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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Role of Solvent Rearrangement on Mg2+ Solvation Structures in Dimethoxyethane Solutions Using Multimodal NMR Analysis

One of the main impediments faced for predicting important emergent properties of a multivalent electrolyte (such as conductivity and electrochemical stability) is the lack of quantitative analysis of ion-ion and ion-solvent interactions, which manifest in solvation structures and dynamics. In particular, the role of ion-solvent interactions is still unclear in cases where the strong electric field from multivalent cations can influence structural evolution of lower permittivity organic solvent molecules on solvation structure. These influences are commonly refered to as electrostriction pheonmena. Using quantitative 1H, 19F, 17O, and 25Mg NMR together with 19F nuclear spin relaxation and diffusion measurments, we find an unusual correlation between ion concentration and solvation structure of Mg(TFSI)2 salt in dimethoxyethane (DME) solution. The domiant solvation structure evolves from contact ion pairs (i.e. [Mg(TFSI)(DME)1-2]+) to fully solvated clusters (i.e. [Mg(DME)3]2+) as salt concentration increases or as temperature decreases. This transition is coupled to a phase separation, which we study here between 0.06 M and 0.36 M. Subsequent analysis is based on an explanation of the electrostriction phenomena and the competion between solvent molecules and TFSI anions for cation coordination.

Chen, Ying↗
DOE OSTI2026

Low-Temperature Non-Oxidative Coupling of Methane on Atomically Dispersed Titanium–Aluminum–Boron Nanopowder

Nonoxidative coupling of methane represents a long-standing challenge in heterogeneous catalysis, as it requires activation of the carbon–hydrogen (C–H) bond, controlled carbon–carbon (C–C) bond formation, and effective hydrogen management without relying on oxidants. Here, we report a low-temperature C–H activation and nonoxidative C–C coupling of methane over atomically dispersed titanium–aluminum–boron nanopowder (Ti–Al–B NP) utilizing a catalytic microreactor coupled to synchrotron single-photon photoionization reflectron time-of-flight mass spectrometry. The soft-ionization, in situ probing method detects the nascent reaction products and radical intermediates under operando conditions, including methyl radical, C2 hydrocarbons, and molecular hydrogen. Methane activation is initiated at 800 K, approximately 700 K below the gas-phase decomposition threshold, leading predominantly to ethylene formation with selectivity reaching up to 78% among the C–C coupled products. Electronic structure calculations on model Ti–Al–B clusters elucidate a cooperative catalytic mechanism in which titanium enables methane adsorption and C–H activation, boron acts as a reversible hydrogen reservoir, and aluminum stabilizes methylene intermediates, thereby facilitating selective C–C coupling and dehydrogenation. These findings establish a distinct catalyst architecture for nonoxidative methane coupling based on earth abundant elements alternative to expensive platinum and other noble metal-containing conventional catalysts and provide molecular-level design principles for controlling dehydrogenation and subsequent C–C bond formation in challenging light alkane conversions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Landfalling Droughts: Global Tracking of Moisture Deficits From the Oceans Onto Land

Abstract Droughts threaten food, energy, and water security, causing death and displacement of millions of people and billions of dollars in damages. However, there are still important gaps in the understanding of drought mechanisms and behaviors, inhibiting the accuracy of early‐warning systems designed to protect communities worldwide. We use an object‐tracking algorithm to track clusters of precipitation‐minus‐evaporation moisture deficits across land and ocean areas of the globe from 1981–2018. This analysis reveals a new type of “landfalling drought” that originates over the ocean and “migrates” onto land. We find that 16% of droughts that affected the continents worldwide from 1981–2018 were landfalling droughts. These droughts were significantly larger (220–425%) and more intense (4–30%)—and grew (253–285%) and intensified (9–28%) faster—than droughts that developed solely over the land or ocean. To identify potential underlying mechanisms, we analyze moisture transport associated with landfalling droughts over western North America. We find that landfalling droughts in this region are associated with anomalously anticyclonic atmospheric pressure patterns that reduce moisture fluxes over the Pacific Ocean toward the continent. By advancing understanding of the spatiotemporal evolution of droughts, our findings offer the potential to improve seasonal‐scale prediction and long‐term projection of global drought risks.

Herrera‐Estrada, Julio E.↗
DOE OSTI2020

Control of single-ligand chemistry on thiolated Au 25 nanoclusters

Diverse methods have been developed to tailor the number of metal atoms in metal nanoclusters, but control of surface ligand number at a given cluster size is rare. Here we demonstrate that reversible addition and elimination of a single surface thiolate ligand (-SR) on gold nanoclusters can be realized, opening the door to precision ligand engineering on atomically precise nanoclusters. We find that oxidative etching of [Au 25 SR 18 ] – nanoclusters adds an excess thiolate ligand and generates a new species, [Au 25 SR 19 ] 0 . The addition reaction can be reversed by CO reduction of [Au 25 SR 19 ] 0 , leading back to [Au 25 SR 18 ] – and eliminating precisely one surface ligand. Intriguingly, we show that the ligand shell of Au 25 nanoclusters becomes more fragile and rigid after ligand addition. This reversible addition/elimination reaction of a single surface ligand on gold nanoclusters shows potential to precisely control the number of surface ligands and to explore new ligand space at each nuclearity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Significant contributions of trimethylamine to sulfuric acid nucleation in polluted environments

Abstract As one of the least understood aerosol processes, nucleation can be a dominant source of atmospheric aerosols. Sulfuric acid (SA)-amine binary nucleation with dimethylamine (DMA) has been recognized as a governing mechanism in the polluted continental boundary layer. Here we demonstrate the importance of trimethylamine (TMA) for nucleation in the complex atmosphere and propose a molecular-level SA-DMA-TMA ternary nucleation mechanism as an improvement upon the conventional binary mechanism. Using the proposed mechanism, we could connect the gaseous amines to the SA-amine cluster signals measured in the atmosphere of urban Beijing. Results show that TMA can accelerate the SA-DMA-based new particle formation in Beijing by 50–100%. Considering the global abundance of TMA and DMA, our findings imply comparable importance of TMA and DMA to nucleation in the polluted continental boundary layer, with probably higher contributions from TMA in polluted rural environments and future urban environments with controlled DMA emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The DESI One-Percent Survey: Modelling the clustering and halo occupation of all four DESI tracers with U CHUU

We present results from a set of mock lightcones for the DESI One-Percent Survey, created from the UCHUU simulation. This 8 h −3 Gpc 3 N-body simulation comprises 2.1 trillion particles and provides high-resolution dark matter (sub)haloes in the framework of the Planck-based ΛCDM cosmology. Employing the subhalo abundance matching (SHAM) technique, we populated the UCHUU (sub)haloes with all four DESI tracers – Bright Galaxy Survey (BGS), luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars (QSOs) – to z = 2.1. Our method accounts for redshift evolution as well as the clustering dependence on luminosity and stellar mass. The two-point clustering statistics of the DESI One-Percent Survey generally agree with predictions from UCHUU across scales ranging from 0.3 h −1 Mpc to 100 h −1 Mpc for the BGS and across scales ranging from 5 h −1 Mpc to 100 h −1 Mpc for the other tracers. We observed some differences in clustering statistics that can be attributed to incompleteness of the massive end of the stellar mass function of LRGs, our use of a simplified galaxy-halo connection model for ELGs and QSOs, and cosmic variance. We find that at the high precision of UCHUU, the shape of the halo occupation distribution (HOD) of the BGS and LRG samples is smaller bias values, likely due to cosmic variance. The bias dependence on absolute magnitude, stellar mass, and redshift aligns with that of previous surveys. These results provide DESI with tools to generate high-fidelity lightcones for the remainder of the survey and enhance our understanding of the galaxy-halo connection.

cosmology↗
DOE OSTI2023

Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structure–function coupling

At the biosphere–atmosphere interface, nonlinear interdependencies among components of an ecohydrological complex system can be inferred using multivariate high frequency time series observations. Information flow among these interacting variables allows us to represent the causal dependencies in the form of a directed acyclic graph (DAG). Here, we use high frequency multivariate data at 10 Hz from an eddy covariance instrument located at 25 m above agricultural land in the Midwestern US to quantify the evolutionary dynamics of this complex system using a sequence of DAGs by examining the structural dependency of information flow and the associated functional response. We investigate whether functional differences correspond to structural differences or if there are no functional variations despite the structural differences. We base our analysis on the hypothesis that causal dependencies are instigated through information flow, and the resulting interactions sustain the dynamics and its functionality. To test our hypothesis, we build upon causal structure analysis in the companion paper to characterize the information flow in similarly clustered DAGs from 3-min non-overlapping contiguous windows in the observational data. We characterize functionality as the nature of interactions as discerned through redundant, unique, and synergistic components of information flow. Through this analysis, we find that in turbulence at the biosphere–atmosphere interface, the variables that control the dynamic character of the atmosphere as well as the thermodynamics are driven by non-local conditions, while the scalar transport associated with CO and H 2 O is mainly driven by short-term local conditions.

58 GEOSCIENCES↗
DOE OSTI2022

Tonoplast Sucrose Trafficking Modulates Starch Utilization and Water Deficit Behavior in Poplar Leaves

Leaf osmotic adjustment by the active accrual of compatible organic solutes (e.g. sucrose) contributes to drought tolerance throughout the plant kingdom. In Populus tremula x alba, PtaSUT4 encodes a tonoplast sucrose–proton symporter, whose downregulation by chronic mild drought or transgenic manipulation is known to increase leaf sucrose and turgor. While this may constitute a single drought tolerance mechanism, we now report that other adjustments which can occur during a worsening water deficit are damped when PtaSUT4 is constitutively downregulated. Specifically, we report that starch use and leaf relative water content (RWC) dynamics were compromised when plants with constitutively downregulated PtaSUT4 were subjected to a water deficit. Leaf RWC decreased more in wild-type and vector control lines than in transgenic PtaSUT4-RNAi (RNA-interference) or CRISPR (clustered regularly interspersed short palindromic repeats) knockout (KO) lines. The control line RWC decrease was accompanied by increased PtaSUT4 transcript levels and a mobilization of sucrose from the mesophyll-enriched leaf lamina into the midvein. The findings suggest that changes in SUT4 expression can increase turgor or decrease RWC as different tolerance mechanisms to reduced water availability. Evidence is presented that PtaSUT4-mediated sucrose partitioning between the vacuole and the cytosol is important not only for overall sucrose abundance and turgor, but also for reactive oxygen species (ROS) and antioxidant dynamics. Interestingly, the reduced capacity for accelerated starch breakdown under worsening water-deficit conditions was correlated with reduced ROS in the RNAi and KO lines. A role for PtaSUT4 in the orchestration of ROS, antioxidant, starch utilization and RWC dynamics during water stress and its importance in trees especially, with their high hydraulic resistances, is considered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Coincident learning for beam-based rf station fault identification using phase information at the SLAC linac coherent light source

Anomalies in radio-frequency (rf) stations can result in unplanned downtime and performance degradation in linear accelerators such as SLAC’s Linac Coherent Light Source (LCLS). Detecting these anomalies is challenging due to the complexity of accelerator systems, high data volume, and scarcity of labeled fault data. Prior work identified faults using beam-based detection, combining rf amplitude and beam position monitor data. Due to the simplicity of the rf amplitude data, classical methods are sufficient to identify faults, but the recall is constrained by the low-frequency and asynchronous characteristics of the data. In this work, we leverage high-frequency, time-synchronous rf phase data to enhance anomaly detection in the LCLS accelerator. Due to the complexity of phase data, classical methods fail, and we instead train deep neural networks within the Coincident Anomaly Detection (CoAD) framework. We find that applying CoAD to phase data detects nearly 3 times as many anomalies as when applied to amplitude data, while achieving broader coverage across rf stations. Furthermore, the rich structure of phase data enables us to cluster anomalies into distinct physical categories. Through the integration of auxiliary system status bits, we link clusters to specific fault signatures, providing additional granularity for uncovering the root cause of faults. We also investigate interpretability via Shapley values, confirming that the learned models focus on the most informative regions of the data and providing insight for cases where the model makes mistakes. This work demonstrates that phase-based anomaly detection for rf stations improves both diagnostic coverage and root cause analysis in accelerator systems and that deep neural networks are essential for effective analysis.

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗
DOE OSTI2025

Synergistic Density Functional Theory and Molecular Dynamics Approach to Elucidate PNIPAM–Water Interaction Mechanisms

This study employs Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to investigate interactions between water molecules and Poly(N-isopropylacrylamide) (PNIPAM). DFT reveals preferential water binding sites, with enhanced binding energy observed in the linker zone. Quantum Theory of Atoms in Molecules (QTAIM) and electron localization function (ELF) analyses highlight the roles of hydrogen bonding and steric hindrance. MD simulations unveil temperature-dependent hydration dynamics, with structural transitions marked by changes in the radius of gyration (Rg) and the radial distribution function (RDF), aligning with DFT findings. Our work goes beyond prior studies by combining a DFT, QTAIM and MD simulations approach across different PNIPAM monomer-to-30mer structures. It introduces a systematic quantification of pseudo-saturation thresholds and explores water clustering dynamics with structural specificity, which have not been previously reported in the literature. These novel insights establish a more complete molecular-level picture of PNIPAM hydration behavior and temperature responsiveness, emphasizing the importance of amide hydrogen and carbonyl oxygen sites in hydrogen bonding, which weakens above the lower critical solution temperature (LCST), resulting in increased hydrophobicity and paving the way for understanding water sorption mechanisms, offering guidance for future applications such as dehumidification and atmospheric water harvesting.

08 HYDROGEN↗
NASA NTRS1982

Quantum chemical calculation of the equilibrium structures of small metal atom clusters

Metal atom clusters are studied based on the application of ab initio quantum mechanical approaches. Because these large 'molecular' systems pose special practical computational problems in the application of the quantum mechanical methods, there is a special need to find simplifying techniques that do not compromise the reliability of the calculations. Research is therefore directed towards various aspects of the implementation of the effective core potential technique for the removal of the metal atom core electrons from the calculations.

Kahn, L. R.↗
DOE OSTI2026

Cross-correlation of SPT-3G D1 CMB lensing and DES Y3 galaxy lensing

Measurements of the weak lensing of galaxies and of the cosmic microwave background (CMB) provide direct probes of the cosmic matter density field, but the two observables are sensitive to different spatial scales, redshift ranges, and survey systematics. Their cross-correlation thus enables consistency checks of the theoretical model and of potential systematics in either dataset. We present measurements of the cross-correlation between CMB lensing and cosmic shear over $\sim$1,300 deg$^2$ of the sky using the SPT-3G D1 CMB lensing maps and the Dark Energy Survey Year 3 (DES Y3) shear catalogs. For the first time, we measure this cross-correlation at high significance ($\sim 14σ$) when using a polarization-only CMB lensing reconstruction that is expected to be robust against biases induced by extragalactic foregrounds. We test a variety of other CMB lensing estimators that include temperature information and exhibit different tradeoffs between foreground biases and noise, as well as a shear sample that consists of blue, star-forming galaxies and has been shown to be less impacted by galaxy intrinsic alignments. Assuming $Λ$CDM and marginalizing over uncertainties in intrinsic alignments, baryonic feedback, and various nuisance parameters, we obtain a constraint on the amplitude of matter clustering $S_8 \equiv σ_8 \sqrt{Ω_m / 0.3} = 0.833^{+0.047}_{-0.061}$, consistent with both the primary CMB results from Planck and shear-only results from DES Y3. By combining our measurement with Planck, we find mild constraints on the astrophysical processes that impact the cross-correlation. We obtain a constraint on the intrinsic alignment amplitude of the DES sample that is competitive with that from shear-only analyses, and we find a lower limit on the strength of baryonic feedback.

Ouellette, A. [Illinois U., Urbana] (ORCID:0000000↗
NASA NTRS1974

Finding the earth in ERTS

Itek's Optical System Division recently completed an investigation funded by NASA to develop interpretation methods and algorithms suitable for recognition of earth resources by machines using multispectral data from ERTS. Through the algorithms developed (and described here) it is now possible to automatically recognize terrain types. The clustering algorithm guarantees high accuracy in the recognition process with almost complete automation. Interestingly, the machine recognition seems to be more accurate than a human photointerpreter who has been restricted to using only ERTS-1 color composites. That is, machine recognition appears to be more sensitive, it can operate much closer, to the resolution limit of the ERTS-1 imagery than the human photointerpreter.

Gramenopoulos, N.↗
DOE OSTI2020

The completed SDSS-IV extended baryon oscillation spectroscopic survey: pairwise-inverse probability and angular correction for fibre collisions in clustering measurements

ABSTRACT The completed extended Baryon Oscillation Spectroscopic Survey (eBOSS) catalogues contain redshifts of 344 080 quasars at 0.8 < z < 2.2, 174 816 luminous red galaxies between 0.6 < z < 1.0, and 173 736 emission-line galaxies over 0.6 < z < 1.1 in order to constrain the expansion history of the Universe and the growth rate of structure through clustering measurements. Mechanical limitations of the fibre-fed spectrograph on the Sloan telescope prevent two fibres being placed closer than 62 arcsec in a single pass of the instrument. These ‘fibre collisions’ strongly correlate with the intrinsic clustering of targets and can bias measurements of the two-point correlation function resulting in a systematic error on the inferred values of the cosmological parameters. We combine the new techniques of pairwise-inverse probability and the angular upweighting (PIP+ANG) to correct the clustering measurements for the effect of fibre collisions. Using mock catalogues, we show that our corrections provide unbiased measurements, within data precision, of both the projected $\rm {\mathit{ w}_p}\left(\mathit{ r}_p\right)$ and the redshift-space multipole ξ(ℓ = 0, 2, 4)(s) correlation functions down to $0.1\, h^{-1}{\rm Mpc}$, regardless of the tracer type. We apply the corrections to the eBOSS DR16 catalogues. We find that, on scales $s\gtrsim 20\, h^{-1}{\rm Mpc}$ for ξℓ, as used to make baryon acoustic oscillation and large-scale redshift-space distortion measurements, approximate methods such as nearest-neighbour upweighting are sufficiently accurate given the statistical errors of the data. Using the PIP method, for the first time for a spectroscopic program of the Sloan Digital Sky Survey, we are able to successfully access the one-halo term in the clustering measurements down to $\sim 0.1\, h^{-1}{\rm Mpc}$ scales. Our results will therefore allow studies that use the small-scale clustering to strengthen the constraints on both cosmological parameters and the halo occupation distribution models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Optical Properties of Low‐Defect Large‐Area Hexagonal Boron Nitride for Quantum Applications

Intrinsic defects and their concentrations in hexagonal boron nitride (h-BN) play a key role in single-photon emission. In this study, the optical properties of large-area multilayer h-BN-on-sapphire grown by metal-organic chemical vapor deposition are explored. Based on the detailed spectroscopic characterization using both cathodoluminescence (CL) and photoluminescence (PL) measurements, the material is devoid of random single-point defects instead of a few clustered complex defects. The emission spectra of the measurements confirm a record-low-defect concentration of ≈10 4 cm −2 . Post-annealing, no significant changes are observed in the measured spectra and the defect concentrations remain unaltered. Through CL and PL spectroscopy, an optically active boron vacancy spin defect is identified and a novel complex defect combination arising from carbon impurities is revealed. This complex defect, previously unreported, signifies a unique aspect of the material. In these findings, the understanding of defect-induced optical properties in h-BN films is contributed, providing insights for potential applications in quantum information science.

42 ENGINEERING↗
DOE OSTI2023

Six More Ultra-faint Milky Way Companions Discovered in the DECam Local Volume Exploration Survey

We report the discovery of six ultra-faint Milky Way satellites identified through matched-filter searches conducted using Dark Energy Camera (DECam) data processed as part of the second data release of the DECam Local Volume Exploration (DELVE) survey. Leveraging deep Gemini/GMOS-N imaging (for four candidates) as well as follow-up DECam imaging (for two candidates), we characterize the morphologies and stellar populations of these systems. We find that these candidates all share faint absolute magnitudes (M$_{V}$ ≥ −3.2 mag) and old, metal-poor stellar populations (τ > 10 Gyr, [Fe/H] < −1.4 dex). Three of these systems are more extended (r$_{1/2}$ > 15 pc), while the other three are compact (r$_{1/2}$ < 10 pc). From these properties, we infer that the former three systems (Boötes V, Leo Minor I, and Virgo II) are consistent with ultra-faint dwarf galaxy classifications, whereas the latter three (DELVE 3, DELVE 4, and DELVE 5) are likely ultra-faint star clusters. Using data from the Gaia satellite, we confidently measure the proper motion of Boötes V, Leo Minor I, and DELVE 4, and tentatively detect a proper-motion signal from DELVE 3 and DELVE 5; no signal is detected for Virgo II. We use these measurements to explore possible associations between the newly discovered systems and the Sagittarius dwarf spheroidal, the Magellanic Clouds, and the Vast Polar Structure, finding several plausible associations. Our results offer a preview of the numerous ultra-faint stellar systems that will soon be discovered by the Vera C. Rubin Observatory and highlight the challenges of classifying the faintest stellar systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗