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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics

Abstract Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R 2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.

58 GEOSCIENCES↗
DOE OSTI2025

Identifying Urban Pluvial Frequency Flooding Hotspots Using the Topographic Control Index and Remote Sensing Radar Images for Early Warning Systems

Identifying areas that frequently experience post-rainfall ponding is essential for effective flood mitigation and planning. This study integrates Sentinel-1 radar imagery and the Topographic Control Index (TCI) to identify 378 flood-prone urban depressions in Beaumont, Texas. Out of 159 major rainfall events, only six had Sentinel-1 radar imagery acquired within six hours of peak rainfall, and these were used to generate the flood frequency map; the ground-based flood sensor data were used to verify that these selected events corresponded to actual peak rainfall and to validate radar-detected water pixels. Validation results showed 100% precision, 70.87% recall, an F1-score of 82.95%, and 71.32% overall accuracy. Approximately 84% of medium-to-high TCI depressions overlapped with Beaumont’s two-year inundation map, confirming a strong relationship between TCI and observed flooding. A total of 124 depressions retained significant water, and after excluding 25 engineered detention ponds, 99 natural depressions remained flood vulnerable. Among these, 74 depressions with medium or high TCI were identified as the highest-priority nuisance flooding hotspots. The results demonstrate that combining TCI with radar imagery provides a reliable and cost-effective approach for identifying areas prone to frequent urban ponding. This framework supports practical decision-making for drainage improvements, hotspot identification, and early-warning system development in urban flood-prone regions.

Sentinel-1 radar imagery↗
DOE OSTI2022

Soil frost controls streamflow generation processes in headwater catchments

The relationship between snowmelt and spring streamflow is changing under warming temperatures and diminishing snowpack. At the same time, the hydrologic connectivity across catchment landscape elements, such as snowpack and surface wetlands, can play a critical role in controlling the routing of snowmelt to streams. The role of hydrologic connectivity is important in headwater regions of the continental northern latitudes, where catchments have low topographic relief and seasonally frozen ground. Nevertheless, the effects of soil frost on the sequence, timing, and magnitudes of hydrologic events that drive the movement of water from a snowpack to a stream are not fully understood. Therefore, we examine two questions: First, what is the flowpath that snow melt and precipitation from spring rain events takes to generate spring streamflow, and second, what hydrologic, climatic, or landscape variables exert the most control on the magnitude of streamflow? In this work, we use long-term hydrological records from the two reference basins at the Marcell Experimental Forest in northern Minnesota to analyze the cascading effects across precipitation, snow, water table elevation, soil frost, and streamflow in peatland-dominated headwater catchments. We identify a sequence of fill-and-spill effects across the landscape that control the timing of spring streamflow generation. Then, we use stepwise regression to show that soil frost is a key supporting predictor for both the magnitude of streamflow in the spring as it adds significantly to the predictive power of precipitation and water table elevation. Our results highlight the importance of recognizing the role of soil frost, when present, on the partitioning of snowmelt between overland runoff and water table recharge during the critical snowmelt period, as well as the later partitioning between evapotranspiration and subsurface flows.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Topographical shifts in fine root lifespan in a mixed, mesic temperate forest

Root lifespan, often is estimated in landscape- and ecosystem-level carbon models using linear approximations. In water manipulation experiments, fine root lifespan can vary with soil water content. Soil water content is generally structured by complex topography, which is largely unaccounted for in landscape- and ecosystem-scale carbon models. Topography governs the range of soil water content experienced by roots which may impact their lifespan. We hypothesized that root lifespan varied nonlinearly across a temperate, mesic, forested catchment due to differences in soil water content associated with topographic position. We expected regions of the landscape that were too wet or too dry would have soils that were not optimal for roots and thus result in shorter root lifespans. Specifically, we hypothesized that root lifespan would be longest in areas that consistently had soil water content in the middle of the soil water content spectrum, while in soils at either very low or very high soil water content, root lifespan would be relatively short. We tested this hypothesis by collecting and analyzing two years of minirhizotron and soil moisture data in plots widely distributed in the Shale Hills catchment of the Susquehanna-Shale Hills Critical Zone Observatory in Pennsylvania. We found that fine root lifespans were longer in traditionally wetter topographic regions, but detected no short term (biweekly) effect of soil moisture on root lifespan. Additionally, depth in soil, soil series, slope face orientation, and season of birth strongly affected root lifespans across the catchment. In contrast, lifespan was unaffected by root diameter or mycorrhizal association. Failure to account for these variables could result in erroneous estimates of fine root lifespan and, consequentially, carbon flux in temperate forested regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multi-layering of SU-8 exhibits distinct geometrical transitions from circular to planarized profiles

The negative tone photoresist SU-8 permits the creation of micrometer-scale structures by optical lithography. It is also the most used photoresist in soft lithography for the fast-prototyping of microfluidic devices. Despite its importance, the effect of capillary forces on SU-8 multi-layering onto topographical features has not been thoroughly studied. In particular, the profile of the added layer has not been examined in detail. The overlaying process exhibits a set of distinct behaviors, or regimes, depending on the relative thickness of the overlay and the underlying rectangular pattern. We demonstrate how capillary effects control the profile of multi-layer microchannels in a predictable manner. We derive a simple static model to describe the evolution of the overlay as a function of dimensionless geometric parameters. Our study provides a critical understanding of the parameters that govern multi-layer spin coating.

Sauzade, Martin↗
DOE OSTI2021

3D Seismic Anatomy of a Watershed Reveals Climate–Topography Coupling That Drives Water Flowpaths and Bedrock Weathering

To investigate how bedrock transforms to soil, we mapped the topography of the interface demarcating onset of weathering under an east-west trending shale watershed in the Valley and Ridge province in the U.S.A. Using wave-equation travel-time tomography from a seismic array of >4000 geophones, we obtained a 3D P-wave velocity (Vp) model that resolves structures ~20 m below land surface (mbls). The depth of mobile soil and the onset of dissolution of chlorite roughly match Vp = 600 m/s and Vp = 2700 m/s, respectively. Chlorite dissolution initiates porosity growth in the shale matrix. Depth to the 2700 m/s contour is greater under the N- as compared to S-facing hillslopes and under sub-planar as compared to concave-up land surfaces. Broadly, the geometries of the ‘soil’ and ‘chlorite’ Vp contours are consistent with the calculated potential for shear fracture opening under weak regional compression. However, this calculated fracture potential does not consistently explain observations related to N- vs. S-facing aspect nor fracture density observed by borehole televiewer. Apparently, regional compression is only a secondary influence on Vp: the primary driver of P-wave slowing in the upper layers of this catchment is topographic control of reactive water flowpaths and their integrated effects on weathering. Here, the Vp result is best explained as the long-term integrated effect of groundwater flow-induced geochemical weathering of shale in response to climate-driven patterns of micro- and macro-topography.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multi-cyclone analysis and machine learning model implications of cyclone effects on forests

Past studies of cyclones (hurricanes, typhoons, tropical cyclones) disturbance showed that meteorological, topographical, and biological factors affect the patterns of forest disturbance intensity but left open the extent to which these findings were representative across different global cyclone regions. Using remote sensing data and machine learning models, we examined how these factors change over spatial scales and assessed their consistency across four major cyclones: Katrina (August 2005), Rita (September 2005), Yasi (February 2011), and María (September 2017). Our results revealed that the factors which best explained forest disturbance intensity pattern varied across these regions. Wind speed and precipitation were the dominant factors contributing to the variation in impacts of Katrina; terrain features, especially elevation, explained most of the variation in disturbance intensity of Rita; pre-disturbance vegetation condition was significant predictors of effects of Yasi; these factors played equal roles in explaining the disturbance intensity variation of María. A 40 m/s (144 km/h) wind speed threshold was proposed to split low- and high-level forest disturbance intensity. Other than wind speed, few generalizations can be made on features across multiple regions. We built several generalized hurricane impact models, which worked well with the test data from cyclones used for model development (R 2 = 0.89). However, these models did not have good predictions on other cyclones, such as Michael (October 2018) and Laura (August 2020). This study showed that each cyclone interacted with the landscape in a unique way and the challenges remained in building a generalized cyclone impact model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhancing Short-Range Weather Forecasts through Temporal Variation Encoding: A Multiperiod Embedding Approach

Machine learning (ML) techniques have emerged as promising approaches to improve regional weather forecast accuracy and reliability through data-driven methods. We propose a novel ML-based weather forecasting model, the Multiperiod Embed Net (MPENet). A key distinguishing feature of MPENet is its explicit utilization of the inherent cyclic nature in weather dynamics, unlike the autoregressive strategies commonly used in other ML weather forecasting approaches. Critical cyclic structures are identified via Fourier analyses of dynamic time series. Cyclicity in the convolutional representation is achieved by transforming one-dimensional time series of meteorological variables into two-dimensional tensors based on identified periods. This approach enables the model to leverage intrinsic weather patterns, enhancing regional forecast performance. To demonstrate the effectiveness of MPENet, we conduct a comparative analysis with Nvidia’s FourCastNet. Both models are trained on High-Resolution Rapid Refresh (HRRR) data from 2015 to 2022, over a 192 km × 192 km region in Tennessee. The comparisons are performed locally at two specific locations known to have different weather dynamics due to orographic effects: Crossville, on the relatively flat Cumberland Plateau with fewer topographic airflow disruptions, and Oak Ridge, in the ridge-and-valley region, where airflow is heavily influenced by surrounding valleys and mountains. Our results indicate that FourCastNet achieves strong accuracy at very short lead times, while MPENet maintains competitive skill and shows advantages in capturing temporal evolution over longer periods. Cross-correlation analyses of MPENet and FourCastNet predictions with the HRRR data suggest that encoding critical cyclicity into the network architecture leads to improvements in the forecasting skill.

Artificial intelligence↗
DOE OSTI2020

X-ray verification of sol-gel resist shrinkage in substrate-conformal imprint lithography for a replicated blazed reflection grating

Surface-relief gratings fabricated through nanoimprint lithography (NIL) are prone to topographic distortion induced by resist shrinkage. Characterizing the impact of this effect on blazed diffraction efficiency is particularly important for applications in astrophysical spectroscopy at soft x-ray wavelengths ( λ ≈ 0.5 − 5 nm) that call for the mass-production of large-area grating replicas with sub-micron, sawtooth surface-relief profiles. A variant of NIL that lends itself well for this task is substrate-conformal imprint lithography (SCIL), which uses a flexible, composite stamp formed from a rigid master template to imprint nanoscale features in an inorganic resist that cures thermodynamically through a silica sol-gel process. While SCIL enables the production of several hundred imprints before stamp degradation and avoids many of the detriments associated with large-area imprinting in NIL, the sol-gel resist suffers shrinkage dependent on the post-imprint cure temperature. Through atomic force microscopy and diffraction-efficiency testing at beamline 6.3.2 of the Advanced Light Source, the impact of this effect on blaze response is constrained for a ∼160-nm-period grating replica cured at 90 ° C. Results demonstrate a ∼2 ° reduction in blaze angle relative to the master grating, which was fabricated by anisotropic wet etching in 〈311〉-oriented silicon to yield a facet angle close to 30 ° .

McCoy, Jake A. (ORCID:0000000216057517)↗
DOE OSTI2025

Reduced Erosion Augments Soil Carbon Storage Under Cover Crops

ABSTRACT Cover crops, a promising strategy to increase soil organic carbon (SOC) storage in croplands and mitigate climate change, have typically been shown to benefit soil carbon (C) storage from increased plant C inputs. However, input‐driven C benefits may be augmented by the reduction of C outputs induced by cover crops, a process that has been tested by individual studies but has not yet been synthesized. Here we quantified the impact of cover crops on organic C loss via soil erosion (SOC erosion) and revealed the geographical variability at the global scale. We analyzed the field data from 152 paired control and cover crop treatments from 57 published studies worldwide using meta‐analysis and machine learning. The meta‐analysis results showed that cover crops widely reduced SOC erosion by an average of 68% on an annual basis, while they increased SOC stock by 14% (0–15 cm). The absolute SOC erosion reduction ranged from 0 to 18.0 Mg C −1 ha −1 year −1 and showed no correlation with the SOC stock change that varied from −8.07 to 22.6 Mg C −1 ha −1 year −1 at 0–15 cm depth, indicating the latter more likely related to plant C inputs. The magnitude of SOC erosion reduction was dominantly determined by topographic slope. The global map generated by machine learning showed the relative effectiveness of SOC erosion reduction mainly occurred in temperate regions, including central Europe, central‐east China, and Southern South America. Our results highlight that cover crop‐induced erosion reduction can augment SOC stock to provide additive C benefits, especially in sloping and temperate croplands, for mitigating climate change.

Huang, Wenjuan [Department of Ecology, Evolution, ↗
DOE OSTI2021

A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA

The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Computational investigation of incident ion angles and material erosion at rough graphite and silicon carbide divertor surfaces

We present a computational investigation of the dependence of material erosion on the incident ion angle at rough graphite and silicon carbide divertor surfaces. Ion angle distributions (IADs) for D plasmas at NSTX-U and DIII-D divertors were calculated by an equation-of-motion model that traces the ion trajectories in the sheath. Then, the effective sputtering yields and ion shadowed area fractions were calculated by a Monte Carlo micro-patterning and roughness code that applied the calculated IADs to surface topographic data that were obtained from optical confocal microscopy of rough graphite and SiC divertor surfaces from NSTX-U and DIII-D experiments. The calculations found that the effective sputtering yields, the sputtering pattern, and the shadowed area are determined by the detailed surface topology rather than the root mean square roughness R RMS , which represents deviations from a flat surface. The suppression of the effective sputtering yields for rough surfaces compared to the yield for a smooth surface was accounted for by the change of the mean local incident ion angle (LIIA) (θ'). The mean surface inclination angle distribution (SIAD) (δ) was found to be a useful parameter to estimate the LIIA from the calculated IADs. We report global empirical formulas for the mean LIIA and fraction of the area shadowed from the main ions for D plasmas for rough surfaces with B-field incident angles α = 85°–89° as a function of the mean SIAD (δ). We propose the use of the mean LIIA (θ') to estimate the sputtering yield for rough surfaces from the angular dependence of the sputtering yield.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Remote Sensing-Informed Zonation for Understanding Snow, Plant and Soil Moisture Dynamics within a Mountain Ecosystem

In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Updates on MURAVES Project at Mt. Vesuvius

The MUon RAdiography of VESuvius (MURAVES) project aims to employ muography imaging techniques to investigate the internal structure of the summit of Mount Vesuvius, an active volcano located near Naples, Italy. This paper reports recent advancements in data analysis and simulation tools that significantly improve the quality and reliability of the experiment’s results. A new track selection method, referred to as the Golden Selection, has been developed to identify high-quality muon tracks by applying an improved χ 2 -based criterion. This method enhances the signal-to-background ratio and improves the resolution of the resulting muographic images. Moreover, the simulation framework has been upgraded through the integration of the MULDER (MUon simuLation for DEnsity Reconstruction) library, which consolidates the functionalities of previously used libraries into a single, unified platform. MULDER enables efficient and accurate modeling of muon flux variations induced by topographical features. A good agreement is observed between the simulated and measured muon flux maps, validating the effectiveness of the new analysis and simulation approaches.

Cosmic rays↗
DOE OSTI2022

Standoff Infrared Measurements of Chemical Plume Dynamics in Complex Terrain Using a Combination of Active Swept-ECQCL Laser Spectroscopy with Passive Hyperspectral Imaging

Chemical plume detection and modeling in complex terrain present numerous challenges. We present experimental results from outdoor releases of two chemical tracers (sulfur hexafluoride and Freon-152a) from different locations in mountainous terrain. Chemical plumes were detected using two standoff instruments collocated at a distance of 1.5 km from the plume releases. A passive long-wave infrared hyperspectral imaging system was used to show time- and space-resolved plume transport in regions near the source. An active infrared swept-wavelength external cavity quantum cascade laser system was used in a standoff configuration to measure quantitative chemical column densities with high time resolution and high sensitivity along a single measurement path. Both instruments provided chemical-specific detection of the plumes and provided complementary information over different temporal and spatial scales. The results show highly variable plume propagation dynamics near the release points, strongly dependent on the local topography and winds. Effects of plume stagnation, plume splitting, and plume mixing were all observed and are explained based on local topographic and wind conditions. Measured plume column densities at distances ~100 m from the release point show temporal fluctuations over ~1 s time scales and spatial variations over ~1 m length scales. The results highlight the need for high-speed and spatially resolved measurement techniques to provide validation data at the relevant spatial and temporal scales required for high-fidelity terrain-aware microscale plume propagation models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Sharp, high numerical aperture (NA), nanoimprinted bare pyramid probe for optical mapping

The ability to correlate optical hyperspectral mapping and high resolution topographic imaging is critically important to gain deep insight into the structure–function relationship of nanomaterial systems. Scanning near-field optical microscopy can achieve this goal, but at the cost of significant effort in probe fabrication and experimental expertise. To overcome these two limitations, we have developed a low-cost and high-throughput nanoimprinting technique to integrate a sharp pyramid structure on the end facet of a single-mode fiber that can be scanned with a simple tuning-fork technique. The nanoimprinted pyramid has two main features: (1) a large taper angle (~70°), which determines the far-field confinement at the tip, resulting in a spatial resolution of 275 nm, an effective numerical aperture of 1.06, and (2) a sharp apex with a radius of curvature of ~20 nm, which enables high resolution topographic imaging. Optical performance is demonstrated through evanescent field distribution mapping of a plasmonic nanogroove sample, followed by hyperspectral photoluminescence mapping of nanocrystals using a fiber-in-fiber-out light coupling mode. Through comparative photoluminescence mapping on 2D monolayers, we also show a threefold improvement in spatial resolution over chemically etched fibers. These results show that the bare nanoimprinted near-field probes provide simple access to spectromicroscopy correlated with high resolution topographic mapping and have the potential to advance reproducible fiber-tip-based scanning near-field microscopy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Nanofabrication of silicon surfaces for reduced virus adhesion

Nanofabrication is a remarkably effective technique to create desirable nanoscale patterns. Here, the effect of surface nanofabrication on altering virus adhesion to the substrates was examined. Arrays of nanoholes, 50 nm in diameter, 22 nm deep, and 100 nm in pitch distance, were created on silicon (Si) wafers by electron-beam lithography and reactive ion etching. MS2 coliphage, which is 26 ± 2 nm in diameter and is frequently used as a surrogate for human viruses, was applied to investigate the interaction between the virions and smooth or nanostructured Si surfaces. Scanning electron microscopy and atomic force microscopy along with surface wettability analyses revealed that the nanofabrication had the effect of reducing not only the number of viruses attached but also the strength of virus adhesion. These effects were ascribed to the presence of nanoholes, which were inaccessible to the virions due to the unique surface topographical parameters and the surface chemistry, resulting in the decrease of the overall solid contact area for MS2 attachment. The periodic spacing of the nanoholes also limited the unit landing area for MS2 particles, restricting the formation of MS2 aggregates and leading to the reduced amount of MS2 attachment. We anticipate that smart design of a surface’s chemical composition and nanostructure will offer a feasible solution to improve mitigations for controlling viral adhesion and transmission to and from food contact surfaces.

36 MATERIALS SCIENCE↗