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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
NASA NTRS2005

Carbon-Nanotube Conductive Layers for Thin-Film Solar Cells

Thin, transparent layers comprising mats of carbon nanotubes have been proposed for providing lateral (that is, inplane) electrical conductivities for collecting electric currents from the front surfaces of the emitter layers of thin-film solar photovoltaic cells. Traditionally, thin, semitransparent films of other electrically conductive materials (usually, indium tin oxide, zinc oxide, or cadmium sulfide) have been used for this purpose. As in the cases of the traditional semitransparent conductive films, the currents collected by the nanotube layers would, in turn, be further collected by front metal contact stripes. Depending on details of a specific solar-cell design, the layer of carbon nanotubes would be deposited in addition to, or instead of, a semitransparent layer of one of these traditional conductive materials (see figure). The proposal is expected to afford the following advantages: The electrical conductivity of the carbon- nanotube layer would exceed that of the corresponding semitransparent layer of traditional electrically conductive material. The greater electrical conductivity of the carbon-nanotube layer would make it possible to retain adequate lateral electrical conductivity while reducing the thickness of, or eliminating entirely, the traditional semitransparent conductive layer. As a consequence of thinning or elimination of the traditional semitransparent conductive layer, less light would be absorbed, so that more of the incident light would be available for photovoltaic conversion. The greater electrical conductivity of the carbon-nanotube layer would make it possible to increase the distance between front metal contact stripes, in addition to (or instead of) thinning or eliminating the layer of traditional semitransparent conductive material. Consequently, the fraction of solar-cell area shadowed by front metal contact stripes would be reduced again, making more of the incident light available for photovoltaic conversion. The electrical conductivities of individual carbon nanotubes can be so high that the mat of carbon nanotubes could be made sparse enough to be adequately transparent while affording adequate lateral electrical conductivity of the mat as a whole. The thickness of the nanotube layer would be chosen so that the layer would contribute significant lateral electrical conductivity, yet would be as nearly transparent as possible to incident light. A typical thickness for satisfying these competing requirements is expected to lie between 50 and 100 nm. The optimum thickness must be calculated by comparing the lateral electrical conductivity, the distance between front metal stripes, and the amount of light lost by absorption in the nanotube layer.

Landis, Geoffrey A.↗
NASA NTRS2008

Making Complex Electrically Conductive Patterns on Cloth

A method for automated fabrication of flexible, electrically conductive patterns on cloth substrates has been demonstrated. Products developed using this method, or related prior methods, are instances of a technology known as 'e-textiles,' in which electrically conductive patterns ar formed in, and on, textiles. For many applications, including high-speed digital circuits, antennas, and radio frequency (RF) circuits, an e-textile method should be capable of providing high surface conductivity, tight tolerances for control of characteristic impedances, and geometrically complex conductive patterns. Unlike prior methods, the present method satisfies all three of these criteria. Typical patterns can include such circuit structures as RF transmission lines, antennas, filters, and other conductive patterns equivalent to those of conventional printed circuits. The present method overcomes the limitations of the prior methods for forming the equivalent of printed circuits on cloth. A typical fabrication process according to the present method involves selecting the appropriate conductive and non-conductive fabric layers to build the e-textile circuit. The present method uses commercially available woven conductive cloth with established surface conductivity specifications. Dielectric constant, loss tangent, and thickness are some of the parameters to be considered for the non-conductive fabric layers. The circuit design of the conductive woven fabric is secured onto a non-conductive fabric layer using sewing, embroidery, and/or adhesive means. The portion of the conductive fabric that is not part of the circuit is next cut from the desired circuit using an automated machine such as a printed-circuit-board milling machine or a laser cutting machine. Fiducials can be used to align the circuit and the cutting machine. Multilayer circuits can be built starting with the inner layer and using conductive thread to make electrical connections between layers.

Chu, Andrew↗
NASA NTRS2011

Thermally Conductive Tape Based on Carbon Nanotube Arrays

To increase contact conductance between two mating surfaces, a conductive tape has been developed by growing dense arrays of carbon nanotubes (CNTs, graphite layers folded into cylinders) on both sides of a thermally conductive metallic foil. When the two mating surfaces are brought into contact with the conductive tape in between, the CNT arrays will adhere to the mating surface. The van der Waals force between the contacting tubes and the mating surface provides adhesion between the two mating surfaces. Even though the thermal contact conductance of a single tube-to-tube contact is small, the tremendous amount of CNTs on the surface leads to a very large overall contact conductance. Interface contact thermal resistance rises from the microroughness and the macroscopic non-planar quality of mating surfaces. When two surfaces come into contact with each other, the actual contact area may be much less than the total area of the surfaces. The real area of contact depends on the load, the surface roughness, and the elastic and inelastic properties of the surface. This issue is even more important at cryogenic temperatures, where materials become hard and brittle and vacuum is used, which prevents any gas conduction through the interstitial region. A typical approach to increase thermal contact conductance is to use thermally conducting epoxies or greases, which are not always compatible with vacuum conditions. In addition, the thermal conductivities of these compounds are often relatively low. The CNTs used in this approach can be metallic or semiconducting, depending on the folding angle and diameter. The electrical resistivity of multiwalled carbon nanotubes (MWCNTs) has been reported. MWCNTs can pass a current density and remain stable at high temperatures in air. The thermal conductivity of a MWCNT at room temperature is measured to be approximately 3,000 W/m-K, which is much larger than that of diamond. At room temperature, the thermal conductance of a 0.3 sq cm array of CNTs was measured to be as high as 10 W/K. The high thermal conductivity and the nanoscale size make CNTs ideal as thermal interface materials. The CNT-based thermal tape can be used for the thermal management of microelectronic packages and electronic systems. It also can be integrated with current device technology and packaging. The material would allow for an efficient method to manage excess heat generation without requiring any additional power. Lastly, the CNT tape can be used to enhance thermal contact conductance across two mating surfaces on some NASA missions.

Kashani, Ali↗
NASA NTRS2009

A Model of Thermal Conductivity for Planetary Soils: 1. Theory for Unconsolidated Soils

We present a model of heat conduction for mono-sized spherical particulate media under stagnant gases based on the kinetic theory of gases, numerical modeling of Fourier s law of heat conduction, theoretical constraints on the gas thermal conductivity at various Knudsen regimes, and laboratory measurements. Incorporating the effect of the temperature allows for the derivation of the pore-filling gas conductivity and bulk thermal conductivity of samples using additional parameters (pressure, gas composition, grain size, and porosity). The radiative and solid-to-solid conductivities are also accounted for. Our thermal model reproduces the well-established bulk thermal conductivity dependency of a sample with the grain size and pressure and also confirms laboratory measurements finding that higher porosities generally lead to lower conductivities. It predicts the existence of the plateau conductivity at high pressure, where the bulk conductivity does not depend on the grain size. The good agreement between the model predictions and published laboratory measurements under a variety of pressures, temperatures, gas compositions, and grain sizes provides additional confidence in our results. On Venus, Earth, and Titan, the pressure and temperature combinations are too high to observe a soil thermal conductivity dependency on the grain size, but each planet has a unique thermal inertia due to their different surface temperatures. On Mars, the temperature and pressure combination is ideal to observe the soil thermal conductivity dependency on the average grain size. Thermal conductivity models that do not take the temperature and the pore-filling gas composition into account may yield significant errors.

Piqueux, S.↗
NASA NTRS2013

Conductive Carbon Nanotube Inks for Use with Desktop Inkjet Printing Technology

Inkjet printing is a common commercial process. In addition to the familiar use in printing documents from computers, it is also used in some industrial applications. For example, wire manufacturers are required by law to print the wire type, gauge, and safety information on the exterior of each foot of manufactured wire, and this is typically done with inkjet or laser printers. The goal of this work was the creation of conductive inks that can be applied to a wire or flexible substrates via inkjet printing methods. The use of inkjet printing technology to print conductive inks has been in testing for several years. While researchers have been able to get the printing system to mechanically work, the application of conductive inks on substrates has not consistently produced adequate low resistances in the kilohm range. Conductive materials can be applied using a printer in single or multiple passes onto a substrate including textiles, polymer films, and paper. The conductive materials are composed of electrical conductors such as carbon nanotubes (including functionalized carbon nanotubes and metal-coated carbon nanotubes); graphene, a polycyclic aromatic hydrocarbon (e.g., pentacene and bisperipentacene); metal nanoparticles; inherently conductive polymers (ICP); and combinations thereof. Once the conductive materials are applied, the materials are dried and sintered to form adherent conductive materials on the substrate. For certain formulations, increased conductivity can be achieved by printing on substrates supported by low levels of magnetic field alignment. The adherent conductive materials can be used in applications such as damage detection, dust particle removal, smart coating systems, and flexible electronic circuitry. By applying alternating layers of different electrical conductors to form a layered composite material, a single homogeneous layer can be produced with improved electrical properties. It is believed that patterning alternate layers of different conductors may improve electrical pathways through alignment of the conductors and band gap optimization. One feature of this innovation is that flexible conductive traces could be accomplished with a conductive ink having a surface resistivity of less than 10 ohms/square. Another result was that a composite material comprising a mixture of carbon nanotubes and metallic nanoparticles could be applied by inkjet printing to flexible substrates, and the resulting applied material was one to two orders of magnitude more conductive than a material made by printing inks containing carbon nanotubes alone.

Roberson, Luke↗
NASA NTRSDate not supplied

Conductive Tether Coating for Electrodynamic Tethers

The Propulsive Small Expendable Deployer System (ProSEDS), which is an on-orbit demonstration of the propulsion capabilities of electrodynamic tethers in space, is a secondary payload on a Delta 11 unmanned expendable booster. The ProSEDS tether consists of a 5 km bare electrodynamic tether and a 1 0-km non-conductive leader tether. Near the Delta 11, 160 m of the conductive tether is insulated to prevent plasma electron collection from the plasma contactor and for other science requirements. The remainder of the 5-km conductive tether is coated with a new conductive coating to collect plasma electrons. A bare metal tether easily collects electrons from the plasma, but thermal concerns preclude this design. A highly emissive conductive polymer developed by Triton Systems, Inc. has been optimized for both conductivity and thermo-optical properties. The current design for the ProSEDS conductive tether is seven individually coated strands of 28 AWG aluminum wire, coated with an atomic oxygen-resistant conductive polymer composed of a mixture of COR (Colorless Oxygen Resistant) and polyanaline (PANI) known as C-COR (Conductive-Colorless Oxygen Resistant). The conductive-coated wire strands are cold-welded to individually coated strands of the insulated tether. The insulated tether is coated with 1 mil of polyimide and an atomic oxygen resistant polymer TOR-BP. The insulated tether must stand off the entire voltage of the tether (1 200 V) at various times during the mission. All seven wires are twisted around a Kevlar-29 core using the Hi-wire design. Extensive testing has been performed at the Marshall Space Flight Center to qualify both the conductive coating and insulating coating for use on the ProSEDS tether. The conductive coating has been exposed to a plasma to verify the coatings ability to collect electrons from the space plasma from 0 to 1500 V, and to verify the coatings ability to collect electrons after atomic oxygen exposure. The insulated coating has been tested to verify its dielectric strength both in a vacuum and air. Thermal optical properties on both coatings have been measured before and after atomic oxygen testing. The results of these tests will be presented.

Jason A Vaughn↗
NASA NTRS1997

Apparatus for use in determining surface conductivity at microwave frequencies

An apparatus is provided for use in determining surface conductivity of a flat or shaped conductive material at microwave frequencies. A plate has an electrically conductive surface with first and second holes passing through the plate. An electrically conductive material under test (MUT) is maintained in a spaced apart relationship with the electrically conductive surface of the plate by one or more non-conductive spacers. A first coupling loop is electrically shielded within the first hole while a second coupling loop is electrically shielded within the second hole. A dielectric resonator element is positioned between the first and second coupling loops, while also being positioned closer to the MUT than the electrically conductive surface of the plate. Microwave energy at an operating frequency f is supplied from a signal source to the first coupling loop while microwave energy received at the second coupling loop is measured. The apparatus is capable of measuring the Q-factor of the dielectric resonator situated in the cavity existing between the electrically conductive surface of the plate and the MUT. Surface conductivity of the electrically conductive surface can be determined via interpolation using: 1) the measured Q-factor with the electrically conductive surface in place, and 2) the measured Q-factor when the MUT is replaced with reference standards having known surface conductivities.

Hearn, Chase P.↗
NASA NTRS1995

Apparatus for Use in Determining Surface Conductivity at Microwave Frequencies

An apparatus is provided for use in determining surface conductivity of a flat or shaped conductive material at microwave frequencies. A plate has an electrically conductive surface with first and second holes passing through the plate. An electrically conductive material under test (MUT) is maintained in a spaced apart relationship with the electrically conductive surface of the plate by one or more nonconductive spacers. A first coupling loop is electrically shielded within the first hole while a second coupling loop is electrically shielded within the second hole. A dielectric resonator element is positioned between the first and second coupling loops, while also being positioned closer to the MUT than the electrically conductive surface of the plate. Microwave energy at an operating frequency f is supplied from a signal source to the first coupling loop while microwave energy received at the second coupling loop is measured. The apparatus is capable of measuring the Q-factor of the dielectric resonator situated in the 'cavity' existing between the electrically conductive surface of the plate and the MUT. Surface conductivity of the electrically conductive surface can be determined via interpolation using: 1 ) the measured Q-factor with the electrically conductive surface in place, and 2) the measured Q-factor when the MUT is replaced with reference standards having known surface conductivities.

Hearn, Chase P.↗
NASA NTRS2010

Methods of Using a Magnetic Field Response Sensor Within Closed, Electrically Conductive Containers

Magnetic field response sensors are a class of sensors that are powered via oscillating magnetic fields, and when electrically active, respond with their own magnetic fields with attributes dependent upon the magnitude of the physical quantity being measured. A magnetic field response recorder powers and interrogates the magnetic sensors [see Magnetic-Field-Response Measurement- Acquisition System, NASA Tech Briefs Vol. 30, No, 6 (June 2006, page 28)]. Electrically conductive containers have low transmissivity for radio frequency (RF) energy and thus present problems for magnetic field response sensors. It is necessary in some applications to have a magnetic field response sensor s capacitor placed in these containers. Proximity to conductive surfaces alters the inductance and capacitance of the sensors. As the sensor gets closer to a conductive surface, the electric field and magnetic field energy of the sensor is reduced due to eddy currents being induced in the conductive surface. Therefore, the capacitors and inductors cannot be affixed to a conductive surface or embedded in a conductive material. It is necessary to have a fixed separation away from the conductive material. The minimum distance for separation is determined by the desired sensor response signal to noise ratio. Although the inductance is less than what it would be if it were not in proximity to the conductive surface, the inductance is fixed. As long as the inductance is fixed, all variations of the magnetic field response are due to capacitance changes. Numerous variations of inductor mounting can be utilized, such as providing a housing that provides separation from the conductive material as well as protection from impact damage. The sensor can be on the same flexible substrate with a narrow throat portion of the sensor between the inductor and the capacitor, Figure 1. The throat is of sufficient length to allow the capacitor to be appropriately placed within the container and the inductor placed outside the container. The throat is fed through the orifice in the container wall (e.g., fuel tank opening) and connects to the inductor and capacitor via electrical leads to form a closed circuit, Figure 2. Another embodiment is to have the inductor and capacitor fabricated as separate units. In this embodiment, the inductor is mounted external to the container, and the capacitor is mounted internal to the container, Figure 1. Electrical leads are fed through the orifice to connect the inductor and capacitor, Figure 2. When a container holding multiple sensors is made of a conductive material, an antenna can be placed internal to the container. An internal antenna allows all components of the sensors to reside inside the container. The antenna must be separated from the container wall s conductive surface. Additionally, the inductors must be maintained in a fixed position relative to and separated from the container

Woodward, Stanley E.↗
NASA NTRS2003

Mixed Conducting Electrodes for Better AMTEC Cells

Electrode materials that exhibit mixed conductivity (that is, both electronic and ionic conductivity) have been investigated in a continuing effort to improve the performance of the alkali metal thermal-to-electric converter (AMTEC). These electrode materials are intended primarily for use on the cathode side of the sodium-ion-conducting solid electrolyte of a sodium-based AMTEC cell. They may also prove useful in sodium-sulfur batteries, which are under study for use in electric vehicles. An understanding of the roles played by the two types of conduction in the cathode of a sodium-based AMTEC cell is prerequisite to understanding the advantages afforded by these materials. In a sodium-based AMTEC cell, the anode face of an anode/solid-electrolyte/cathode sandwich is exposed to Na vapor at a suitable pressure. Upon making contact with the solid electrolyte on the anode side, Na atoms oxidize to form Na+ ions and electrons. Na+ ions then travel through the electrolyte to the cathode. Na+ ions leave the electrolyte at the cathode/electrolyte interface and are reduced by electrons that have been conducted through an external electrical load from the anode to the cathode. Once the Na+ ions have been reduced to Na atoms, they travel through the cathode to vaporize into a volume where the Na vapor pressure is much lower than it is on the anode side. Thus, the cathode design is subject to competing requirements to be thin enough to allow transport of sodium to the low-pressure side, yet thick enough to afford adequate electronic conductivity. The concept underlying the development of the present mixed conducting electrode materials is the following: The constraint on the thickness of the cathode can be eased by incorporating Na+ -ionconducting material to facilitate transport of sodium through the cathode in ionic form. At the same time, by virtue of the electronically conducting material mixed with the ionically conducting material, reduction of Na+ ions to Na atoms can take place throughout the thickness of the cathode. The net effect is to reduce the diffusion and flow resistance to sodium through the electrode while reducing the electronic resistance by providing shorter conduction paths for electrons. Reduced resistance to both sodium transport and electronic conductivity results in an increase in electric power output.

Ryan, Margaret↗
NASA NTRS2019

Predictability of Ocean Heat Content From Electrical Conductance

Ocean heat content (OHC) is a key climate variable that needs to be monitored to know how Earth's energy imbalance is changing, yet observing OHC remains a challenge. The present study examines whether a depth integral of the ocean's electrical conductivity (“conductance”), which may be inferred from both in situ methods and satellite magnetometers over the global ocean, could help monitor OHC. The ocean's electrical conductivity locally depends on temperature, salinity, and pressure, but it is not as well known how the conductance depends on OHC and ocean salt content. By examining the output of an ocean state estimate shown to agree well with observations that have not been assimilated, this study evaluates the fundamental limitations of using perfectly known ocean conductance to predict OHC, rather than the challenges associated with accounting for observational error. It is found that the ocean's conductance and OHC fields are nonlinearly related but nevertheless highly correlated. A statistical framework tends to predict OHC more accurately than ocean salt content from ocean conductance in regions where conductivity is more sensitive to salinity than temperature. The annually (bidecadally) averaged OHC can be predicted from a combination of conductance and depth‐averaged conductivity ocean fields to within nearly 0.1% (1%) error globally and even more accurately in many poorly observed (e.g., ice‐covered) regions. Practical application of this statistical framework to monitor OHC requires examination of the effect of uncertainties in the observed bathymetry and ocean conductance, which vary with application.

Trossman, D. S.↗
DOE OSTI2024

Coal-derived electrically conductive asphalt pavements for snow/ice melting: From laboratory to field

Timely removal of ice and snow from roads is critical to safe, fast, and uninterrupted transportation networks in cold regions. Constructing electrically conductive asphalt pavements to melt the ice and snow on the roads through resistive eating is an emerging alternative technology to traditional snow/ice removal approaches such as utilizing snowplow machines and deicing chemicals. Carbon-based fibers and fillers including carbon fiber and graphite have been widely reported to make electrically conductive hot mix asphalt mixtures for pavement snow/ice-melting applications. This study aimed to develop and demonstrate a novel type of electrically conductive asphalt pavements for snow/ice-melting, which utilizes electrically conductive cold mix asphalt (CMA) mixtures incorporating coal-derived carbon-based coke aggregate as resistive heating elements. Both laboratory experiments and field tests were conducted to investigate the electrical, mechanical, and thermal properties of such electrically conductive asphalt mixtures and pavements. The laboratory experiment results indicated that the electrically conductive CMA mixtures incorporating coke aggregate had sufficiently high electrical conductivity and satisfactory mechanical performance and the pavement prototype slab utilizing a thin layer of such CMA mixtures could successfully raise the pavement surface temperatures to 8.3–11.7 °C rom a low temperature of –5 °C with an input power density of 473 W/m 2 . The field test results showed that the full-scale coal-derived electrically conductive asphalt pavements were able to increase the pavement surface temperatures when electricity was applied, but the magnitude of temperature increase was highly dependent on the power density. Furthermore, it is promising to use coke aggregate to construct coal-derived electrically conductive asphalt pavements for snow/ice melting.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An Optothermal Raman Model Accounting for Convection, Radiation, and Substrate Effects for Evaluation of the Thermal Conductivity of Two‐Dimensional Coatings

Thermal conductivity of two‐dimensional (2D) materials is important to characterize as this will govern thermal transport physics in the various thermal and energy applications benefitting from the unique properties of these materials. Here, analytical and numerical techniques are presented to determine the thermal conductivity and interfacial conductance of a 2D material coating using confocal micro‐Raman spectroscopy as a noncontact diagnostic. While several methods have been proposed for calculating the values of thermal conductivity, interfacial conductance, and spatial temperature profile, these models often do not consider effects of radiation, convection, and substrate resistance on the temperature and heat flux profiles. We present a model to calculate the thermal conductivity and interfacial conductance which accounts for convection, radiation, and substrate effects to characterize a variety of 2D material coatings, which is demonstrated using large area graphene transferred onto copper and nickel substrates. Convection and radiation effects are found to have a negligible effect on the temperature profile of supported coatings, while the substrate effects have a considerable effect and therefore impact the quantification of thermal conductivity and interfacial conductance in addition to limiting the spatial resolution of the technique. These findings will allow for more accurate and reproducible extraction of the thermal conductivity of graphene and other 2D material coatings when employing noncontact optothermal Raman spectroscopy methods.

2D coatings↗
DOE OSTI2024

Experimental and numerical investigation of fracture conductivity between non-smooth rock surfaces with and without proppant

The enhancement of fracture conductivity is vital for the efficient recovery of subsurface resources, such as geothermal energy and petroleum hydrocarbons. Proppants, granular materials injected into hydraulic fractures to maintain their conductivity, have been studied primarily in the context of smooth fractures (i.e., fractures between smooth rock surfaces). However, non-smooth fractures (i.e., fractures between rough rock surfaces) are common in geoenergy reservoirs and thus require further investigations. In this study, we conducted laboratory measurements of fracture conductivity on shale slabs with non-smooth surfaces and carried out numerical simulation using the lattice Boltzmann (LB) method, which aimed to investigate the conductivity of non-smooth fractures with and without proppants placement. When ceramic proppant with an areal concentration of 2 lb/ft 2 was placed in the fracture, the conductivity was enhanced by roughly 3-8 times compared to fractures without proppant. In fractures with proppant, gas-measured conductivity was higher than that measured with water due to proppant embedment caused by water. The experiments demonstrate the advantages of using proppant in fractures, even if the rock surface roughness can provide certain fracture conductivity via the self-propping mechanism. For fractures without proppants, high rock surface roughness is not necessarily favorable for enhancing fracture conductivity because the self-propping mechanism requires shear slip along the fracture surface. If there is no shear slip, high rock surface roughness can cause a detrimental effect on the fracture conductivity due to the interlocking effect. Utilizing advanced experimental equipment and LB modeling, this research explores the interplays between proppant placement, fracture geometry, and stress conditions to develop a comprehensive understanding of the productivity in non-smooth fractures. Further, the outcomes of this investigation indicate the importance of creating fractures with surface roughness during hydraulic fracturing and will contribute to the development of more efficient stimulation techniques for subsurface energy extraction.

15 GEOTHERMAL ENERGY↗
NASA NTRS1999

Evidence for the Absence of Conductivity Variations above Thunderstorms

Over the last decade, atmospheric conductivity measurements over thunderstorms have been made at 20 km with high altitude aircraft. These measurements show no significant variations in conductivity above thunderstorms once the effects of aircraft charge are removed. The polar conductivities (alpha(sub +) and alpha(sub -)) were simultaneously acquired using a pair of Gerdien probes. In addition, the vertical electric field and the aircraft self charge were measured using two field mills installed nearly symmetrically on the top and bottom of the aircraft. The measurements were collected during the COHMEX (1986), CaPE (1991), STORM-FEST (1992), TOGA COARE (1993) and CAMEX-1, 2 (1993 1995) field campaigns. This extensive data set includes more than 330 overflights of electrified storms (> 170 over storms producing lightning) and represents a broad sample of storm types, seasonal regimes, and geographical distribution. Most of the observed variations in conductivity are caused by charge induced on the aircraft (and hence on the exterior case of the conductivity probes). Theory (Swann, 1914) indicates that some ions with the same sign as the induced charge will be deflected from the air stream reducing the measured conductivity of that polarity. For the opposite polarity, the additional ions that are attracted by the induced charge are collected on the exterior case rather than the inner electrode, leaving the ambient measurement unaltered. The aircraft observations, in agreement with theory, show reductions proportional to the magnitude of the aircraft self charge for conductivity of the same polarity as the self charge and no change for conductivity of opposite polarity. After compensating for the effects of aircraft charge no significant variations in conductivity above thunderstorms have been found. These Gerdien results contrast strongly with the large (and frequent) conductivity variations reported in studies using relaxation probe techniques.

Bailey, Jeff C.↗
NASA NTRS1999

Effects of CO2 on stomatal conductance: do stomata open at very high CO2 concentrations?

Potato and wheat plants were grown for 50 d at 400, 1000 and 10000 micromoles mol-1 carbon dioxide (CO2). and sweetpotato and soybean were grown at 1000 micromoles mol-1 CO2 in controlled environment chambers to study stomatal conductance and plant water use. Lighting was provided with fluorescent lamps as a 12 h photoperiod with 300 micromoles m-2 s-1 PAR. Mid-day stomatal conductances for potato were greatest at 400 and 10000 micromoles mol-1 and least at 1000 micromoles mol-1 CO2. Mid-day conductances for wheat were greatest at 400 micromoles mol-1 and least at 1000 and 10000 micromoles mol-1 CO2. Mid-dark period conductances for potato were significantly greater at 10000 micromoles mol-1 than at 400 or 1000 micromoles mol-1, whereas dark conductance for wheat was similar in all CO2 treatments. Temporarily changing the CO2 concentration from the native 1000 micromoles mol-1 to 400 micromoles mol-1 increased mid-day conductance for all species, while temporarily changing from 1000 to 10000 micromoles mol-1 also increased conductance for potato and sweetpotato. Temporarily changing the dark period CO2 from 1000 to 10000 micromoles mol-1 increased conductance for potato, soybean and sweetpotato. In all cases, the stomatal responses were reversible, i.e. conductances returned to original rates following temporary changes in CO2 concentration. Canopy water use for potato was greatest at 10000, intermediate at 400, and least at 1000 micromoles mol-1 CO2, whereas canopy water use for wheat was greatest at 400 and similar at 1000 and 10000 micromoles mol-1 CO2. Elevated CO2 treatments (i.e. 1000 and 10000 micromoles mol-1) resulted in increased plant biomass for both wheat and potato relative to 400 micromoles mol-1, and no injurious effects were apparent from the 10000 micromoles mol-1 treatment. Results indicate that super-elevated CO2 (i.e. 10000 micromoles mol-1) can increase stomatal conductance in some species, particularly during the dark period, resulting in increased water use and decreased water use efficiency.

NASA Discipline Life Support Systems↗