Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “environmental testing”

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.

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

At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

CO 2 Mineralization Using Porous Carbon and Industrial Wastes to Make Multifunctional Concrete

Along with substantial benefits and promises of carbon capture and storage (CCS) technologies, significant challenges exist in developing scalable materials and methods for capture and of CO 2 . The overarching objective of this project is to provide a system approach for developing a new CO 2 capture and utilization technology using porous carbon and industrial wastes to make a low-cost, scalable and multifunctional concrete product. The objective of Phase I of this project is to develop and fine-tune activated carbons from different wastes such as plastic wastes to offer a cheap, abundant and scalable feedstock for CO 2 uptake. The objective of Phase 2 is creating a facile protocol to develop a concrete prototype comprising CO 2 adsorbed activated carbon (e.g. from plastic wastes), followed by product validation, life-cycle analysis, and bench-scale testing. The project objectives are designed to specifically obtain a final product and technology that directly addresses the main goals of the DOE’s CCS programs.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Integrating Machine Learning into a Methodology for Early Detection of Wellbore Failure [Slides]

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integrating Machine Learning into a Methodology for Early Detection of Wellbore Failure [Slides]

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Appendix Q: Recommendations for Developing Molecular Assays for Microbial Pathogen Detection Using Modern In Silico Approaches

We describe the use of in silico approaches to improve the process of molecular assay development and reduce time and cost by utilizing available databases of whole genome pathogen sequences combined with modern bioinformatics and physical modeling tools. Well-characterized assays are needed for accurately detecting pathogens in environmental and patient samples and also for evaluation of the efficacy of a medical countermeasure that may be administered to patients. The polymerase chain reaction (PCR) remains the gold standard for pathogen detection due to the simplicity of its instrumentation, low cost of reagents, and outstanding limit of detection (LOD), sensitivity, and specificity. However, creation of such PCR assays often involves iterations of design, preliminary testing, and thorough validation with clinical isolates and testing in relevant matrices, which can be time consuming, costly, and result in suboptimal assays. Since formal validation (e.g., for Emergency Use Authorization [EUA] or Food and Drug Administration [FDA] licensure) of an infectious disease assay can be very expensive and can require extensive time of development, having a well-designed assay up front is a critical first step. Yet, many assays described in the literature utilized limited design capabilities and many initially promising assays fail the validation process, resulting in increased costs and timelines for successful product development. While the computational approaches outlined in this document by no means obviate the need for wet lab testing, they can reduce the amount of effort wasted on empirical optimization and iterative redesigns and also guide validation studies. The proposed computational approaches also result in higher performing assays with better sensitivity, specificity, and lower LOD and reduce the possibility of assay failure due to signature erosion. To provide clarity, an extensive glossary of defined terms is provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deep Neural Network Assisted Distributed Strain and Temperature Fiber Sensor System for Natural Gas Pipeline Monitoring

Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This article presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDAs) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/ με and 0.9 MHz/°C for peak 1; and 47.2 kHz/ με , and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber (DBPF) on a 1-in steel pipe under pressure up to 1000 per square inch (psi) at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network (PDNN) framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-in outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28 με /psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.

03 NATURAL GAS↗
DOE OSTI2022

2020 Hydrogen and Fuel Cell Technologies Market Report

This report features summaries of relevant hydrogen and fuel cell activity in 2020, consistent with prior annual reports across applications and sectors. However, 2020 was no ordinary year. Despite pandemic hardships, the hydrogen and fuel cell industry not only stepped up to assist with humanitarian and healthcare efforts but saw continued momentum in several growing market sectors with numerous partnerships, initiatives, and investments. Using hydrogen and fuel cell technologies to provide an environmental and economic pathway to clean, reliable power was a key feature of world economic recovery and energy infrastructure plans. Today, hydrogen energy and fuel cell systems are being developed and deployed in a range of stationary power, portable power, and transportation applications around the world. Throughout 2020, there were significant achievements in each sector, building on years of research and development (R&D), field testing, large-scale demonstration projects, and real-world customer experience. The heavy-duty truck market in particular continued to make substantial gains, with new players entering the space and a coalition of companies pledging to produce and deploy more than 100,000 fuel cell trucks in the next two decades. To provide hydrogen for trucks and other end users, larger and larger electrolyzer projects were announced throughout the year. Collectively, there were more than 430 megawatts (MW) of electrolyzer systems either deployed, ordered, or proposed around the world. This 2020 report also provides special “Spotlights” on the collective industry response to the COVID-19 pandemic as well as international commitments and hydrogen strategies, providing overviews of significant activity during and after the initial onset of COVID-19 around the world.

08 HYDROGEN↗
DOE OSTI2020

Mapping of Benthic Habitats at Marine Renewable Energy Sites Using Multibeam Echosounder and Sediment Profile Imaging Technologies

Objectives/Scope: The goal of this work was to develop a consistent and semi-automated seafloor survey method for generating high-resolution, benthic habitat maps for environmental assessments and monitoring of marine renewable energy sites. Sediment profile and plan view imaging (SPI/PV) technology was combined with multibeam bathymetry and acoustic backscatter methods to demonstrate a rapid, cost-effective benthic mapping protocol. A key technical innovation of this project was the development of an image processing platform that automatically measures key features from the images. Methods: Multibeam echosounder (MBES) acoustic and SPI/PV surveys were conducted at three coastal areas off the U.S. west coast, including the PacWave South energy test site off of Newport, Oregon. Point data on physical and biological sediment conditions obtained from the SPI/PV imagery were used to efficiently ground-truth the high-resolution MBES bathymetry and backscatter mosaics. The SPI camera is an optical corer that obtains an undisturbed 21 by 15 cm, high-resolution, cross-sectional image of the sediment–water interface and upper sediment column. The plan view camera attached to the SPI camera frame captures a downward looking view of the seabed immediately before the SPI image is obtained. As part of this project, we developed a computer vision image processing platform (iSPI) that uses deep convolutional neural networks and other approaches to automatically identify and measure key features in the images, such as grain size and surface relief. Results: Detailed benthic habitat maps were generated using this mapping approach for three different marine settings. The sites mapped included a silt-dominated embayment; a sloping, nearshore, transitional very fine to coarse sand bottom; and a medium, sand-dominated continental shelf marine energy test site. In each case, acoustic and imaging surveys were completed in less than week, and detailed benthic habitat maps were generated within 60 days. The results were placed within the Coastal and Marine Ecological Classification Standard (CMECS) habitat mapping framework, and various combinations of the bathymetry, backscatter, and SPI and PV data were used to generate CMECS component maps. Novel Information: This project developed and demonstrated a repeatable and cost-effective approach for efficiently mapping benthic habitat conditions over broad areas of the seafloor by combining state-of-the-art acoustic and imaging techniques. The primary survey tools used in this project have been used previously in both the offshore renewable (wind) and oil and gas sectors to map and monitor benthic environments. This project’s innovations include 1) the focused use of high-resolution SPI/PV imagery to ground-truth acoustic mosaics, and 2) the development of a computer automated image analysis processing tool that both streamlines and standardizes the generation of data from the imagery and makes the data extraction process more cost-effective and repeatable.

13 HYDRO ENERGY↗
DOE OSTI2023

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Roughrider Carbon Storage Hub (Final Report)

The Roughrider Carbon Storage Hub was a 2-year project (October 2023 – September 2025) conducted by the Energy & Environmental Research Center (EERC) focused on advancing the feasibility of a commercial-scale carbon dioxide (CO 2 ) geologic storage hub in McKenzie County, North Dakota. The project’s objective was to investigate the potential that stacked storage complexes (multiple deep saline formations) can safely and economically store at least 50 million tonnes of CO 2 within 30 years. The captured CO 2 would be sourced from industrial emitters including project partner ONEOK, Inc.’s gas-processing plants and a planned gas-to-liquids facility. Drilling of the Roughrider 1 stratigraphic test well (14,979-ft total depth) was completed in November 2024. The wellbore intersected four candidate storage formations: Inyan Kara, Broom Creek, Mission Canyon, and Black Island–Deadwood. Operational challenges, including a stuck drill string, were resolved without long-term impact. A comprehensive logging and coring program was conducted, followed by successful well abandonment and site reclamation. Over 660 ft of 4-in. whole core was retrieved. Core plug samples were processed and analyzed for petrophysical and geochemical properties. Results confirmed promising porosity and permeability in the Inyan Kara and Broom Creek Formations and removal of the Mission Canyon and Black Island–Deadwood horizons from further investigation. Data derived from the logging and coring program were used to improve initial geologic models built from legacy data. CO 2 injection simulations showed that the Inyan Kara alone can feasibly store the target mass of CO 2 . Because of subtle differences in geologic structure and porosity trends between the formations, a stacked storage scenario using the Broom Creek and Inyan Kara Formations resulted in a larger overall plume area than using the Inyan Kara alone. Preliminary CO 2 pipeline routes from the industrial sources were mapped utilizing existing rights of way and evaluated for capacity and cost using U.S. Department of Energy Office of Fossil Energy and Carbon Management/National Energy Technology Laboratory models and U.S. Environmental Protection Agency emissions data. Integrating capture, transport, and storage cost estimates with policy incentives (e.g., 45Q credits) provided a total cost-per-ton analysis. Results indicate that the small scale of the volumes to be transported over the cumulative large distances does not support the project’s financial viability. However, the groundwork laid during this project from geological, regulatory, and social perspectives positions the Roughrider hub site as a promising candidate for commercial carbon storage in North Dakota, especially if the economy of scale is introduced for CO 2 transportation to the hub site.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Efficient Mesofluidic Separation of Large Particles in nuclear slurries - 20408

Efficient and effective particle separation is essential to cleanup of many nuclear wastes. For example, particle separation may be used to accelerate settle-decant bottlenecks that limit the throughput necessary to achieve the U.S. Department of Energy waste cleanup mission in a timely manner. Particle separation may be used to protect downstream processing equipment from pulses of solids that may be encountered near (within a few feet of) liquid-solid interfaces during waste transfer operations, thereby preventing work stoppages. Effective particle separation may permit efficient sludge washing. These are only a few of the ways in which particle separation is important. Yet, efficient and effective particle separation in nuclear processing environments remains challenging. For example, settle-decant operations permit larger and heavier particles to settle, leaving smaller, lighter particles suspended. However, settle-decant operations are slow, convection currents may resuspend solids, hindered settling of multicomponent slurries remains incompletely understood, and predictive models for settling in graduated cylinders fail to match limited observations of settling in large waste tanks. Additionally, pumping operations can cause turbulent resuspension of particles when the pump intake is close to the settled solids layer. Filtration techniques that use membranes or partially permeable barriers retain larger particles, permitting smaller particles to permeate. However, filters, including dead-end filtration, are prone to clogging and caking, operate at elevated pressures due to minimal void volume, and increase in pressure during operations, which require more control systems. Therefore, the need for high throughput particle separation techniques that operate with modest pressure drops persists. A novel mesofluidic separator presents the opportunity to effectively and efficiently accelerate the waste cleanup mission. The separator separates large particles from process streams across a broad range of particle sizes and has no moving parts or media to replace, regenerate, or clean. This separator design has an unusually large void volume, permitting operation at much higher flow rates (and lower pressures) than traditional filtration (e.g., dead-end filtration). Industrial-scale flow rates have been demonstrated. In performance testing, mesofluidic separators operate at might flow rates (>90 gpm (0.0006 m{sup 3}/s) in piping 3 inches (0.08 m) in diameter; Re>10{sup 5}), with modest pressure drop (∼25 psi (170 KPa) in testing). In complex, aggregating waste simulants, the separator loses <25% of flow projected over months without back pulsing or chemical cleaning. Mesofluidic separation presents infrastructure advantages, reduces risk, and provides mission impact. Infrastructure advantages include implementation within existing transfer systems, plug and play without facility modifications to safety systems, and operation at low pressures. The separator is flexible in location, may be positioned within or outside of waste tanks, and may replace or augment dead-end and cross-flow filters. Mesofluidic separation reduces risk by minimizing waste (no media to replace or regenerate and no cleaning chemicals to handle and dispose) and by minimizing filter change-out consistent with as low as reasonably achievable (ALARA) exposure to workers. The potential mission impact of these separators is substantial. The separator has potential to support, simplify, and accelerate in-farm transfers and waste feed delivery. Furthermore, the separator can free up settling and holding tanks in the Direct Feed Low-Activity Waste (DFLAW) mission by reducing or eliminating post-transfer settling and wait times with the potential to free up 1 Mgal (4000 m{sup 3}) of double-shell tank space. This paper quantitatively compares mesofluidic separation to dead-end filtration, discusses scale-up results, and considers the separator's potential to efficiently and effectively reduce the long-term environmental ability of particulate-rich nuclear wastes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Pragmatic protocols for working cleanly when measuring ice nucleating particles

We report that measuring ice nucleating particles (INPs) is critical for understanding, and modeling, cloud formation, reflectivity, and precipitation patterns. However, because INPs are very rare in the atmosphere, but abundant—sometimes alarmingly so—in the dust that covers all work surfaces, contamination during processing of collected aerosol samples can be a significant hindrance to obtaining accurate measures of INPs. In preparing this technical note, we questioned the cleanliness of every collection and processing step involved in making immersion freezing measurements of INPs re-suspended from filter samples. The aim was to identify, and then minimize, all potential sources of significant contamination, including containers and tools used to store filters and prepare liquid suspensions, gloves, work surfaces, and the polymerase chain reaction (PCR) trays commonly used for sample analysis. While plasticware released few INPs, most gloves readily shed them, but this can be mitigated by using washed cleanroom vinyl or polyethylene gloves. Work surfaces, even those that had been cleaned, were prodigious reservoirs of contaminating INPs, but simply covering them with aluminum foil will provide an INP-free surface. By applying these practices, we developed a method to reduce the background level of INPs on Nuclepore™ polycarbonate filters in our tests to 0 at -25 °C and < 20 at -27 °C, making them suitable for use in all sampling environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Particle size influences decay rates of environmental DNA in aquatic systems

Abstract Environmental DNA (eDNA) analysis is a powerful tool for remote detection of target organisms. However, obtaining quantitative and longitudinal information from eDNA data is challenging, requiring a deep understanding of eDNA ecology. Notably, if the various size components of eDNA decay at different rates, and we can separate them within a sample, their changing proportions could be used to obtain longitudinal dynamics information on targets. To test this possibility, we conducted an aquatic mesocosm experiment in which we separated fish‐derived eDNA components using sequential filtration to evaluate the decay rate and changing proportion of various eDNA particle sizes over time. We then fit four alternative mathematical decay models to the data, building towards a predictive framework to interpret eDNA data from various particle sizes. We found that medium‐sized particles (1–10 μm) decayed more slowly than other size classes (i.e., <1 and > 10 μm), and thus made up an increasing proportion of eDNA particles over time. We also observed distinct eDNA particle size distribution (PSD) between our Common carp and Rainbow trout samples, suggesting that target‐specific assays are required to determine starting eDNA PSDs. Additionally, we found evidence that different sizes of eDNA particles do not decay independently, with particle size conversion replenishing smaller particles over time. Nonetheless, a parsimonious mathematical model where particle sizes decay independently best explained the data. Given these results, we suggest a framework to discern target distance and abundance with eDNA data by applying sequential filtration, which theoretically has both metabarcoding and single‐target applications.

Brandão‐Dias, Pedro F. P.↗
DOE OSTI2020

Geologic Characterization for CO 2 Storage with Enhanced Oil Recovery in Northern Michigan

The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership initiative. Since its founding, MRCSP has made significant strides toward making CCUS a viable option for states in the region. The public/private consortium, funded through the DOE Regional Carbon Sequestration Initiative, brings together nearly 40 industry partners and 10 states. Battelle, as the project lead, oversees research, development and operations and coordinates activities among the partners. The incremental, phased approach has built a valuable knowledge base for the industry and paved the way for commercial-scale adoption of CCUS technologies. From 2008 to 2020, MRCSP Phase III focused on the development of large-scale injection projects. This report is part of a series of reports prepared under the Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase). These reports summarize and detail the findings of the work conducted under the Phase III project. This report compiles the results of geologic characterization of late-stage reefs, active EOR reefs, and new EOR reefs in the Northern Niagaran Pinnacle Reef Trend (NNPRT) in Michigan to demonstrate developed methodologies, geologic variability, and reservoir potential for CO 2 storage. Conducting CO 2 injection and monitoring in these settings provide significant insights for addressing the project objectives: assessment of injectivity in complex carbonate reef deposits; assessing CO 2 trapping mechanisms in a closed reservoir system; assessing any final oil recovery from transitional or residual oil zones in a field where CO 2 flooding is nearing completion; evaluating the transition from EOR to storage of CO 2 ; assessing regional commercialization; assessing new technologies for tracking CO 2 , brine, and oil movements underground; and monitoring options in a closed reservoir with oil, residual oil, and water zones.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Transformational Sorbent Materials for a Substantial Reduction in the Energy Requirement for Direct Air Capture of CO 2

InnoSepra’s project, “Transformational Sorbent Materials for a Substantial Reduction in the Energy Requirement for Direct Air Capture of CO 2 ,” utilized computational tools, materials characterization, and lab scale testing to optimize previously identified materials to determine their performance under direct air capture conditions. InnoSepra utilized the test results to determine the energy required for regeneration and to develop a high-level process design/analysis to demonstrate the application of developed materials for direct air capture which could be utilized for future techno-economic and life-cycle analyses to fully assess the potential of the materials for direct air capture. InnoSepra also updated the State Point Data Table and completed the environmental, health, and safety (EH&S) Risk Assessment.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Portable Waveguide-Based Optical Biosensor

Rapid, on-site diagnostics allow for timely intervention and response for warfighter support, environmental monitoring, and global health needs. Portable optical biosensors are being widely pursued as a means of achieving fieldable biosensing due to the potential speed and accuracy of optical detection. We recently developed the portable engineered analytic sensor with automated sampling (PEGASUS) with the goal of developing a fieldable, generalizable biosensing platform. Here, we detail the development of PEGASUS’s sensing hardware and use a test-bed system of identical sensing hardware and software to demonstrate detection of a fluorescent conjugate at 1 nM through biotin-streptavidin chemistry.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

54 ENVIRONMENTAL SCIENCES↗