SEARCH · Engineering Papers

Results for “Surface estimation”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 955 records · Page 53

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗

DOE OSTI2025

Assessing the Performance of Exchange‐Correlation Density Functionals in Describing the Iron‐Catalyzed Ammonia Synthesis System

Density functional theory (DFT) has been widely employed for elucidating mechanistic aspects of heterogeneous catalysis. However, the accuracy of DFT calculations relies heavily on selecting an exchange-correlation (XC) functional that correctly describes the electronic structure of materials involved in the reactions. This study assesses the accuracy of several XC density functionals for modeling the iron-catalyzed ammonia synthesis system. In the assessment of functional accuracy, experimental references are compared to DFT-calculated values for the formation energy of gas-phase ammonia and nitrogen, bulk Fe/Fe-nitride (γ′-Fe 4 N) lattice constants and cohesive/formation energies, and nitrogen and ammonia binding energies on Fe(100), Fe(111), Fe(110), and γ′-Fe 4 N(111). It is observed that the experimental value for each of these descriptors is accurately modeled by at least one functional. RPBE alone provides reliable estimates for both the lattice constant and cohesive energy of Fe and γ′-Fe 4 N. Temperature-programmed desorption experiments led to estimates for N and NH 3 adsorption across several Fe-based facets that are best captured by RPBE. These results highlight the importance of choosing an appropriate XC functional that accurately describes experimental systems and offer insights into effectively modeling the interactions between nitrogen and ammonia on Fe-based surfaces.

adsorption↗

DOE OSTI2022

Laboratory Heat Flux Estimates of Seawater Foam for Low Wind Speeds

Laboratory experiments were conducted to measure the heat flux from seafoam continuously generated in natural seawater. Using a control volume technique, heat flux was calculated from foam and foam-free surfaces as a function of ambient humidity (ranged from 40% to 78%), air–water temperature difference (ranged from -9 °C to 0 °C), and wind speed (variable up to 3 m s-1). Water-surface skin temperature was imaged with a calibrated thermal infrared camera, and near-surface temperature profiles in the air, water, and foam were recorded. Net heat flux from foam surfaces increased with increasing wind speed and was shown to be up to four times greater than a foam-free surface. The fraction of the total heat flux due to the latent heat flux was observed for foam to be 0.75, with this value being relatively constant with wind speed. In contrast, for a foam-free surface the fraction of the total heat flux due to the latent heat flux decreased at higher wind speeds. Temperature profiles through foam are linear and have larger gradients, which increased with wind speed, while foam free surfaces show the expected logarithmic profile and show no variation with temperature. The radiometric surface temperatures show that foam is cooler and more variable than a foam-free surface, and bubble-resolving thermal images show that radiometrically transparent bubble caps and burst bubbles reveal warm foam below the cool surface layer, contributing to the enhanced variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Mechanistic Study of 1,2-Dichloroethane Hydrodechlorination on Cu-Rich Pt–Cu Alloys: Combining Reaction Kinetics Experiments with DFT Calculations and Microkinetic Modeling

Cu-rich Pt–Cu bimetallic catalysts are among the most promising candidates for actively catalyzing the hydrodechlorination of 1,2-dichloroethane (1,2-DCA) toward ethylene production. Combining reaction kinetics experiments with density functional theory (DFT) calculations and mean-field microkinetic modeling, we present a systematic mechanistic study for 1,2-DCA hydrodechlorination on Cu-rich Pt–Cu alloy catalysts. Our DFT (PBE+(TS+SCS)) results suggest that increasing Cu content in the Pt–Cu alloy destabilizes C 2 -species adsorption while stabilizing the binding of atomic chlorine. The reaction energetics of all the elementary steps in the 1,2-DCA reaction network were calculated on a Pt 1 Cu 3 (111) model surface. The DFT results were then used to construct a microkinetic model, and the model-predicted reaction rates were compared with our reaction kinetics experimental results on a Cu-rich SiO 2 -supported Pt–Cu alloy catalyst through a parameter estimation procedure. Both the reaction kinetics experiments and the microkinetic model after parameter adjustments yielded 100% selectivity to ethylene. The microkinetic model pointed to a reaction pathway involving two sequential chlorine-removal steps on the Pt–Cu alloy catalyst, a mechanism distinct from the one previously identified on pure Pt/SiO 2 catalysts, which involved an initial hydrogen-removal step. Adjustments to the DFT-derived parameters indicate the possible formation of chlorine-induced Cu-enriched surface sites during 1,2-DCA hydrodechlorination conditions, sites that are more active than those encountered in the bulk Pt 1 Cu 3 (111) alloy surface. Furthermore, our study offers valuable initial insights on the 1,2-DCA hydrodechlorination reaction mechanism and the nature of the active sites on PtCu bimetallic catalysts.

1,2-Dichloroethane↗

DOE OSTI2022

Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California

Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Quantifying Errors in Effective Cluster Interactions of Lattice Gas Cluster Expansions

The promise of lattice gas (LG) cluster expansions (CEs) is that they can describe a given system property to any level of accuracy since the orthogonal “cluster basis functions” span the complete space of available configurations. Such an approach can be constructed to an arbitrarily large surface of a finite number of distinct adsorption sites. Unfortunately, this is only true for the case of an ideal, fixed lattice decorated with components at precise lattice points (the lattice “sites”) with no distortions or relaxations subsequently allowed. Since most systems, and surfaces specifically, do not conform to such an ideal set of constraints, errors in LG CEs must be expected or CE convergence severely hampered. Beyond this, numerical errors in the provided data can complicate the proper construction of a truly predictive and/or physically significant CE. In this work, we show here how reliance on typical statistical tools like confidence intervals cannot be expected to provide an accurate representation of the uncertainty of effective cluster interactions (ECIs) in the CE due to the nature of the target ab initio data and the nature of CEs themselves. We develop a method for estimating these errors that does not rely on statistical assumptions about the model or data. We then use these ECI errors to quantify fundamental consequences on the uncertainty of ECIs in CEs built from O/Fe(100) data whose surface and adsorbates have been allowed to relax in a typical manner and from O/Fe(100) data whose surface and adsorbates are fixed in ideal lattice positions. We also quantify the effect of using a different density functional theory exchange–correlation functional, using these ECI errors to assess the significance in any deviations. In both cases, our method is shown to have remarkable utility in the quantification of errors in the ECIs of CEs. While we stick to the lattice gas convention in this work, the method is equally applicable to the Ising convention or, in principle, any linear model of sufficient complexity.

08 HYDROGEN↗

DOE OSTI2023

A Comparison of Adverse Meteorology Inputs for Emergency Management and Safety Basis Dispersion Modeling Applications at SRS

The MELCOR Accident Consequence Calculation System (MACCS), used for safety basis accident analyses at SRS, implements adjustment factors to consider the increased vertical turbulence associated with local surface roughness and the increased lateral turbulence associated with time-based plume meander. The two models used for SRS emergency planning radiological consequence assessment, i.e., Hotspot and Puff/Plume, currently do not have an option to input or apply diffusion adjustments for meander or surface roughness. Adverse meteorology conditions at SRS have been determined as E stability class and 1.3 meters per second wind speed for ground level releases. This is the input used for emergency planning models. Contrastingly safety basis analyses use a different input, representing adverse meteorology values as F stability class and wind speeds of 1.3 meters per second (for a ground level release). This results in an apparent discrepancy between the two approaches. In this report we document the differing modeling assumptions, discuss the logic behind each modeling approach used to represent local roughness and plume meander conditions, and numerically compare dispersion coefficients and concentration estimates obtained from each set of model inputs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A probabilistic inverse prediction method for predicting plutonium processing conditions

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Molecule-surface interaction from van der Waals-corrected semilocal density functionals: The example of thiophene on transition-metal surfaces

Semilocal density functional approximations are widely used. None of them can capture the long-range van der Waals (vdW) attraction between separated subsystems, but they differ remarkably in the extent to which they capture intermediate-range vdW effects responsible for equilibrium bonds between neighboring small closed-shell subsystems. The local density approximation (LDA) often overestimates this effect, while the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) underestimates it. The strongly constrained and appropriately normed (SCAN) meta-GGA often estimates it well. All of these semilocal functionals require an additive nonlocal correction such as the revised Vydrov-Van Voorhis 2010 (rVV10) to capture the long-range part. This work reports adsorption energies and the corresponding geometry of aromatic thiophene (C 4 H 4 S) bound to transition-metal surfaces. The adsorption process requires a genuine interplay of covalent and weak binding and requires a simultaneously accurate description of surface and adsorption energies with the correct prediction of the adsorption site. All these quantities must come from well-balanced short- and long-range correlation effects for a universally applicable method for weak interactions with chemical accuracy. Here, our methods indicate that the correct interplay is not present in any combination of recent meta-GGA's and rVV10. The simple short-range damping of the vdW correction scheme that is practically successful in the combination of GGA's and vdW approximations is less transferable in SCAN+rVV10 or in the revised version, revSCAN+rVV10. In addition, we present accurate random-phase-approximation-quality adsorption energies from a model based on the one of Zaremba and Kohn.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

IMS observations of infrasound and acoustic-gravity waves produced by the January 2022 volcanic eruption of Hunga, Tonga: A global analysis

The 15 January 2022 Hunga, Tonga, volcano's explosive eruption produced the most powerful blast recorded in the last century, with an estimated equivalent TNT yield of 100–200 megatons. The blast energy was propagated through the atmosphere as various wave types. The most prominent wave was a long-period (>2000 s) surface-guided Lamb wave with energy comparable to that of the 1883 Krakatoa Lamb wave; both were clearly observed by pressure sensors (barometers) worldwide. Internal gravity, acoustic-gravity, and infrasound waves were captured in great detail by the entire infrasound component of the International Monitoring System (IMS). For instance, infrasound waves (<300 s period) were seen to circumnavigate Earth up to eight times. Atmospheric waves captured by the IMS infrasound network and selected barometers near the source provide insight on Earth's impulse response at planetary scales.

58 GEOSCIENCES↗

DOE OSTI2022

Insights into Controls of Mineralogy and Pore Structure on the Density of Methane Adsorption Phase in Shales under Supercritical Conditions

With gradually increasing enhancing population and industrialization, exploitation of shale gas is enhancing in the world to satisfy the growing demand for energy worldwide. To exploit shale gas efficiently, accurate assessment of shale gas in place (GIP) is necessary for determining production strategies. Unlike free and dissolved gas, adsorbed gas contributes to the shale GIP up to 85% due to the well-developed surface area and micropores. Measured excess adsorption of methane (up to over 95% of subsurface shale gas) is often corrected to absolute adsorption to obtain the actual amount of adsorption under supercritical (or geological) conditions. During the correction, adsorption phase density (APD) is critical. However, the APD of subsurface shale gas and the effects of shale properties (e.g., mineralogy and pore structure) on APD remain poorly understood. A series of high-pressure methane adsorption isotherms (HPMAI) on Caney Shales were collected and analyzed in conjunction with other United States and Chinese shales from the literature at 35–125 °C and up to 15 MPa. Here, a three-layer Ono–Kondo (OK3) model is utilized to derive the temperature- and pressure-dependent APD coupling low-pressure nitrogen adsorption isotherms (LPNAI) and HPMAI. X-ray diffraction and organic geochemistry are combined to reveal the mineralogy. Brunauer–Emmett–Teller, Barrett–Joyner–Halenda, and Horvath–Kawazoe analyses via LPNAI are used to investigate the pore structures. Results show that APD increases with organic matter (OM) proxied by total organic carbon and decreases with clay minerals and the sum of quartz and feldspar. OM dramatically contributes to the APD as multiple-layer adsorption exists, and the APD for OM could be 1.4–8.5 times that for clay minerals. Other inorganic minerals contribute less to APD. The properties and constitution of the surface area instead of the volume fraction contribute to the APD in shales. APD does not show an obvious correlation with micropore volume, likely related to the ratio of micropore volume to the total pore volume. Here, we provide a significant and comprehensive study of petrological factors that impact the APD of subsurface shale gas, which will improve the estimation of supercritical adsorption and shale GIP under reservoir conditions. Also, the findings in this work can provide applications for subsurface carbon dioxide adsorption and storage.

58 GEOSCIENCES↗

DOE OSTI2024

Dynamics of Electric Polarization and Relaxation of Ions at Humid Calcite Surfaces

Mobile ions at mineral surfaces can respond to an applied electric field, adopting a new distribution that effectively represents polarization of the electrical double layer. When the field is released, the ions relax to their equilibrium distribution. In both cases, the dynamics are characteristic of the interface. However, current models of electrokinetic phenomena are not sufficiently robust to accurately predict collective ion dynamics at structurally and chemically specific mineral–water interfaces. Here, in this study, we use electrostatic force microscopy (EFM) to investigate the dynamics of ion relaxation at hydrated calcite (104) surfaces at controlled relative humidity (RH). Electrically biased probes are used to polarize the distributions of calcium and carbonate ions that are intrinsic to this interface across a range of RH values. Polarization kinetics are tracked by monitoring the tip–sample force gradient during charging, and EFM imaging is used to characterize the spatial relaxation dynamics after the applied field is released. Electrostatic finite element modeling of the sample/probe system across length-scales from nanometers to millimeters reproduces the observed stretched exponential charging response. Together, these results allow us to estimate the ion diffusivities at the interface across a wide range of RH values. These diffusivities increase by roughly 5 orders of magnitude as the RH is increased from 5 to 90%, highlighting the critical role of adsorbed water for surface ion solvation that enables ion mobility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Effective climate sensitivity distributions from a 1D model of global ocean and land temperature trends, 1970–2021

Abstract Current theoretically based Earth system models (ESMs) produce Effective Climate Sensitivities (EffCS) that range over a factor of three, with 80% of those models producing stronger global warming trends for 1970–2021 than do observations. To make a more observationally based estimate of EffCS, a 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a full range of three model free parameters are evaluated to produce fits to a range of observed surface temperature trends (± 2σ) from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived EffCS are larger than over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.86 °C global EffCS, ± 34% range 1.48–2.15 °C) than the older Representative Concentration Pathway forcing (RCP6, 2.49 °C global average EffCS, ± 34% range 2.04–2.87 °C). The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone. The results are consistent with previous observation-based studies that concluded EffCS during the observational period is on the low end of the range produced by current ESMs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Multiscale Temporal Variability of the Global Air‐Sea CO 2 Flux Anomaly

Abstract The global air‐sea CO 2 flux (F) impacts and is impacted by a plethora of climate‐related processes operating at multiple time scales. In bulk mass transfer formulations, F is driven by physico‐ and bio‐chemical factors such as the air‐sea partial pressure difference (∆pCO 2 ), gas transfer velocity, sea surface temperature, and salinity–all varying at multiple time scales. To de‐convolve the impact of these factors on variability in F at different time scales, time‐resolved estimates of F were computed using a global data set assembled between 1988 and 2015. The F anomalies were defined as temporal deviations from the 28‐year time‐averaged value. Spectral analysis revealed four dominant timescales of variability in F–subseasonal, seasonal, interannual, and decadal with relative amplitude differences varying across regions. A second‐order Taylor series expansion was then conducted along these four timescales to separate drivers across differing regions. The analysis showed that on subseasonal timescales, wind speed variability explains some 66% of the global F anomaly and is the dominant driver. On seasonal, interannual, and decadal timescales, the ∆pCO 2 effect controlled by the ∆pCO 2 anomaly, explained much of the F anomaly. On decadal timescales, the F anomaly was almost entirely governed by the ∆pCO 2 effect with large contributions from high latitudes. The main drivers across timescales also dominate the regional F anomaly, particularly in the mid‐high latitude regions. Finally, the driver of the ∆pCO 2 effect was closely connected with the relative strength of atmospheric pCO 2 and the nonthermal component of oceanic pCO 2 anomaly associated with dissolved inorganic carbon and alkalinity.

Environmental Sciences & Ecology↗

DOE OSTI2025

Resolving Low Cloud Feedbacks Globally With E3SM High‐Res MMF: Agreement With LES but Stronger Shortwave Effects

This study investigates low cloud feedback in a warmer climate using global simulations from the High-Resolution Multi-scale Modeling Framework (HR-MMF), which explicitly simulates small-scale eddies globally. Two 5-year simulations—one with present-day sea surface temperatures (SSTs) and a second with SSTs warmed uniformly by 4 K—reveal a positive global shortwave cloud radiative effect (SWCRE = 0.3 W/m 2 /K), comparable to estimates from CMIP models. As the climate warms, significant reductions in low cloud cover occur over stratocumulus regions. This study is the first attempt to compare HR-MMF results with predictions from idealized large-eddy simulations from the CGILS intercomparison. Despite different underlying assumptions, we find qualitative agreement in SWCRE and inversion height changes between HR-MMF and CGILS predictions. This suggests reasonable credibility for the CGILS framework in predicting cloud responses under the out-of-sample conditions found in HR-MMF. However, the HR-MMF exhibits stronger SWCRE changes than predicted by CGILS. We explore potential causes for this discrepancy, examining variations in cloud-controlling factors (CCFs) and cloud conditions. Our results show a fairly homogeneous SWCRE response, with little systematic variation tied to the variations in CCFs. This reveals a dominant role for SST forcing in modulating SWCRE.

boundary-layer clouds↗

DOE OSTI2021

Dielectric screening in perovskite photovoltaics

Abstract The performance of perovskite photovoltaics is fundamentally impeded by the presence of undesirable defects that contribute to non-radiative losses within the devices. Although mitigating these losses has been extensively reported by numerous passivation strategies, a detailed understanding of loss origins within the devices remains elusive. Here, we demonstrate that the defect capturing probability estimated by the capture cross-section is decreased by varying the dielectric response, producing the dielectric screening effect in the perovskite. The resulting perovskites also show reduced surface recombination and a weaker electron-phonon coupling. All of these boost the power conversion efficiency to 22.3% for an inverted perovskite photovoltaic device with a high open-circuit voltage of 1.25 V and a low voltage deficit of 0.37 V (a bandgap ~1.62 eV). Our results provide not only an in-depth understanding of the carrier capture processes in perovskites, but also a promising pathway for realizing highly efficient devices via dielectric regulation.

14 SOLAR ENERGY↗

DOE OSTI2022

Hyphae move matter and microbes to mineral microsites: Integrating the hyphosphere into conceptual models of soil organic matter stabilization

Associations between soil minerals and microbially derived organic matter (often referred to as mineral-associated organic matter or MAOM) form a large pool of slowly cycling carbon (C). Here, the rhizosphere, soil immediately adjacent to roots, is thought to control the spatial extent of MAOM formation because it is the dominant entry point of new C inputs to soil. However, emphasis on the rhizosphere implicitly assumes that microbial redistribution of C into bulk (non-rhizosphere) soils is minimal. We question this assumption, arguing that because of extensive fungal exploration and rapid hyphal turnover, fungal redistribution of soil C from the rhizosphere to bulk soil minerals is common, and encourages MAOM formation. First, we summarize published estimates of fungal hyphal length density and turnover rates and demonstrate that fungal C inputs are high throughout the rhizosphere–bulk soil continuum. Second, because colonization of hyphal surfaces is a common dispersal mechanism for soil bacteria, we argue that hyphal exploration allows for the non-random colonization of mineral surfaces by hyphae-associated taxa. Third, these bacterial communities and their fungal hosts determine the chemical form of organic matter deposited on colonized mineral surfaces. Collectively, our analysis demonstrates that omission of the hyphosphere from conceptual models of soil C flow overlooks key mechanisms for MAOM formation in bulk soils. Moving forward, there is a clear need for spatially explicit, quantitative research characterizing the environmental drivers of hyphal exploration and hyphosphere community composition across systems, as these are important controls over the rate and organic chemistry of C deposited on minerals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Effects of Shallow-Velocity Reductions on 3D Propagation of Seismic Waves

Abstract We perform 3D simulations of seismic wavefields to clarify effects of strong reductions of shallow velocities on long-period seismic waves. The simulations use a reference Community Velocity Model of southern California and a modified version with strong velocity reductions in the top 500 m of the Los Angeles basin. Differences between wavefields generated by 10 earthquakes in the reference and perturbed models are analyzed. Velocity changes are estimated by measuring relative time shifts between reference and perturbed seismograms using wavelet cross-correlation spectra. The results indicate that strong localized temporal velocity drops near the surface, such as those observed during strong ground motions, may generate regional perturbations of wavefields at periods up to 20 s. These perturbations may be misinterpreted as generated by temporal changes at seismogenic depths. The results also have important implications for waveform tomography studies.

Geochemistry & Geophysics↗