuracy of orbital energies and electron affinities for all three rows of elements in the periodic table (H–Ar) using a variety of theoretical approaches and customized basis sets. Among all of the theoretical methods studied here, we find that a nonempirically tuned range-separated approach (constructed to satisfy DFT-Koopmans’ theorem for the anionic electron system) provides the best accuracy for a variety of basis sets, even for small basis sets where most functionals typically fail. Previous approaches to solve this conundrum of positive E HOMO values have utilized non-self-consistent methods; however, electronic properties, such as electronic couplings/gradients (which require a self-consistent potential and energy), become ill-defined with these approaches. In contrast, the nonempirically tuned range-separated procedure used here yields well-defined electronic couplings/gradients and correct E HOMO values because both the potential and resulting electronic energy are computed self-consistently. Orbital energies and electron affinities are further analyzed in the context of the electronic energy as a function of electronic number (including fractional numbers of electrons) to provide a stringent assessment of self-interaction errors for these complex anion systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Projective Integral Updates for High-Dimensional Variational Inference

Variational inference is an approximation framework for Bayesian inference that seeks to improve quantified uncertainty in predictions by optimizing a simplified distribution over parameters to stand in for the full posterior. Capturing model variations that remain consistent with training data enables more robust predictions by reducing parameter sensitivity. This work introduces a fixed-point optimization for variational inference that is applicable when every feasible log density can be expressed as a linear combination of functions from a given basis. In such cases, the optimizer becomes a fixed-point of projective integral updates. When the basis spans univariate quadratics in each parameter, the feasible distributions are Gaussian mean-fields and the projective integral updates yield quasi-Newton variational Bayes (QNVB). Other bases and updates are also possible. Since these updates require high-dimensional integration, this work begins by proposing an efficient quasirandom sequence of quadratures for mean-field distributions. Each iterate of the sequence contains two evaluation points that combine to correctly integrate all univariate quadratic functions and, if the mean-field factors are symmetric, all univariate cubics. More importantly, averaging results over short subsequences achieves periodic exactness on a much larger space of multivariate polynomials of quadratic total degree. The corresponding variational updates require four loss evaluations with standard (not second-order) backpropagation to eliminate error terms from over half of all multivariate quadratic basis functions. Furthermore, this integration technique is motivated by first proposing stochastic blocked mean-field quadratures, which may be useful in other contexts. A PyTorch implementation of QNVB allows for better control over model uncertainty during training than competing methods. Experiments demonstrate superior generalizability for multiple learning problems and architectures.

Gaussian mean-field↗
DOE OSTI2026

Recovery of terephthalic acid from solar PV backsheets using waste solvent from distilled spirits production

Current research on solar photovoltaic (PV) recycling mainly focuses on recovering valuable metals and glass, often neglecting the polymeric components, particularly the backsheets, which are typically landfilled or thermally decomposed. This study explores an innovative approach to upcycle PV backsheets into value-added products, specifically terephthalic acid (TPA), using waste ethanol solvent from the distilled spirits industry. Experimental results show that increasing both exposure time and ethanol concentration significantly enhances backsheet delamination efficiency. Using waste ethanol, a maximum delamination efficiency of 80% was achieved at room temperature after 24 hours. In decomposition trials, both sodium hydroxide (NaOH) and potassium hydroxide (KOH) demonstrated comparable efficiencies (96.6–97.5%) over 8 and 24 hour reactions. With virgin ethanol, NaOH yielded 94–97.5% TPA recovery. Notably, using waste ethanol achieved a TPA recovery efficiency of 96.8%, underscoring the process's economic viability and sustainability. Analytical characterization of TPA recovered after 8 hours showed consistent spectral patterns across both alkalis and solvents, indicating a similar chemical environment and functional groups. The recovered TPA can be repolymerized into high-purity PET, suitable for manufacturing new PV backsheets. This work advances polymer-recycling by demonstrating that an industrial waste solvent (distilled-spirits ‘heads’) can replace virgin ethanol without loss in delamination performance or TPA yield. While PV backsheet PET is a modest share of global PET, using waste ethanol to upcycle this currently under-recycled stream demonstrates a transferable solvent-reuse pathway that can extend to higher-volume PET sources.

Nain, Preeti [Michigan State Univ., East Lansing, ↗
DOE OSTI2025

Linking structural and rheological memory in disordered soft materials

Linking the macroscopic flow properties and nanoscopic structure is a fundamental challenge to understanding, predicting, and designing disordered soft materials. Under small stresses, these materials are soft solids, while larger loads can lead to yielding and the acquisition of plastic strain, which adds complexity to the task. In this work, we connect the transient structure and rheological memory of a colloidal gel under cyclic shearing across a range of amplitudes via a generalized memory function using rheo-X-ray photon correlation spectroscopy (rheo-XPCS). Our rheo-XPCS data show that the nanometer scale aggregate-level structure recorrelates whenever the change in recoverable strain over some interval is zero. The macroscopic recoverable strain is therefore a measure of the nano-scale structural memory. We further show that yielding in disordered colloidal materials is strongly heterogeneous and that memories of prior deformation can exist even after the material has been subjected to flow.

Kamani, Krutarth M. [Univ. of Illinois at Urbana-C↗
DOE OSTI2020

Long-range and local crystal structures of the S r 1 - x C a x Ru O 3 perovskites

Here, the crystal structures of the Sr 1–x Ca x RuO 3 (0 ≤ x ≤ 1) perovskites are investigated using both long-range and local structural probes. High-resolution synchrotron powder x-ray-diffraction characterization at ambient temperature shows that the materials have orthorhombic long-range crystal structures to high precision, and we support previous work showing that Ca 2+ substitution for Sr 2+ primarily changes the tilting of rigid corner-sharing RuO 6 octahedra at their shared oxygen vertices. X-ray pair-distribution function analysis for SrRuO 3 , CaRuO 3 , and one intermediate composition show them to display monoclinic symmetry at the local level; when averaged over different domain orientations this yields orthorhombic symmetry in the long range, and no long-range or local phase transitions are observed between 80 and 300 K for materials with intermediate compositions. High-resolution transmission electron microscopy shows that the Sr/Ca distribution is random at the nanoscale. Lastly, we plot magnetic characteristics such as the ferromagnetic T c , Curie-Weiss theta, effective moment, and ambient temperature susceptibility vs the octahedral tilt and unit-cell volume.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

UNCERTAINTIES IN GALACTIC CHEMICAL EVOLUTION MODELS

We use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number of SNe Ia per M {sub ⊙} formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions, along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model does not include uncertainties relating to stellar yields, star formation and merger histories, and modeling assumptions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Identification and functional analysis of strigolactone pathway genes regulating tillering traits in sugarcane

Abstract Saccharum officinarum and Saccharum spontaneum are two fundamental species of modern sugarcane cultivars, exhibiting divergent tillering patterns crucial for sugarcane architecture and yield. Strigolactones (SLs), a class of plant hormones, are considered to play a central role in shaping plant form and regulating tillering. Our study highlights the distinct tillering patterns observed between S. officinarum and S. spontaneum and implicates significant differences in SL levels in root exudates between the two species. Treatment with rac-GR24 (an artificial SL analog) suppressed tillering in S. spontaneum. Based on transcriptome analysis, we focused on two genes, TRANSCRIPTION ELONGATION FACTOR 1 (TEF1) and CIRCADIAN CLOCK ASSOCIATED1 (CCA1), which show higher expression in S. spontaneum or S. officinarum, respectively. While the overexpression of SoCCA1 did not lead to significant phenotypic differences, overexpression of SsTEF1 in rice stimulated tillering and inhibited plant height, demonstrating its role in tillering regulation. However, the overexpression of suggests that SoCCA1 may not be the key regulator of sugarcane tillering. Yeast one-hybrid assays identified four transcription factors (TFs) regulating SsTEF1 and four and five TFs regulating SsCCA1 and SoCCA1. This study provides a theoretical foundation for deciphering the molecular mechanisms underlying the different tillering behaviors between S. officinarum and S. spontaneum, providing valuable insights for the molecular-based design of sugarcane breeding strategies.

Qi, Yiying↗
DOE OSTI2016

Atomistic modeling of the metallic-to-semiconducting phase boundaries in monolayer MoS{sub 2}

Recent experimental demonstration on the coexistence of metallic and semiconducting phases in the same monolayer MoS{sub 2} crystal has attracted much attention for its use in ultra-low contact resistance-MoS{sub 2} transistors. However, the electronic structures of the metallic-to-semiconducting phase boundaries, which appear to dictate the carrier injection in such transistors, are not yet well understood. In this letter, interfacing the 2H and 1T′ polytypes appropriately, we first model the “beta” (β) and the “gamma” (γ) phase boundaries, and demonstrate good agreement with experiential results. We then apply first-principles based density functional theory to calculate the electronic structures for those optimized geometries. We further employ non equilibrium Green's function formalism to evaluate the transmission spectra and the local density of states (LDOS) in order to assess the Schottky barrier nature of the phase boundaries. Our study reveals that while the γ boundary yields p-type Schottky barrier, the β boundary leads to the distinct symmetric Schottky barrier with an atomically sharp transition region. This understanding could be useful for designing high performance transistors using phase-engineered MoS{sub 2} crystals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Adaptive Variational Quantum Imaginary Time Evolution Approach for Ground State Preparation

Abstract An adaptive variational quantum imaginary time evolution (AVQITE) approach is introduced that yields efficient representations of ground states for interacting Hamiltonians on near‐term quantum computers. It is based on McLachlan's variational principle applied to imaginary time evolution of variational wave functions. The variational parameters evolve deterministically according to equations of motions that minimize the difference to the exact imaginary time evolution, which is quantified by the McLachlan distance. Rather than working with a fixed variational ansatz, where the McLachlan distance is constrained by the quality of the ansatz, the AVQITE method iteratively expands the ansatz along the dynamical path to keep the McLachlan distance below a chosen threshold. This ensures the state is able to follow the quantum imaginary time evolution path in the system Hilbert space rather than in a restricted variational manifold set by a predefined fixed ansatz. AVQITE is used to prepare ground states of H 4 , H 2 O, and BeH 2 molecules, where it yields compact variational ansätze and ground state energies within chemical accuracy. Polynomial scaling of circuit depth with system size is shown through a set of AVQITE calculations of quantum spin models. Finally, quantum Lanczos calculations are demonstrated alongside AVQITE without additional quantum resource costs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗