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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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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Asymmetrically Stratified Beaufort Gyre: Mean State and Response to Decadal Forcing

Abstract Recent progress in understanding Beaufort Gyre (BG) dynamics reveals an important role of ice‐ocean stress in stabilizing BG freshwater content (FWC) over seasonal to interannual timescales. But how the BG's stratification and FWC respond to surface forcing over decadal timescales has not been fully explored. Using a global ocean‐sea ice model, we partition the BG into upper, middle (halocline), and lower (thermocline) layers and perform a volume budget analysis over 1948–2017. We find that the BG's asymmetric geometry (with steep and tight isohalines over continental slopes relative to the deep basin) is key in determining the mean volume transport balance. We further find that a net Ekman suction during 1983–1995 causes the upper and middle layers to deflate isopycnally, while an enhanced Ekman pumping during 1996–2017 causes these layers to inflate both isopycnally and diapycnally, the latter via anomalous flux from the upper to the middle layer.

Zhang, Jiaxu↗
DOE OSTI2023

The Influence of Cloud Types on Cloud–Radiative Forcing During DYNAMO/AMIE

Cloud-radiative feedback is known to be an important process for the Madden-Julian Oscillation (MJO) and its accurate representation in general circulation models. The MJO is known to have a stronger cloud-radiative feedback compared to higher frequency convectively coupled equatorial waves (CCEW), for reasons that remain unclear. The objective of this study is to use data from the Dynamics of the MJO/Atmospheric Radiation Measurement MJO Investigation Experiment field campaign to investigate how cloud type evolution and their associated cloud-radiative forcing (CRF) differ between the MJO and CCEWs and their application to MJO dynamics under moisture mode theory. This study finds that the amplitude of CRF is the largest within the MJO compared to the higher frequency CCEWs, and CRF maximizes after the maximum precipitation (enhanced phase) of the MJO. We suggest this delay in the timing of maximum CRF occurs because most cloud types (congestus, deep, anvil, and cirrus) simultaneously maximize in frequency after the enhanced phase of the MJO, which is not seen for the higher frequency CCEWs. These specific cloud types help maintain the MJO through their radiatively-driven moistening which acts to prolong the enhanced phase of the MJO. The decomposition of radiatively-driven moistening by cloud types shows that simultaneous moistening by deep, anvil, and congestus clouds are particularly responsible for the maintenance of the MJO under moisture mode theory. Further, this analysis suggests the importance of capturing the correct evolution of different cloud types and their associated radiative effects to better understand the thermodynamics of the MJO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Orographic Cirrus and Its Radiative Forcing in NCAR CAM6

Cirrus clouds play an important role in the Earth's radiative energy budget, thereby affecting the climate state and climate change. Orographic gravity wave (OGW)-induced sub-grid scale vertical velocity (i.e., cooling rate) is not resolved by large-scale models and its impact on ice formation in cirrus clouds is not well quantified. In this study, one sub-grid scale OGW scheme (e.g., McFarlane) is used in the Community Atmosphere Model version 6 (CAM6) to generate vertical velocity variance (σw) for cirrus formation. Results from the default model and simulations with the OGW-induced σw are evaluated against the DOE ARM Small Particles in Cirrus (SPARTICUS) campaign observations. The OGW based on the McFarlane scheme increases the sub-grid scale σw over mountains compared to the default model and improves the model agreement with the SPARTICUS observations. Larger σ w due to OGWs can trigger more frequent homogeneous nucleation in orographic cirrus and generates a higher number concentration of ice crystals observed during the SPARTICUS campaign. Moreover, our evaluation of the model simulations against satellite observations indicates that the McFarlane scheme generates high in-cloud ice number concentrations (>200 L -1 ) in the upper troposphere over mountains and high plateaus at mid- and high-latitudes of the winter hemisphere as shown in the observations. More ice crystals with smaller sizes absorb more infrared radiation (+0.523 ± 0.125 W m -2 ). The net radiative cloud forcing change at the top of the atmosphere is +0.330 W m -2 due to the orographic cirrus.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Larger Cloud Liquid Water Enhances Both Aerosol Indirect Forcing and Cloud Radiative Feedback in Two Earth System Models

Previous studies have noticed that the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with a stronger cooling from aerosol-cloud interactions (ACI) also have an enhanced warming from positive cloud feedback, and these two opposing effects are counter-balanced in simulations of the historical period. However, reasons for this anti-correlation are less explored. In this study, we perturb the cloud ice microphysical processes to obtain cloud liquid of varying amounts in two Earth System Models (ESMs). We find that the model simulations with a larger liquid water path (LWP) tend to have a stronger cooling from ACI and a stronger positive cloud feedback. More liquid clouds in the mean-state present more opportunities for anthropogenic aerosol perturbations and also weaken the negative cloud feedback at middle to high latitudes. This work, from a cloud state perspective, emphasizes the influence of the mean-state LWP on effective radiative forcing due to ACI (ERF ACI ).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Electrical Responses of Modified Mineral Surfaces as Observed With Spectral Induced Polarization and Atomic Force Microscopy

Abstract Atomic force microscopy (AFM) and spectral induced polarization (SIP) are widely used to investigate the electrical properties of mineral surfaces at vastly different scales of measurement. We compare AFM and SIP measurements made on two different materials (glass beads and silica gel) subjected to etching, deposition of iron oxide particles, and inclusion of calcite grains. We found that the treatments produced qualitatively consistent behaviors in the AFM and SIP data. Direct AFM measurements of surface charge density for silica and calcite surfaces were quantitatively compared to values estimated from the SIP results using a grain polarization model. No statistically significant difference (at a 95% confidence level) was found between the surface charge density of silica estimated by AFM (2.3 ± 6.6 mC/m 2 for glass beads and 1.6 ± 0.1 mC/m 2 for silica gel) versus SIP (5.4 ± 4.4 mC/m 2 for glass beads and 1.6 ± 0.5 mC/m 2 for silica gel). The surface charge density for calcite determined by AFM (43.5 ± 12.9 mC/m 2 ) was approximately 19 times higher than that found for silica. While the charge density of calcite surfaces determined by SIP was also generally higher than that found for silica, different treatments produced significantly different values between 4.7 and 258 mC/m 2 (with a maximum 95% CI of ±8.7 mC/m 2 ). Several possible explanations exist for the range of the observed SIP measurements, including aging of the calcite surfaces. Overall, this study suggests the potential for the complementary use of AFM and SIP measurements to constrain future investigations of polarization mechanisms in porous media.

Geochemistry & Geophysics↗
DOE OSTI2025

Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect

Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.

Rugenstein, Maria [Colorado State University Fort ↗
DOE OSTI2025

ELM–Wet: Inclusion of a Wet–Landunit With Sub–Grid Representation of Eco–Hydrological Patches and Hydrological Forcing Improves Methane Emission Estimations in the E3SM Land Model (ELM)

Wetlands are the largest emitters of biogenic methane (CH 4 ) and represent the highest source of uncertainty in global CH 4 budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH 4 flux predictions. We develop an updated version, ELM-Wet, where we activate a separate landunit for wetlands that handles multiple wetland-specific eco-hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site-level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO 2 and CH 4 fluxes in the developed wet-landunit. Site-level simulations of a coastal non-tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO 2 and CH 4 fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch-specific chamber flux observations and observations of CH 4 concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM-Wet reduces the model's CH 4 emission root mean squared error by up to 33% and is able to represent inter-daily CO 2 and CH 4 flux variability across the wetland's eco-hydrological patches, including during periods of extreme dry or wet conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Impacts of Mean State Ocean Heat Transport on Climate and Its Response to CO 2 Forcing

Simulations of the slab ocean configuration of the coupled Energy Exascale Earth System Model (E3SM) were used to isolate the role of poleward ocean heat transport (OHT) in shaping the climate and its response to CO 2 forcing. Imposed changes to mean-state OHT produce compensating changes in atmospheric heat transport (AHT) that are mediated by changes in surface evaporation. A reduction of maximum OHT by 0.56 PW (32%) reduces the global mean surface air temperature by 3.6°C. However, this cooler mean state exhibits 1.2°C more warming under CO 2 quadrupling, with the largest differences occurring at high latitudes. The amplified warming arises from stronger surface albedo and lapse rate feedbacks in polar regions and a shortwave cloud feedback in the southern midlatitudes. These results highlight the critical role of mean-state OHT in modulating mean-state climate, the partitioning between the OHT and AHT, and climate sensitivity.

Atmosphere-ocean-ice interactions↗
DOE OSTI2023

Global organic and inorganic aerosol hygroscopicity and its effect on radiative forcing

The climate effects of atmospheric aerosol particles serving as cloud condensation nuclei (CCN) depend on chemical composition and hygroscopicity, which are highly variable on spatial and temporal scales. Here we present global CCN measurements, covering diverse environments from pristine to highly polluted conditions. We show that the effective aerosol hygroscopicity, κ, can be derived accurately from the fine aerosol mass fractions of organic particulate matter (ϵ org ) and inorganic ions (ϵ inorg ) through a linear combination, κ = ϵ org · κ org + ϵ inorg · κ inorg . In spite of the chemical complexity of organic matter, its hygroscopicity is well captured and represented by a global average value of κ org = 0.12 ± 0.02 with κinorg = 0.63 ± 0.01 as the corresponding value for inorganic ions. By showing that the sensitivity of global climate forcing to changes in κ org and κ inorg is small, we constrain a critically important aspect of global climate modelling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Thermodynamic driving forces in contact electrification between polymeric materials

Abstract Contact electrification, or contact charging, refers to the process of static charge accumulation after rubbing, or even simple touching, of two materials. Despite its relevance in static electricity, various natural phenomena, and numerous technologies, contact charging remains poorly understood. For insulating materials, even the species of charge carrier may be unknown, and the direction of charge-transfer lacks firm molecular-level explanation. Here, we use all-atom molecular dynamics simulations to investigate whether thermodynamics can explain contact charging between insulating polymers. Based on prior work suggesting that water-ions, such as hydronium and hydroxide ions, are potential charge carriers, we predict preferred directions of charge-transfer between polymer surfaces according to the free energy of water-ions within water droplets on such surfaces. Broad agreement between our predictions and experimental triboelectric series indicate that thermodynamically driven ion-transfer likely influences contact charging of polymers. Furthermore, simulation analyses reveal how specific interactions of water and water-ions proximate to the polymer-water interface explain observed trends. This study establishes relevance of thermodynamic driving forces in contact charging of insulators with new evidence informed by molecular-level interactions. These insights have direct implications for future mechanistic studies and applications of contact charging involving polymeric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-‘Herringbone,’ Au(110)-(1 × 2)-‘Missing-Row,’ and Au(100)-‘Quasi-Hexagonal’ reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗
DOE OSTI2020

Tensor factorization for elucidating mechanisms of piezoresponse relaxation via dynamic Piezoresponse Force Spectroscopy

Spatially resolved time and voltage-dependent polarization dynamics in PbTiO 3 thin films is explored using dynamic piezoresponse force microscopy (D-PFM) in conjunction with interferometric displacement sensing. This approach gives rise to 4D data sets containing information on bias-dependent relaxation dynamics at each spatial location without long-range electrostatic artifacts. To interpret these data sets in the absence of defined physical models, we employ a non-negative tensor factorization method which clearly presents the data as a product of simple behaviors allowing for direct physics interpretation. Correspondingly, we perform phase-field modeling finding the existence of ‘hard’ and ‘soft’ domain wall edges. This approach can be extended to other multidimensional spectroscopies for which even exploratory data analysis leads to unsatisfactory results due to many components in the decomposition.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep learning for exploring ultra-thin ferroelectrics with highly improved sensitivity of piezoresponse force microscopy

Hafnium oxide-based ferroelectrics have been extensively studied because of their existing ferroelectricity, even in ultra-thin film form. However, studying the weak response from ultra-thin film requires improved measurement sensitivity. In general, resonance-enhanced piezoresponse force microscopy (PFM) has been used to characterize ferroelectricity by fitting a simple harmonic oscillation model with the resonance spectrum. However, an iterative approach, such as traditional least squares (LS) fitting, is sensitive to noise and can result in the misunderstanding of weak responses. In this study, we developed the deep neural network (DNN) hybrid with deep denoising autoencoder (DDA) and principal component analysis (PCA) to extract resonance information. The DDA/PCA-DNN improves the PFM sensitivity down to 0.3 pm, allowing measurement of weak piezoresponse with low excitation voltage in 10-nm-thick Hf 0.5 Zr 0.5 O 2 thin films. Our hybrid approaches could provide more chances to explore the low piezoresponse of the ultra-thin ferroelectrics and could be applied to other microscopic techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy

We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combination of ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. It allows the identification of which of the available observational channels, sampled sequentially, are most predictive of selected behaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in piezoresponse force microscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictive channel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. The same workflow and code are applicable for any multimodal imaging and local characterization methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Relation between interfacial shear and friction force in 2D materials

Understanding the interfacial properties between an atomic layer and its substrate is of key interest at both the fundamental and technological levels. From Fermi level pinning to strain engineering and superlubricity, the interaction between a single atomic layer and its substrate governs electronic, mechanical and chemical properties. Here, we measure the hardly accessible interfacial transverse shear modulus of an atomic layer on a substrate. By performing measurements on bulk graphite, and on epitaxial graphene films on SiC with different stacking orders and twisting, as well as in the presence of intercalated hydrogen, we find that the interfacial transverse shear modulus is critically controlled by the stacking order and the atomic layer–substrate interaction. Importantly, we demonstrate that this modulus is a pivotal measurable property to control and predict sliding friction in supported two-dimensional materials. The experiments demonstrate a reciprocal relationship between friction force per unit contact area and interfacial shear modulus. As a result, the same relationship emerges from simulations with simple friction models, where the atomic layer– substrate interaction controls the shear stiffness and therefore the resulting friction dissipation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Elucidating nanoscale mechanical properties of diabetic human adipose tissue using atomic force microscopy

Abstract Obesity-related type 2 diabetes (DM) is a major public health concern. Adipose tissue metabolic dysfunction, including fibrosis, plays a central role in DM pathogenesis. Obesity is associated with changes in adipose tissue extracellular matrix (ECM), but the impact of these changes on adipose tissue mechanics and their role in metabolic disease is poorly defined. This study utilized atomic force microscopy (AFM) to quantify difference in elasticity between human DM and non-diabetic (NDM) visceral adipose tissue. The mean elastic modulus of DM adipose tissue was twice that of NDM adipose tissue (11.50 kPa vs. 4.48 kPa) to a 95% confidence level, with significant variability in elasticity of DM compared to NDM adipose tissue. Histologic and chemical measures of fibrosis revealed increased hydroxyproline content in DM adipose tissue, but no difference in Sirius Red staining between DM and NDM tissues. These findings support the hypothesis that fibrosis, evidenced by increased elastic modulus, is enhanced in DM adipose tissue, and suggest that measures of tissue mechanics may better resolve disease-specific differences in adipose tissue fibrosis compared with histologic measures. These data demonstrate the power of AFM nanoindentation to probe tissue mechanics, and delineate the impact of metabolic disease on the mechanical properties of adipose tissue.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Radiative forcing from the 2020 shipping fuel regulation is large but hard to detect

Reduction in aerosol cooling unmasks greenhouse gas warming, exacerbating the rate of future warming. The strict sulfur regulation on shipping fuel implemented in 2020 (IMO2020) presents an opportunity to assess the potential impacts of such emission regulations and the detectability of deliberate aerosol perturbations for climate intervention. Here we employ machine learning to capture cloud natural variability and estimate a radiative forcing of +0.074 ±0.005 W m -2 related to IMO2020 associated with changes in shortwave cloud radiative effect over three low-cloud regions where shipping routes prevail. We find low detectability of the cloud radiative effect of this event, attributed to strong natural variability in cloud albedo and cloud cover. Regionally, detectability is higher for the southeastern Atlantic stratocumulus deck. These results raise concerns that future reductions in aerosol emissions will accelerate warming and that proposed deliberate aerosol perturbations such as marine cloud brightening will need to be substantial in order to overcome the low detectability.

54 ENVIRONMENTAL SCIENCES↗