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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Raw_data_Batch_I: Lisle to Waterfall Glen

Date of collection: May 11, 2023 Location: DuPage County, IL This dataset contains lidar and vision data collected between Lisle, IL, and the Waterfall Glen parking lot. The vehicle started near Cass School District 63, headed east along IL 34. The vehicle then turned south along IL 83 until Interstate 55. Finally, the vehicle turned southwest along I 55 until Exit 273A and headed toward the Waterfall Glen parking lot. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![lisle waterfall image](lisle-waterfall.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Randall Road

Date of collection: June 3, 2022 Location: Randall Road, DuPage County, IL Content: “North to South” “South to North” This data set contains lidar and vision data collected along Randall Road in DuPage County, Illinois. The “South to North” folder starts at 1480 N. Orchard Road, Aurora, IL 60506, headed north along Randall Road until 238 N. Randall Road, St. Charles, IL 60174. The “North to South” folder starts from 238 N. Randall Road, St. Charles, IL 60174, headed south along Randall Road until 1480 N. Orchard Road, Aurora, IL. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![randall road image](randall-road.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: State Street

Date of collection: May 18, 2023 Location: State Street, Chicago, IL This data set contains lidar and vision data collected along State Street. The vehicle started from outside of the McCormick Tribune Campus Center at the Illinois Institute of Technology’s Mies Campus and headed north along State Street, until the north end of State Street in the Gold Coast. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![state street image](state-street.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

An imaging and fractal approach towards understanding reservoir scale changes in coal due to bioconversion

Microbially enhanced coalbed methane (MECBM) aims to replicate the natural process of microbial methane generation in coal under in situ conditions. Considerable work has been reported over the years to optimize the associated microbial geo-chemistry. However, there is very little insight with regards to changes in the physical structure of coal due to bioconversion, and its impact on fluid flow properties. Here this paper presents the result of an image and fractal-based approach used to evaluate the changes in physical properties of coal. Samples of coal treated over 30, 60 and 120 days respectively were imaged before and after bioconversion. The results revealed that coal bioconversion resulted in swelling of the coal matrix. Cleats narrower than 5 µm reduced in its width post-bioconversion. A 77% drop in the permeability of coal is expected from the obtained results. Bioconversion also resulted in separation of thin layers of coal flakes from the coal surface, serving as a potential source of fines. Fractal analysis of the images revealed a decrease in the fractal dimension post bioconversion, which is in agreement with the previously reported studies, thus corroborating the variations in sorption-trends observed due to bioconversion. Additionally, the methodology to determine the fractal dimension via two-dimensional image processing was modified to improve its accuracy, and remove the dependency of the dimension to the scale of the obtained images. Bioconversion also resulted in formation of new pores/fractures. Shorter treatment duration resulted in nanometer-scale discontinuous pores, which do not contribute to Darcian flow. Longer treatment periods resulted in sub-micron wide continuous pores. Also, few larger fractures (>5 µm wide) saw an increase in its aperture post-treatment. This opens up new avenues, such as, utilizing artificially induced fracturing techniques to enhance biogenic methane production in future.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants

High-throughput plant phenotyping—the use of imaging and remote sensing to record plant growth dynamics—is becoming more widely used. The first step in this process is typically plant segmentation, which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants. However, preparing such training data is both time and labor intensive. To solve this problem, we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems. This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages. The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed. We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes. We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques. This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Geomechanical Framework for Secure CO 2 Storage in Fractured Reservoirs and Caprocks for Sedimentary Basins in theMidwest United States

This report presents final technical results for the project Geomechanical Framework for Secure CO 2 Storage in Fractured Reservoirs and Caprocks for Sedimentary Basins in the Midwest United States (DE-FE0023330). The project was a three-year effort consisting of seven technical tasks focused on defining geomechanical factors for CO 2 storage applications in deep saline rock formations in Ohio and the Midwest United States, because geomechancial issues have been identified as a significant risk factor for large-scale CO 2 storage applications. A basin-scale stress-strain analysis was completed to describe the geomechanical setting for rock formations of Ordovician-Cambrian age in Ohio and adjacent areas of the Midwest United States in relation to geologic CO 2 storage applications. The tectonic setting, stress orientation-magnitude, and geomechanical and petrophysical parameters for CO 2 storage zones and caprocks in the region were cataloged. Ten geophysical image logs were analyzed for natural fractures, borehole breakouts, and drilling-induced fractures. The logs indicated mostly less than 10 fractures per 100 vertical feet in the borehole, with mostly N65E principal stress orientation through the section. Geophysical image logs and other logs were obtained for three wells located near the sites where specific models were developed for geomechanical simulations: Arches site in Boone County, Kentucky; Northern Appalachian Basin site in Chautauqua County, New York; and E-Central Appalachian Basin site in Tuscarawas County, Ohio. For these three wells, 9,700 feet of image logs were processed and interpreted to provide a systematic review of the distribution within each well of natural fractures, wellbore breakouts, faults, and drilling induced fractures. There were many borehole breakouts and drilling-induced tensile fractures but few natural fractures. Concentrated fractures were present at the Rome-basal sandstone and basal sandstone-Precambrian contacts at the Arches and East-Central Appalachian Basin sites. Geophysical logs were utilized to develop local-scale geologic models by determining geomechanical and petrophysical parameters within the geologic formations. These data were ported to coupled fluid-flow and reservoir geomechanics multi-phase CO 2 injection simulations. The models were developed to emphasize the geomechanical layers within the CO 2 storage zones and caprocks. A series of simulations were completed for each site to evaluate whether commercial-scale CO 2 could be safely injected into each site, given site-specific geologic and geomechanical controls. This involved analyzing the simulation results for the integrity of the caprock, intermediate, and reservoir zones, as well quantifying the areal uplift at the surface. Simulation results were also examined to ensure that the stress-stress perturbations were isolated within the subsurface, and that there was only limited upward migration of the CO 2 . Simulations showed capacity to inject more than 10 million metric tons of CO 2 in a single well at the Arches and East Central Appalachian Basin sites without excessive geomechanical risks. Low-permeability rock layers at the Northern Appalachian Basin study area well resulted in very low CO 2 injection capacity. Fracture models developed for the sites suggests that the sites have sparse fracture network in the deeper Cambrian rocks. However, there were indicators in image logs of a moderate fracture matrix in the Rose Run Sandstone at the Northern Appalachian Basin site. Dual permeability fracture matrix simulations suggest the much higher injection rates may be feasible in the fractured interval. Guidance was developed for geomechanical site characterization in the areas of geophysical logging, rock core testing, well testing, and site monitoring. The guidance demonstrates that there is a suitable array of options for addressing geomechanical issues at CO 2 storage sites. Finally, a review of Marcellus and Utica-Point Pleasant shale gas wells and CO 2 storage intervals indicates that these items are vertically separated, except for the Oriskany sandstone and Marcellus wells in southwest Pennsylvania and northern West Virginia. Together, project results present a more realistic portrayal of geomechanical risk factors related to CO 2 storage for existing and future coal-fired power plants in Ohio.

58 GEOSCIENCES↗
DOE OSTI2023

Automatic detection of cataclysmic variables from SDSS images

Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.

Astronomy & Astrophysics↗
DOE OSTI2018

Correction of Fluorescent Images of Biological Tissues for the Effects of Light Absorption and Scattering

A method for correcting the influence of light attenuation processes in biological tissues on their fluorescent images is proposed. The transfer function that takes into account the radiation losses in the medium at the wavelengths of excitation and emission of fluorescence is calculated analytically, depending on the transport scattering index and the hemoglobin tissue index. The latter is determined on the basis of a color image of the tissue in reflected visible light. Verification of the developed method was carried out on the basis of computer simulation of fluorescent images of biological tissue by the Monte Carlo method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Proposed Analytical Methods for Determining Filter Media Properties

High Efficiency Particulate Air (HEPA) filters, commonly used in nuclear filtration applications, are an integral part of the waste management processes in nuclear plants. HEPA filters are 99.97% efficient filtration devices characterized by their high resistance to air flow, or pressure drop. From theoretical models, the initial pressure drop across a clean filter is proven to be a function of the filter media fiber diameters and porosity of the media. These filter properties are relatively difficult to obtain with traditional manual methods; therefore, there is a need to develop analytical methods to find the fiber diameter and porosity to determine the pressure drop. Typically, these values can be found by substituting a calculated representative value based upon measured values, such as equivalent fiber diameter based upon a clean pressure drop. However, these values may also be directly recorded and measured by incorporating Scanning Electron Microscope (SEM) image analysis and other measurement methods to analyze the filter media and determine its physical properties without the need for media testing. DiameterJ, an open source Java plug-in, used with ImageJ, can process images of filter media taken by an SEM to find statistical data such as mean fiber diameter and porosity. To produce the raw data, SEM images of two filter media type samples are taken and segmented in DiameterJ using the traditional and statistical region merging segmentation algorithms. Manual segmentation is necessary after the initial segmentation by the algorithms as the images tend to be too complex for the algorithms to output with the necessary accuracy. However, complications exist in the manual segmentation process as these methods can be time intensive and prone to the individual bias of the user. This in turn can skew the final mean fiber diameter result, and lead to either an over or under prediction of the pressure drop. It was also discovered that the porosity data produced by DiameterJ is inaccurate, as the SEM analyzes the three-dimensional filter media by projecting its geometry onto a plane and analyzing it as a two-dimensional binary image. Thus, the porosity is artificially inflated through the segmentation process, rendering this result incorrect. Alternatively, density determination, gravimetric analysis, and thickness testing of the filter media is collectively used to determine the filter fiber porosity. Together, the SEM image analysis and analytical lab methods produce results through direct measurements which allow for the prediction of the initial pressure drop from clean filter media without the need for prior media testing to collect pressure data. By improving upon this proposed analytical method in the future, there is potential to streamline the process of finding these filter properties into a more direct methodology for determining the pressure drop across HEPA filters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Predicting melt pool depth and grain length using multiple signatures from in-situ single camera two-wavelength imaging pyrometry for laser powder bed fusion

In laser powder bed fusion (LPBF), the in-situ process signatures are known to have a direct correlation with the microstructural properties of the solidified melt pool (MP). It is known that the MP cooling and heating rates, and laser processing parameters can critically determine the grain structure and thereby affect the part properties. The objective of this work is to study the feasibility of using in-process, high-speed imaging pyrometry for evaluating the solidified MP properties “below” the surface, such as depth and microstructural properties. To accomplish this, we employ an in-house single camera-based two-wavelength imaging pyrometry (STWIP) system for monitoring the printing of single-scan tracks with Inconel 718 on a commercial LPBF printer (EOS M290). Further, the lab designed STWIP system is a coaxial high-speed (>10,000 fps) imaging system capable of monitoring MP temperature, morphology, and intensity profiles. The temperature measurements from STWIP are emissivity independent. The STWIP measured MP signatures of the printed tracks are correlated with the ex-situ microscopy characterized MP depth and the average grain lengths. From the data analysis, using support vector machine (SVM)-based regression models, we found that the MP temperature signatures are crucial for an accurate prediction of MP depth and the grain length, thus validating the novelty and necessity of the developed in-situ monitoring methods and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Personal Journey in Nanoscience via Developing and Applying Liquid Phase TEM

Liquid phase TEM has attracted widespread attention in recent years as a groundbreaking tool to address various fundamental problems in nanoscience. It has provided the opportunity to reveal many unseen dynamic phenomena of nanoscale materials in solution processes by direct imaging through liquids with high spatial and temporal resolution. After my earlier work on real-time imaging of the nucleation, growth, and dynamic motion of nanoparticles in liquids by developing high-resolution liquid phase transmission electron microscopy (TEM) down to the sub-nanometer level, I established my own research group at Lawrence Berkeley National Lab in 2010. My group focuses on developing and applying liquid phase TEM to investigate complex systems and reactions. We have studied a set of scientific problems centered on understanding how atomic level heterogeneity and fluctuations at solid-liquid interfaces impact nanoscale materials transformations using advanced liquid phase TEM. This article describes my personal journey in nanoscience, highlighting the main discoveries of my research group using liquid phase TEM as a unique tool. Some perspectives on the impacts of liquid phase TEM and the future opportunities in nanoscience and nanotechnology enabled by liquid phase TEM are also included.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances by Discovery in Radiation Detection

A National Institutes of Health (NIH) and U.S. Department of Energy (DOE) Office of Science virtual workshop on shared general topics was held in July of 2021 and reported on in this publication in January of 2023. Following the inaugural 2021 joint meeting representatives from the DOE Office of Science and NIH met to discuss organizing a second joint workshop that would concentrate on radiation detection to bring together teams from both agencies and their grantee populations to stimulate collaboration and efficiency. To meet this scientific mission within the NIH and DOE radiation detection space, the organizers assembled workshop sessions covering the state–of–the–art in cameras, detectors, and sensors for radiation external and internal (diagnostic and therapeutic) to human, data acquisition and electronics, image reconstruction and processing, and the application of artificial intelligence. NIH and DOE are committed to continuing the process of convening a joint workshop every 12–24 months. This Special Report recaps the findings of this second workshop. Beyond showing only the innovations and areas of success, important gaps in our knowledge were defined and presented. Finally, we summarize by defining four areas of greatest opportunity and need that emerged from the unique, dynamic dialogue the in–person workshop provided the attendees.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Comparison of automated chemical-guided segmentation and human annotation of soil organic matter in X-ray microcomputed tomography imaging in contrasted soil types

Soil organic matter (OM) formation and persistence is strongly influenced by the spatial distribution of organic substrates and microscale soil heterogeneity by dictating OM accessibility to microorganisms. However, traditional size and/or density fractionation techniques disrupt aggregate architecture, eliminating spatial information needed to fully understand intra-aggregate OM distribution. To quantify three-dimensional OM spatial distribution and automate segmentation in X-ray microcomputed tomography (µCT) imaging without human annotation bias, we developed an iodine gas vapor (I2) based staining workflow that eliminates labor-intensive manual annotation while maintaining segmentation accuracy, using aggregates from four taxonomically diverse soils (Xerofluvent, Haploxeroll Sphagnofibrist, Palehumult) with an 8-fold range of soil organic carbon. Human annotation of 10 µCT slices by the experienced and inexperienced annotators resulted in variations up to 3% in the Dice similarity coefficient (DSC), reflecting a degree of inherent subjectivity of manual labeling. Such inconsistencies are expected to compound as the number of manually annotated slices increases. Dual-energy µCT imaging at 33.1 keV (below the iodine (I) K-edge) and 33.2 keV (above the I K-edge) was used to resolve aggregate microstructure following I2 staining. The automated image subtraction pipeline identified OM regions by the I Kedge induced brightness increases, achieving DSC values of 0.58–0.83 relative to an experienced annotator. Sensitivity analyses revealed that the reconstruction alpha value—optimized via the open-source tool TomocuPy—and the 3D registration slice count were the primary determinants of accuracy, providing a novel benchmark for dual-energy soil imaging. The pipeline without GPU acceleration achieved 9.6 to 43.2 times faster than manual annotation. Using GPU-accelerated image post-processing and affine transformation matrices, the pipeline successfully segmented OM elements for large-scale datasets (3232×3232 pixel, 2048 slices) within ~5200 s from raw file acquisition to segmented output. The high-throughput approach enables the quantification of OM spatial distribution across diverse and heterogeneous soil.

Soil microbial biomass↗
DOE OSTI2025

Corrosion susceptibility and chromium loss in Austenitic steels and Nickel-based alloys in molten FLiNaK at 700 °C

A comparative study was conducted to evaluate the corrosion susceptibility of 316L, 316H, Ni 200, Inconel 625, and Hastelloy N in molten FLiNaK at 700 °C for 100 h. Top-view and cross-sectional scanning electron microscopy (SEM) imaging, combined with energy-dispersive X-ray spectroscopy (EDS) mapping, was performed to investigate microstructural and compositional changes. SEM images were further processed by introducing a contrast threshold to map cavity distribution. Using EDS mapping, intergranular and intragranular Cr loss were separately characterized. The ranking of mass loss after corrosion, from highest to lowest, is as follows: 316H > 316L > Inconel 625 > Hastelloy N > Ni 200. Cr loss was found to be correlated with Mo concentration, in agreement with many previous studies, with higher Mo content resulting in reduced Cr loss. On the other hand, there is no evidence that the high carbon content in 316H enhances corrosion resistance. The relatively low solubility of carbon in austenite at the testing temperature limits the amount of dissolved carbon. Therefore, the carbon-retarded vacancy diffusion, and consequently the reduced Cr diffusion, were not observed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

The Geothermal Artificial Intelligence for geothermal exploration

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). Here, we processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal / non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

In situ coherent diffractive imaging

Coherent diffractive imaging (CDI) has been widely applied in the physical and biological sciences using synchrotron radiation, X-ray free-electron laser, high harmonic generation, electrons, and optical lasers. One of CDI’s important applications is to probe dynamic phenomena with high spatiotemporal resolution. Here, we report the development of a general in situ CDI method for real-time imaging of dynamic processes in solution. By introducing a time-invariant overlapping region as real-space constraint, we simultaneously reconstructed a time series of complex exit wave of dynamic processes with robust and fast convergence. We validated this method using optical laser experiments and numerical simulations with coherent X-rays. Our numerical simulations further indicated that in situ CDI can potentially reduce radiation dose by more than an order of magnitude relative to conventional CDI. With further development, we envision in situ CDI could be applied to probe a range of dynamic phenomena in the future.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Magnetic domain engineering in SrRuO3 thin films

Abstract Magnetic domain engineering in ferromagnetic thin films is a very important route toward the rational design of spintronics and memory devices. Although the magnetic domain formation has been extensively studied, artificial control of magnetic domain remains challenging. Here, we present the control of magnetic domain formation in paradigmatic SrRuO 3 /SrTiO 3 heterostructures via structural domain engineering. The formation of structural twin domains in SrRuO 3 films can be well controlled by breaking the SrTiO 3 substrate symmetry through engineering miscut direction. The combination of x-ray diffraction analysis of structural twin domains and magnetic imaging of reversal process demonstrates a one-to-one correspondence between structural domains and magnetic domains, which results in multi-step magnetization switching and anomalous Hall effect in films with twin domains. Our work sheds light on the control of the magnetic domain formation via structural domain engineering, which will pave a path toward desired properties and devices applications.

Materials Science↗