Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SAMPLING DEVICE”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Citywide indoor air sampling mirrors wastewater and clinical for environmental surveillance of respiratory viruses

Wastewater surveillance of respiratory pathogens can provide timely estimates of viral activity and disease trends in a population. Indoor air surveillance could be used similarly with some advantages but remains largely unvalidated at the community -scale. Here, an indoor air surveillance program was employed as part of public health environmental surveillance in Chicago, Illinois, USA. Ten air samplers were placed in healthcare and congregate living settings across the city. Weekly air samples were evaluated for influenza A, influenza B, respiratory syncytial virus, and SARS -CoV-2 over two respiratory virus seasons (2023 -2025). Citywide, aggregated air sample positivity and viral load were closely correlated with local clinical case and wastewater surveillance data across all respiratory viruses. Virus trends in air data often preceded clinical and wastewater, although this varied across pathogens and respiratory virus seasons. Further, whole -genome sequencing of SARS -CoV-2 showed close correlation of variant proportions across all datasets. At the building -scale, air samples obtained from a single sampling device provided efficient respiratory virus surveillance, with respiratory pathogen levels mirroring citywide clinical surveillance data. These data demonstrate that air surveillance can provide respiratory virus case and variant trend data at a building or community -scale, serving as an alternative or complementary tool for public health environmental surveillance.

Wilton, Rosemarie↗
DOE OSTI2022

The ultrafast pixel array camera system and its applications in high energy density physics

Diagnostics in high energy density physics, shock physics, and related fields are primarily driven by a need to record rapidly time-evolving signals in single-shot events. These measurements are often limited by channel count and signal degradation issues on cable links between the detector and digitizer. Here we present the Ultrafast Pixel Array Camera (UPAC), a compact and flexible detector readout system with 32 waveform-recording channels at up to 10 Gsample/s and 1.8 GHz analog bandwidth. The compact footprint allows the UPAC to be directly embedded in the detector environment. A key enabling technology is the PSEC4A chip, an eight-channel switch-capacitor array sampling device with up to 1056 samples/channel. The UPAC system includes a high-density input connector that can plug directly into an application-specific detector board, programmable control, and serial readout, with less than 5 W of power consumption in full operation. We present the UPAC design and characterization, including a measured timing resolution of ~20 ps or better on acquisitions of sub-nanosecond pulses with minimal system calibrations. Example applications of the UPAC are also shown to demonstrate operation of a solid-state streak camera, an ultrafast imaging array, and a neutron time-of-flight spectrometer.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Canary

CANARY is a wearable environmental sampling device that is small, lightweight, and low-power. CANARY integrates a novel mechanism that uses magnets rather than powered pumps to mimic human breathing. In its current form, CANARY breathes, but sensor integration can easily turn this into a standalone device for use in airborne detection of chemicals, biologicals, viruses, etc. CANARY has a small footprint and requires little power to operate enabling it to be used in various environments. Los Alamos researcher have developed a working prototype of the air intake system and are seeking licensees for the current air intake, validation outside the lab, or further development to integrate biosensors as desired.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Scalable Thin Light-Emitting Diode (LED) Light Sheet Platform

The goal of the Scalable Thin Light-Emitting Diode (LED) Light Sheet Platform project is to derisk the manufacturing scalability for a high chip count heterogeneous smart lighting platform, with the potential to enable significant energy savings through dynamic directional light control and custom. Specifically, the project derisks the scalability of a disruptive new computer controlled microassembly fabrication process SRI International is developing, to address fundamental cost barriers to mass production of high chip count systems. This is key for enabling mass adoption and thus maximum societal energy savings impact. The LED light sheet technology is a smart illumination platform that can be applied to lighting, signage, and display. It has features and a form factor similar to bendable or conformal OLED light sheets but uses more efficient LEDs with a remote phosphor layer that is very close to the LED to deliver a luminous efficacy that exceeds 125 lumen/Watt and enable > 50% Lighting Application Efficiency (LAE) energy savings. During Budget Period 1 (BP1), all of the BP1 milestones were successfully completed: (M2.0.1) Design demonstrator details that will meet the final project goals, (M6.1.1) Show lighting output model can predict illuminance, spatial, spectral distribution for specific light-sheet designs and use case, (M3.2.1) Automated loading supports 200 chip arrays, and (M3.3.1) Finish first interconnect process run for 200 chip array. The BP1 Go/No-Go Decision Point (G/NG 1) 200 chip demonstrator sample was in the process of final assembly and test at the end of BP1 on December 31, 2023. The objective was successfully achieved on January 16, 2024, when a wired 200-chip sample underwent confirmation testing that demonstrated a 93.75% first pass electrical yield that successfully exceeded the 75% first-pass electrical yield BP1 G/NG requirement. In addition, all 93.75% of the sample devices lit up, further demonstrating the ability to assemble and transfer small LED chips without damaging their functionality.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Subsurface Flow Across Polygonal Tundra Measured by Bromide Tracer, Utqiagvik (Barrow), Alaska, 2015-2016

A bromide tracer study with the primary focus on hydrology in the active layer of polygonal tundra in the Barrow Environmental Observatory in Utqiagvik (Barrow), Alaska from July 2015 to September 2016. Sampling devices placed at various depths in the polygon centers, rims, and troughs were used to monitor tracer concentrations during thaw seasons. The tracer was continually monitored simultaneously on a low- and high-centered polygon throughout thaw seasons. Included is one data file of the bromide tracer concentrations. Also included are measurements taken throughout the experiment - one data file each of frost table depth and precipitation; and twelve water table elevation data files. Water level data, meteorological data, and thaw depth measurements were taken throughout the experiment. Our approach permits a comparison of hydrological behaviors in low- and high-centered polygons over the same time period and meteorological conditions.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Collab Fracture Characterization: Preliminary Results from the Modeling and Flow Testing of Experiment 1

The EGS Collab project is developing ~10-20 m-scale field sites where fracture stimulation and flow models can be validated against controlled, small-scale, in-situ experiments. At the first experimental site, a hydraulic fracture will be created in metamorphic rock at the 4850 level in the former Homestake Mine. The experimental concept is for fracturing to be initiated at an injection well and continue until it intersects a production well approximately 10 meters away. Initial step-pressure flow testing of the fracture are to be performed to describe pressure-aperture-flow relationships. The flow field between the injector and producer boreholes will be characterized using a series of forced gradient dipole tracer tests. A number of fracture characterization models have been used to assist in the design of the tracer tests. The tracer test program will use conservative, sorbing, and particulate tracers, as well as sampling for radon gas to describe the fracture surface area (i.e., potential heat transfer area), flow pathway distribution, and volume. Tracer concentration at the production well (i.e. break through curves) will be measured using a series of inline sensors, liquid sampling devices, filtering of the effluent, and the measuring of gas concentrations in the effluent. Tracer monitoring data will be compared against results from a collection of numerical simulators developed at U.S. national laboratories and universities for the purpose of validating these flow and transport models. Ultimately these same models will be used to predict the heat transfer characteristics of the fracture and will be compared to a thermal characterization field test where cold water will be injected at the injection well and the temperature will be measured at the production well. This paper discusses the early tracer transport model predictions and parameters sensitivity that affect the tracer breakthrough curves at the production well.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Collab Fracture Characterization: Preliminary Results from the Modeling and Flow Testing of Experiment 1

The EGS Collab project is developing ~10-20 m-scale field sites where fracture stimulation and flow models can be validated against controlled, small-scale, in-situ experiments. At the first experimental site, a hydraulic fracture will be created in metamorphic rock at the 4850 level in the former Homestake Mine. The experimental concept is for fracturing to be initiated at an injection well and continue until it intersects a production well approximately 10 meters away. Initial step-pressure flow testing of the fracture are to be performed to describe pressure-aperture-flow relationships. The flow field between the injector and producer boreholes will be characterized using a series of forced gradient dipole tracer tests. A number of fracture characterization models have been used to assist in the design of the tracer tests. The tracer test program will use conservative, sorbing, and particulate tracers, as well as sampling for radon gas to describe the fracture surface area (i.e., potential heat transfer area), flow pathway distribution, and volume. Tracer concentration at the production well (i.e. break through curves) will be measured using a series of inline sensors, liquid sampling devices, filtering of the effluent, and the measuring of gas concentrations in the effluent. Tracer monitoring data will be compared against results from a collection of numerical simulators developed at U.S. national laboratories and universities for the purpose of validating these flow and transport models. Ultimately these same models will be used to predict the heat transfer characteristics of the fracture and will be compared to a thermal characterization field test where cold water will be injected at the injection well and the temperature will be measured at the production well. This paper discusses the early tracer transport model predictions and parameters sensitivity that affect the tracer breakthrough curves at the production well.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Collab Fracture Characterization: Preliminary Results from the Modeling and Flow Testing of Experiment 1

The EGS Collab project is developing ~10-20 m-scale field sites where fracture stimulation and flow models can be validated against controlled, small-scale, in-situ experiments. At the first experimental site, a hydraulic fracture will be created in metamorphic rock at the 4850 level in the former Homestake Mine. The experimental concept is for fracturing to be initiated at an injection well and continue until it intersects a production well approximately 10 meters away. Initial step-pressure flow testing of the fracture are to be performed to describe pressure-aperture-flow relationships. The flow field between the injector and producer boreholes will be characterized using a series of forced gradient dipole tracer tests. A number of fracture characterization models have been used to assist in the design of the tracer tests. The tracer test program will use conservative, sorbing, and particulate tracers, as well as sampling for radon gas to describe the fracture surface area (i.e., potential heat transfer area), flow pathway distribution, and volume. Tracer concentration at the production well (i.e. break through curves) will be measured using a series of inline sensors, liquid sampling devices, filtering of the effluent, and the measuring of gas concentrations in the effluent. Tracer monitoring data will be compared against results from a collection of numerical simulators developed at U.S. national laboratories and universities for the purpose of validating these flow and transport models. Ultimately these same models will be used to predict the heat transfer characteristics of the fracture and will be compared to a thermal characterization field test where cold water will be injected at the injection well and the temperature will be measured at the production well. This paper discusses the early tracer transport model predictions and parameters sensitivity that affect the tracer breakthrough curves at the production well.

Enhanced Geothermal Systems, EGS Collab, Sanford U↗
DOE OSTI2019

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

Morrow, Mitchell Hedges↗
DOE OSTI2021

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

Tohlen, Michael Aaron↗
DOE OSTI2021

Electronic device and method for compressing sampled data

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.

Morrow, Mitchell Hedges↗
DOE OSTI2023

Device for automated crop root sampling

This invention comprises a device for measuring root pulling force (RPF) in a plant. The RPF device comprises a plant grasping mechanism, as well as a force measurement sensor. In certain embodiments, the device is automatic, so that the “hand of man” is not required to exert force on the plant while the root pulling force of the plant is being measured. Also disclosed is a root pulling force motion mechanism, which brings the RPF device into proximity of a plant to be measured. Further disclosed is a method for measuring root pulling force of a plant.

McKay, John Keith↗
DOE OSTI2024

Capture probe

A system and method for sampling a sample material includes a device for directing sample into a capture probe. The device for supplying sample material to the probe can be a device for radiating energy to the sample material to eject sample from the sample material. A probe includes an outer probe housing having an open end. A liquid supply conduit has an outlet positioned to deliver liquid to the open end. An exhaust conduit removes liquid from the open end of the housing. The liquid supply conduit can be connectable to a liquid supply for delivering liquid at a first volumetric flow rate to the open end of the housing. A liquid exhaust system can be in fluid connection with the liquid exhaust conduit for removing liquid from the liquid exhaust conduit at a second volumetric flow rate such that gas with sample is withdrawn with the liquid.

Kertesz, Vilmos↗
DOE OSTI2022

Capture probe

A system and method for sampling a sample material includes a device for directing sample into a capture probe. The device for supplying sample material to the probe can be a device for radiating energy to the surface to eject sample from the sample material. A probe includes an outer probe housing having an open end. A liquid supply conduit has an outlet positioned to deliver liquid to the open end. An exhaust conduit removes liquid from the open end of the housing. The liquid supply conduit can be connectable to a liquid supply for delivering liquid at a first volumetric flow rate to the open end of the housing. A liquid exhaust system can be in fluid connection with the liquid exhaust conduit for removing liquid from the liquid exhaust conduit at a second volumetric flow rate such that gas with sample is withdrawn with the liquid.

Kertesz, Vilmos↗
DOE OSTI2022

Predicting failure parameters of semiconductor devices subjected to stress conditions

A method for predicting failure parameters of semiconductor devices can include receiving a set of data that includes (i) characteristics of a sample semiconductor device, and (ii) parameters characterizing a stress condition. The method further includes extracting a plurality of feature values from the set of data and inputting the plurality of feature values into a trained model executing on the one or more processors, wherein the trained model is configured according to an artificial intelligence (AI) algorithm based on a previous plurality of feature values, and wherein the trained model is operable to output a failure prediction based on the plurality of feature values. Further, the method includes generating, via the trained model, a predicted failure parameter of the sample semiconductor device due to the stress condition.

Ahmed, Moinuddin↗