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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Influence of Carbon-Nitride Dot-Emitting Species and Evolution on Fluorescence-Based Sensing and Differentiation

Carbon dots have attracted widespread interest for sensing applications based on their low cost, ease of synthesis, and robust optical properties. We investigate structure–function evolution on multiemitter fluorescence patterns for model carbon-nitride dots (CNDs) and their implications on trace-level sensing. Hydrothermally synthesized CNDs with different reaction times were used to determine how specific functionalities and their corresponding fluorescence signatures respond upon the addition of trace-level analytes. Archetype explosives molecules were chosen as a testbed due to similarities in substituent groups or inductive properties (i.e., electron withdrawing), and solution-based assays were performed using ratiometric fluorescence excitation–emission mapping (EEM). Analyte-specific quenching and enhancement responses were observed in EEM landscapes that varied with the CND reaction time. We then used self-organizing map models to examine EEM feature clustering with specific analytes. Finally, the results reveal that interactions between carbon-nitride frameworks and molecular-like species dictate response characteristics that may be harnessed to tailor sensor development for specific applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

High Resolution Infrared Spectroscopy of Highly Reactive Chemical Intermediates: Berkeley Inspiration and a C.B. Moore Retrospective

One of the long-standing paradoxes in chemistry is that the molecular species of greatest interest are often highly reactive chemical intermediates and thus present only at vanishingly small concentrations under typical steady state conditions. Here, this has proven both a key frustration and powerful motivation for physical chemists in probing such species, particularly for high resolution laser spectroscopic methods in the infrared. Over the past 4-5 decades, this fundamental sensitivity challenge has stimulated the development of many novel techniques for efficient generation, cooling, and infrared probing of such transient chemical intermediates, achieving increasingly sophisticated levels of spectroscopic detail, chemical insight, and a testbed for comparison with first principles ab initio quantum calculations. Two of the pioneering contributors to this scientific arena have been George Pimentel and C. Bradley Moore, both responsible for nourishing DJN’s own enduring fascination with spectroscopic IR study of elusive chemical intermediates. This chapter is not intended to provide a comprehensive review of this large and enormously successful field, but it rather more simply attempts to capture a few selective “Moore-centric” snapshots of the Nesbitt group scientific evolution, specifically in i) advances by Pimentel for the original development of rapid scan flash kinetic spectroscopy based on spectrometers with rapidly rotating IR gratings, ii) the modernization/extension of these methods in the Moore group toward the first high resolution IR laser spectroscopy of singlet/triplet methylene 1,3 CH 2 diradical, and finally to iii) the development of slit supersonic discharge expansion methods at JILA for sub-Doppler infrared laser study of multiple radicals, jet cooled molecular ions, and highly reactive chemical intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A High-Fidelity Molecular Model of the Cu(111) Repeating Unit

Dynamic processes at surfaces are central to heterogeneous catalysis, but their atomistic mechanism(s) can prove difficult to elucidate due to variations in material structure and the corresponding impact on reactivity. Moreover, disparities between reaction conditions and those employed for spectroscopic characterization at surfaces can inhibit detailed understanding of catalysis-relevant chemistries. Herein, we substantiate the so-called “cluster-surface” analogy by leveraging a low-valent tricopper architecture ( 1 ) as a model system for small molecule activation at Cu(111). Two reaction classes are explored: the adsorption of carbon monoxide (CO) and the dissociative adsorption of dihydrogen (H 2 ). These processes serve as an ideal testbed to compare the reactivity of a molecular cluster ( 1 ) to that of a heterogeneous surface, as both reactions have empirical data from measurements performed on crystalline Cu(111). Cluster 1 reversibly binds CO. Variable temperature NMR analysis with 13 CO reveals a favorable enthalpy but large negative entropy (−5.1 kcal × mol –1 and −22.9 cal × mol –1 × K –1 , respectively) for CO binding, affording a process that is marginally endergonic at room temperature (ΔG ads (298.15 K) = 1.7 ± 0.5 kcal × mol –1 ). Similarly, analogous to a Cu(111) surface, 1 is shown to oxidatively add (chemisorb) H 2 . Kinetic parameters were determined for this process and the activation enthalpy (8.4 ± 0.5 kcal × mol –1 ) closely mirrors that established for H 2 binding at the Cu(111) facet (6.0 to 12.4 kcal × mol –1 ). Together, these results showcase that a trinuclear cluster can reproduce the small molecule binding and activation energetics of a bulk crystalline surface, setting the stage for studying less-defined surface processes in an atomically precise molecular setting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Testing Cloud Microphysics Parameterizations in NCAR CAM5 with ISDAC and M-PACE Observations

Arctic clouds simulated by the NCAR Community Atmospheric Model version 5 (CAM5) are evaluated with observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Indirect and Semi-Direct Aerosol Campaign (ISDAC) and Mixed-Phase Arctic Cloud Experiment (M-PACE), which were conducted at its North Slope of Alaska site in April 2008 and October 2004, respectively. Model forecasts for the Arctic Spring and Fall seasons performed under the Cloud- Associated Parameterizations Testbed (CAPT) framework generally reproduce the spatial distributions of cloud fraction for single-layer boundary layer mixed-phase stratocumulus, and multilayer or deep frontal clouds. However, for low-level clouds, the model significantly underestimates the observed cloud liquid water content in both seasons and cloud fraction in the Spring season. As a result, CAM5 significantly underestimates the surface downward longwave (LW) radiative fluxes by 20-40 W m-2. The model with a new ice nucleation parameterization moderately improves the model simulations by increasing cloud liquid water content in mixed-phase clouds through the reduction of the conversion rate from cloud liquid to ice by the Wegener-Bergeron- Findeisen (WBF) process. The CAM5 single column model testing shows that change in the homogeneous freezing temperature of rain to form snow from -5 C to -40 C has a substantial impact on the modeled liquid water content through the slowing-down of liquid and rain-related processes. In contrast, collections of cloud ice by snow and cloud liquid by rain are of minor importance for single-layer boundary layer mixed-phase clouds in the Arctic.

58 GEOSCIENCES↗
DOE OSTI2012

TWP-ICE Global Atmospheric Model Intercomparison: Convection Responsiveness and Resolution Impact

Results are presented from an intercomparison of global atmospheric model (GAM) simulations of tropical convection during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE). The distinct cloud properties, precipitation, radiation, and vertical diabatic heating profiles associated with three different monsoon regimes (wet, dry, and break) from available observations are used to evaluate 9 GAM forecasts initialized daily from realistic global analyses. All models well captured the evolution of large-scale circulation and the thermodynamic fields, but cloud properties differed substantially among models. For example, liquid water path and ice water path differed by up to two orders of magnitude. Compared with the relatively well simulated top-heavy heating structures during the wet and break period, most models had difficulty in depicting the bottom-heavy heating profiles associated with cumulus congestus. The best performing models during this period were the ones whose convection scheme was most responsive to the free tropospheric humidity. Compared with the large impact of cloud and convective parameterizations on model cloud and precipitation characteristics, resolution has relatively minor impact on simulated cloud properties. However, one feature that was influence by the resolution study in several models was the diurnal cycle of precipitation. Peaking at a different time from convective precipitation, large-scale precipitation generally increases in high resolution forecasts and modulates the total precipitation diurnal cycle. Overall, the study emphasizes the importance of more environmental responsive convective parameterizations to capture various types of convection and the substantial diversity among large-scale cloud and precipitation schemes in current GAMs. This experiment has also demonstrated itself to be a very useful testbed for those developing cloud and convection schemes in these models.

58 GEOSCIENCES↗
DOE OSTI2021

The Implications of Global Change for the Co-Evolution of Argentina's Integrated Energy-Water-Land Systems

This study seeks to understand how Argentina’s energy, water, and land (EWL) systems will co-evolve under a representative array of human and earth system influences, including socioeconomic change, climate change, and climate policy. To capture Argentina’s sub-national EWL dynamics in the context of global change, we couple the Global Change Analysis Model with a suite of consistent, gridded sectoral downscaling models to explore multiple stakeholder-engaged scenarios. Across scenarios, Argentina has the economic opportunity to use its vast land resources to satisfy growing domestic and international demand for crops, such as oil (e.g., soy) and biomass. The human (rather than earth) system produces the most dominant changes in mid-century EWL resource use. A Reference scenario characterized by modest socioeconomic growth projects a 40% increase in Argentina’s agricultural production by 2050 (relative to 2020) by using 50,000 km 2 of additional cropland and 40% more water. A Climate Policy scenario designed to achieve net-zero carbon emissions globally shortly after mid-century projects that Argentina could use 100,000 km 2 of additional land (and 65% more water) to grow biomass and other crops. The burden of navigating these national opportunities and challenges could fall disproportionately on a subset of Argentina’s river basins. The Colorado and Negro basins could experience moderate-to-severe water scarcity as they simultaneously navigate substantial irrigated crop demand growth and climate-induced declines in natural water availability. Argentina serves as a generalizable testbed to demonstrate that multi-scale EWL planning challenges can be identified and managed more effectively via integrated analysis of coupled human-earth systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite-derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above-ground biomass. The adjusted simulation significantly reduced the above-ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation-constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Will Anthropogenic Warming Increase Evapotranspiration? Examining Irrigation Water Demand Implications of Climate Change in California

Abstract Climate modeling studies and observations do not fully agree on the implications of anthropogenic warming for evapotranspiration (ET), a major component of the water cycle and driver of irrigation water demand. Here, we use California as a testbed to assess the ET impacts of changing atmospheric conditions induced by climate change on irrigated systems. Our analysis of irrigated agricultural and urban regions shows that warmer atmospheric temperatures have minimal implications for ET rates and irrigation water demands—about one percent change per degree Celsius warming (∼1% °C −1 ). By explicitly modeling irrigation, we control for the confounding effect of climate‐driven soil moisture changes and directly estimate water demand implications. Our attribution analysis of the drivers of ET response to global anthropogenic warming shows that as the atmospheric temperature and vapor pressure deficit depart from the ideal conditions for transpiration, regulation of stomata resistance by stressed vegetation almost completely offsets the expected increase in ET rates that would otherwise result from abiotic processes alone. We further show that anthropogenic warming of the atmosphere has minimal implications for mean relative humidity (<1.7% °C −1 ) and the surface available energy (<0.2% °C −1 ), which are critical drivers of ET. This study corroborates the growing evidence that plant physiological changes moderate the degree to which changes in potential ET are realized as actual ET.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulating Global Terrestrial Carbon and Nitrogen Biogeochemical Cycles With Implicit and Explicit Representations of Soil Microbial Activity

Abstract Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of E3SM Simulated Aerosols and Aerosol‐Cloud Interactions Across GCM and Convection‐Permitting Scales

This paper introduces an Earth system modeling testbed for predicting aerosols and aerosol‐cloud interactions (ACIs) at convection‐permitting scales. Using the Energy Exascale Earth System Model (E3SM) version 2 with a four‐mode Modal Aerosol Module, we conduct simulations at 3.25 km resolution on a regionally refined mesh (RRM) across four regions with distinct aerosol and cloud regimes. Results are compared with the standard 100 km E3SM configuration and evaluated against satellite, aircraft, and ground‐based observations. We find that increasing model resolution improves heavy precipitation simulation but amplifies positive bias in light drizzle at coarse resolution. These resolution‐induced changes affect cloud and aerosol properties to varying degrees across regions. Generally, cloud cover and liquid water path (LWP) show better agreement with satellite retrievals at 3.25 km, though surface‐based comparisons suggest otherwise. Aerosol composition remains poorly represented at both resolutions. The RRM increases Aitken mode aerosol number concentrations via enhanced new particle formation. However, accumulation mode aerosols are decreased at higher resolution as aerosol removals become more efficient. This partially contributes to fewer cloud condensation nuclei (CCN) and lower cloud droplet number concentrations (N d ), which produces larger model biases in some scenarios. These findings suggest that solely increasing horizontal resolution to kilometer scales is insufficient to broadly improve aerosol and cloud predictions without concurrent advancements in physical and chemical process representations. Nonetheless, the RRM moderately improves key ACI relationships such as CCN‐N d correlation, reflecting enhanced aerosol activation representation. The LWP‐N d relationship is also better captured by RRM, suggesting a better characterization of LWP adjustment.

Huang, Meng [Pacific Northwest National Laboratory↗
DOE OSTI2017

Universal lower limit on vortex creep in superconductors

Superconductors are excellent testbeds for studying vortices, topological excitations that also appear in superfluids, liquid crystals and Bose–Einstein condensates. Vortex motion can be disruptive; it can cause phase transitions, glitches in pulsars, and losses in superconducting microwave circuits, and it limits the current-carrying capacity of superconductors4. Understanding vortex dynamics is fundamentally and technologically important, and the competition between thermal energy and energy barriers defined by material disorder is not completely understood. Specifically, early measurements of thermally activated vortex motion (creep) in iron-based superconductors unveiled fast rates (S) comparable to measurements of YBa 2 Cu 3 O 7–δ . This was puzzling because S is thought to somehow correlate with the Ginzburg number (Gi), and Gi is significantly lower in most iron-based superconductors than in YBa 2 Cu 3 O 7–δ . Here, we report very slow creep in BaFe 2 (As 0.67 P 0.33 ) 2 films, and propose the existence of a universal minimum realizable S ~ Gi 1/2 (T/T c ) (T c is the superconducting transition temperature) that has been achieved in our films and few other materials, and is violated by none. Furthermore, this limitation provides new clues about designing materials with slow creep and the interplay between material parameters and vortex dynamics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Harmonic to anharmonic tuning of moiré potential leading to unconventional Stark effect and giant dipolar repulsion in WS 2 /WSe 2 heterobilayer

Excitonic states trapped in harmonic moiré wells of twisted heterobilayers is an intriguing testbed for exploring many-body physics. However, the moiré potential is primarily governed by the twist angle, and its dynamic tuning remains a challenge. Here we demonstrate anharmonic tuning of moiré potential in a WS 2 /WSe 2 heterobilayer through gate voltage and optical power. A gate voltage can result in a local in-plane perturbing field with odd parity around the high-symmetry points. This allows us to simultaneously observe the first (linear) and second (parabolic) order Stark shift for the ground state and first excited state, respectively, of the moiré trapped exciton - an effect opposite to conventional quantum-confined Stark shift. Depending on the degree of confinement, these excitons exhibit up to twenty-fold gate-tunability in the lifetime (100 to 5 ns). Also, exciton localization dependent dipolar repulsion leads to an optical power-induced blueshift of ~ 1 meV/μW - a five-fold enhancement over previous reports.

42 ENGINEERING↗
DOE OSTI2024

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Uniaxial strain tuning of charge modulation and singularity in a kagome superconductor

Tunable quantum materials hold great potential for applications. Of special interest are materials in which small lattice strain induces giant electronic responses. The kagome compounds AV 3 Sb 5 (A = K, Rb, Cs) provide a testbed for electronic tunable states. In this study, through angle-resolved photo- emission spectroscopy, we provide comprehensive spectroscopic measure- ments of the electronic responses induced by compressive and tensile strains on the charge-density-wave (CDW) and van Hove singularity (VHS) in CsV 3 Sb 5 . We observe a tripling of the CDW gap magnitudes with ~ 1% strain. Simulta- neously, changes of both energy and mass of the VHS are observed. Combined, this reveals an anticorrelation between the unconventional CDW order para- meter and the mass of the VHS, and highlight the role of the latter in the superconducting pairing. The substantial electronic responses uncover a rich strain tunability of the versatile kagome system in studying quantum inter- plays under lattice variations.

electronic properties and materials↗
DOE OSTI2022

Electronic nature of the pseudogap in electron-doped Sr 2 IrO 4

In high-temperature ($T_c$) cuprate superconductors, many exotic phenomena are rooted in the enigmatic pseudogap state, which has been interpreted as consisting of preformed Cooper pairs or competing orders or a combination thereof. Observation of pseudogap phenomenologically in electron-doped Sr 2 IrO 4 —the 5d electron counterpart of the cuprates, has spurred intense interest in the strontium iridates as a testbed for exploring the exotic physics of the cuprates. Here, we examine the pseudogap state of electron-doped Sr 2 IrO 4 by angle-resolved photoemission spectroscopy (ARPES) and parallel theoretical modeling. Our analysis demonstrates that the pseudogap state of Sr 2 IrO 4 appears without breaking the particle–hole symmetry or inducing spectral broadening which are telltale signatures of competing orders in the cuprates. We find quasiparticle dispersion and its temperature dependence in the pseudogap state of Sr 2 IrO 4 to point to an electronic order with a zero scattering wave vector and limited correlation length. Particle–hole symmetric preformed Cooper pairs are discussed as a viable mechanism for such an electronic order. The potential roles of incommensurate density waves are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Gapless Dirac magnons in CrCl3

Abstract Bosonic Dirac materials are testbeds for dissipationless spin-based electronics. In the quasi two-dimensional honeycomb lattice of CrX 3 (X = Cl, Br, I), Dirac magnons have been predicted at the crossing of acoustical and optical spin waves, analogous to Dirac fermions in graphene. Here we show that, distinct from CrBr 3 and CrI 3 , gapless Dirac magnons are present in bulk CrCl 3 , with inelastic neutron scattering intensity at low temperatures approaching zero at the Dirac K point. Upon warming, magnon-magnon interactions induce strong renormalization and decreased lifetimes, with a ~25% softening of the upper magnon branch intensity from 5 to 50 K, though magnon features persist well above T N . Moreover, on cooling below ~50 K, an anomalous increase in the a -axis lattice constant and a hardening of a ~26 meV phonon feature are observed, indicating magnetoelastic and spin-phonon coupling arising from an increase in the in-plane spin correlations that begins tens of Kelvin above T N .

36 MATERIALS SCIENCE↗
DOE OSTI2022

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale ® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations

The density and configurational changes of crystal dislocations during plastic deformation influence the mechanical properties of materials. These influences have become clearest in nanoscale experiments, in terms of strength, hardness and work hardening size effects in small volumes. The mechanical characterization of a model crystal may be cast as an inverse problem of deducing the defect population characteristics (density, correlations) in small volumes from the mechanical behavior. In this work, we demonstrate how a deep residual network can be used to deduce the dislocation characteristics of a sample of interest using only its surface strain profiles at small deformations, and then statistically predict the mechanical response of size-affected samples at larger deformations. As a testbed of our approach, we utilize high-throughput discrete dislocation simulations for systems of widths that range from nano- to micro- meters. We show that the proposed deep learning model significantly outperforms a traditional machine learning model, as well as accurately produces statistical predictions of the size effects in samples of various widths. By visualizing the filters in convolutional layers and saliency maps, we find that the proposed model is able to learn the significant features of sample strain profiles.

36 MATERIALS SCIENCE↗