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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Global Environmental Micro Sensors Test Operations in the Natural Environment

ENSCO, Inc. is developing an innovative atmospheric observing system known as Global Environmental Micro Sensors (GEMS). The GEMS concept features an integrated system of miniaturized in situ, airborne probes measuring temperature, relative humidity, pressure, and vector wind velocity. In order for the probes to remain airborne for long periods of time, their design is based on a helium-filled super-pressure balloon. The GEMS probes are neutrally buoyant and carried passively by the wind at predetermined levels. Each probe contains onboard satellite communication, power generation, processing, and geolocation capabilities. ENSCO has partnered with the National Aeronautics and Space Administration's Kennedy Space Center (KSC) for a project called GEMS Test Operations in the Natural Environment (GEMSTONE) that will culminate with limited prototype flights of the system in spring 2007. By leveraging current advances in micro and nanotechnology, the probe mass, size, cost, and complexity can be reduced substantially so that large numbers of probes could be deployed routinely to support ground, launch, and landing operations at KSC and other locations. A full-scale system will improve the data density for the local initialization of high-resolution numerical weather prediction systems by at least an order of magnitude and provide a significantly expanded in situ data base to evaluate launch commit criteria and flight rules. When applied to launch or landing sites, this capability will reduce both weather hazards and weather-related scrubs, thus enhancing both safety and cost-avoidance for vehicles processed by the Shuttle, Launch Services Program, and Constellation Directorates. The GEMSTONE project will conclude with a field experiment in which 10 to 15 probes are released over KSC in east central Florida. The probes will be neutrally buoyant at different altitudes from 500 to 3000 meters and will report their position, speed, heading, temperature, humidity, and pressure via satellite. The GEMS data will be validated against reference observations provided by current weather instrumentation located at KSC. This paper will report on the results of the GEMSTONE project and discuss the challenges encountered in developing an airborne sensor system.

Adams, Mark L.↗
NASA NTRS2014

Mobilization Protocols for Hybrid Sensors for Environmental AOP Sampling (HySEAS) Observations

The protocols presented here enable the proper mobilization of the latest-generation instruments for measuring the apparent optical properties (AOPs) of aquatic ecosystems. The protocols are designed for the Hybrid Sensors for Environmental AOP Sampling (HySEAS) class of instruments, but are applicable to the community of practice for AOP measurements. The protocols are organized into eleven sections beyond an introductory overview: a) cables and connectors, b) HySEAS instruments, c) platform preparation, d) instrument installation, e) cable installation, f) test deployment, g) test recovery, h) maintenance, i) shipping, j) storage, and k) smallboat operations. Each section concentrates on documenting how to prevent the most likely faults, remedy them should they occur, and accomplishing both with the proper application of a modest set of useful tools. Within the twelve sections, there are Socratic exercises to stimulate thought, and the answers to these exercises appear in Appendix A. Frequently asked questions (FAQs) are summarized in a separate section after the answers to the exercises in Appendix B. For practitioners unfamiliar with the nautical terms used throughout this document plus others likely encountered at sea, an abbreviated dictionary of nautical terms appears in Appendix C. An abbreviated dictionary of radiotelephone terms is presented in Appendix D. To ensure familiarity with many of the tools that are presented, Appendix E provides a description of the tools alongside a thumbnail picture. Abbreviated deployment checklists and cable diagrams are provided in Appendix F. The document concludes with an acknowledgments section, a glossary of acronyms, a definition of symbols, and a list of references.

Mobilizarion↗
DOE OSTI2022

System and method for measuring sun-induced chlorophyll fluorescence

A chlorophyll fluorescence measuring system having at least one spectrometer coupled to a data logger. The data logger provides direct control of the spectrometer and includes on-board memory for storage of target and reference spectrum data obtained by the spectrometer. The data logger may be coupled to an external computer that receives and analyzes target and reference spectrum data to determine SIF using a spectral fitting algorithm. The system may include a spectrometer aiming system coupled to and controlled by the data logger. The system may also include one or more environmental sensors configured to measure environment variables. The environmental sensors may be coupled to the data logger for control and data storage. The environmental data may be communicated to the external computer for use in the spectral fitting algorithm. The data logger may be connected to a network for remote monitoring and control.

Gu, Lianhong↗
DOE OSTI2021

Development of an Innovative Inspection Tool for Superheater Tubes in Fossil Fuel Power Plants

Fossil fuel power plants are complex systems containing multiple components that require periodic health monitoring. Failures in these systems can lead to increased downtime for the plant, reduction of power, and significant cost for repairs. Inspections of the plant’s superheater tubes are typically manual, laborious, and extremely time-consuming. This is due to their small diameter size (between 1.3 and 7.6 cm) and the coiled structure of the tubing. In addition, the tubes are often stacked close to each other, limiting access for external inspection. This paper presents the development and testing of an electrically powered pipe crawler that can navigate inside 5 cm diameter tubes and provide an assessment of their health. The crawler utilizes peristaltic motion within the tubes via interconnected modules for gripping and extending. The modular nature of the system allows it to traverse through straight sections and multiple 90° and 180° bends. Additional modules in the system include an ultrasonic sensor for tube thickness measurements, as well as environmental sensors, a light detecting and ranging (LiDAR) sensor, and camera. Furthermore, these modules utilize a gear system that allows for 360° rotation and provides a means to inspect the entire internal circumference of the tubes.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Expert system for controlling plant growth in a contained environment

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an ''expert system'' which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the ''expert system'' remotely, to assess activity within the growth chamber, and can override the ''expert system''.

May, George A.↗
NASA NTRS2011

Expert system for controlling plant growth in a contained environment

In a system for optimizing crop growth, vegetation is cultivated in a contained environment, such as a greenhouse, an underground cavern or other enclosed space. Imaging equipment is positioned within or about the contained environment, to acquire spatially distributed crop growth information, and environmental sensors are provided to acquire data regarding multiple environmental conditions that can affect crop development. Illumination within the contained environment, and the addition of essential nutrients and chemicals are in turn controlled in response to data acquired by the imaging apparatus and environmental sensors, by an "expert system" which is trained to analyze and evaluate crop conditions. The expert system controls the spatial and temporal lighting pattern within the contained area, and the timing and allocation of nutrients and chemicals to achieve optimized crop development. A user can access the "expert system" remotely, to assess activity within the growth chamber, and can override the "expert system".

May, George A.↗
NASA NTRS2002

Molecular modeling of interactions in electronic nose sensors for environmental monitoring

We report a study aimed at understanding analyte interactions with sensors made from polymer-carbon black composite films. The sensors are used in an Electronic Nose (ENose) which is used for monitoring the breathing air quality in human habitats. The model mimics the experimental conditions of the composite film deposition and formation and was developed using molecular modeling and simulation tools. The Dreiding 2.21 Force Field was used for the polymer and analyte molecules while graphite parameters were assigned to the carbon black atoms. The polymer considered for this work is methyl vinyl ether / maleic acid copolymer. The target analytes include both inorganic (NH3) and organic (methanol) types of compound. Results indicate different composite-analyte interaction behavior.

electronic noise polymer-carbon composite molecula↗
NASA NTRS1999

A magnetostatic-coupling based remote query sensor for environmental monitoring

A new type of in situ, remotely monitored magnetism-based sensor is presented that is comprised of an array of magnetically soft, magnetostatically-coupled ferromagnetic thin-film elements or particles combined with a chemically responsive material that swells or shrinks in response to the analyte of interest. As the chemically responsive material changes size the distance between the ferromagnetic elements changes, altering the inter-element magnetostatic coupling. This in turn changes the coercive force of the sensor, the amplitude of the voltage spikes detected in nearby pick-up coils upon magnetization reversal and the number of higher-order harmonics generated by the flux reversal. Since the sensor is monitored through changes in magnetic flux, no physical connections such as wires or cables are needed to obtain sensor information, nor is line of sight alignment required as with laser telemetry; the sensors can be detected from within sealed, opaque or thin metallic enclosures.

NASA Discipline Life Sciences Technologies↗
NASA NTRS1990

Electrochemical amperometric gas sensors for environmental monitoring and control

Theoretical considerations and experimental results regarding a unique class of vapor sensors are presented, and the sensors are compared to semiconductor-based sensors. The electrochemical sensors are based on nonaquaeous electrolytes, and gas-detection selectivity achieved by applying a known potential to the sensing electrode using a reference electrode and a counter electrode. Results are given regarding the detection of oxygen and carbon dioxide using one cell, the detection of 3-percent carbon dioxide in nitrogen, and the detection of carbon dioxide in air at percentages ranging from 3 to 6. The sensors are found to be effective in the detection of toxic chemical species including CO, NO2, and formaldehyde; the sensors are further found to require minimal power, operate over long periods of time, and function over a wide temperature range.

Venkatasetty, H. V.↗
DOE OSTI2024

Laser-induced graphene gas sensors for environmental monitoring

Artemesia tridentatais a foundational plant taxon in western North America and an important medicinal plant threatened by climate change. Low-cost fabrication of sensors is critical for developing large-area sensor networks for understanding and monitoring a range of environmental conditions. However, the availability of materials and manufacturing processes is still in the early stages, limiting the capacity to develop cost-effective sensors at a large scale. In this study, we demonstrate the fabrication of low-cost flexible sensors using laser-induced graphene (LIG); a graphitic material synthesized using a 450-nm wavelength bench top laser patterned onto polyimide substrates. We demonstrate the effect of the intensity and focus of the incident beam on the morphology and electrical properties of the synthesized material. Raman analyses of the synthesized LIG show a defect-rich graphene with a crystallite size in the tens of nanometers. This shows that the high level of disorder within the LIG structure, along with the porous nature of the material provide a good surface for gas adsorption. The initial characterization of the material has shown an analyte response represented by a change in resistance of up to 5% in the presence of volatile organic compounds (VOCs) that are emitted and detected byArtemisiaspecies. Bend testing up to 100 cycles provides evidence that these sensors will remain resilient when deployed across the landscapes to assess VOC signaling in plant communities. The versatile low-cost laser writing technique highlights the promise of low-cost and scalable fabrication of LIG sensors for gas sensor monitoring.

Chemistry↗
NASA NTRS2023

In Situ Sensors for Monitoring the Space Environment and Its Effect Upon Satellite Materials

Development of advanced materials for space requires both an understanding of the space environment and how a material might be affected by the environment. Despite a long history of space missions, we have insufficient knowledge to fully characterize the exposure that spacecraft materials experience over a mission lifetime, much less the effects that this exposure induces upon spacecraft materials. In addition, the physics of materials/environment interactions is less well understood than optimum owing to the complex nature of the space environment and the challenges in simulating this environment in the laboratory. Our understanding of both the environment and materials behavior in that environment would be advanced by the development of sensors that could be deployed on a variety of missions and collect sufficient data. In-situ environmental sensors would improve both our understanding of spacecraft materials environmental durability and lead to improved ground-laboratory investigations. There are a number of factors that have limited the development of a widespread network of space environmental sensors intended to fill this need. The cost of deploying space systems generally encourages system designers to minimize any functionality that is extraneous to the main mission of a space vehicle. Deploying additional sensors adds cost, size, weight, power and telemetry bandwidth that could interfere with mission goals. The complexity of the space environment makes it challenging to manufacture a sensor that provides a complete characterization of its environment, especially with a limited impact upon the host. Finally, such a hosted sensor could impact the security or reliability of the main mission.

Jim Barrie↗
NASA NTRS2012

Adaptive Sampling of Time Series During Remote Exploration

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Thompson, David R.↗
DOE OSTI2024

COMPASS-FME Synoptic Sites Level 1 Sensor Data v1-2

This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project**This dataset includes:- An overall dataset README file that describes the current version, gives citation and contact information, etc.- Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year.- Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site.- Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v1-2 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

COMPASS-FME Synoptic Sites Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-0 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v1-2

This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project**This dataset includes:- An overall dataset README file that describes the current version, gives citation and contact information, etc.- Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year.- Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site.- Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes.Please see v1-2 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods.* Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021* TEMPEST 1: June 22, 2022* TEMPEST 2: June 6-7, 2023* TEMPEST 3: June 11-13, 2024

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Molecular modeling of polymer composite interactions with analytes in electronic nose sensors for environmental monitoring in International Space Station

We report a molecular modeling study to investigate the polymer-carbon black (CB) composite-analyte interactions in resistive sensors. These sensors comprise the JPL Electronic Nose (ENose) sensing array developed for monitoring breathing air in human habitats. The polymer in the composite is modeled based on its stereisomerism and sequence isomerism, while the CB is modeled as uncharged naphthalene rings (with no hydrogens). The Dreiding 2.21 force field is used for the polymer and solvent molecules and graphite parameters are assigned to the carbon black atoms. A combination of molecular mechanics (MM) and molecular dynamics (NPT-MD and NVT-MD) techniques are used to obtain the equilibrium composite structure by inserting naphthalene rings in the polymer matrix. Polymers considered for this work include poly(4- vinylphenol), polyethylene oxide, and ethyl cellulose. Analytes studied are representative of both inorganic (ammonia) and organic (methanol, toluene, hydrazine) compounds. The results are analyzed for the composite microstructure by calculating the radial distribution profiles as well as for the sensor response by predicting the interaction energies of the analytes with the composites.

electronic noise environmental monitoring polymer ↗