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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↗
DOE OSTI2023

Process-dependent anisotropic thermal conductivity of laser powder bed fusion AlSi10Mg: impact of microstructure and aluminum-silicon interfaces

Purpose AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity. Design/methodology/approach The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity. Findings The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm 3 and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm 3 . The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains. Practical implications The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications. Originality/value To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

Engineering↗
DOE OSTI2012

Effect of bending buckling of carbon nanotubes on thermal conductivity of carbon nanotube materials

The effect of bending buckling of carbon nanotubes (CNTs) on thermal conductivity of CNT materials is investigated in atomistic and mesoscopic simulations. Nonequilibrium molecular dynamics simulations of the thermal conductance through an individual buckling kink in a (10,10) single-walled CNT reveal a strong dependence (close to inverse proportionality) of the thermal conductance of the buckling kink on the buckling angle. The value of the buckling kink conductance divided by the cross-sectional area of the CNT ranges from 40 to 10 GWm -2 K -1 as the buckling angle changes from 20 to 110°. The predictions of the atomistic simulations are used for parameterization of a mesoscopic model that enables calculations of thermal conductivity of films composed of thousands of CNTs arranged into continuous networks of bundles. The results of mesoscopic simulations demonstrate that the conductivity of CNT films is sensitive to the angular dependence of the buckling kink conductance and the length of the individual CNTs. For a film composed of 1 $µ$m-long CNTs, the values of the in-plane film conductivity predicted with a constant conductance of 20 GWm -2 K -1 and the angular-dependent conductance obtained in atomistic simulations are about 40 and 20% lower than the conductivity predicted for the same film with zero thermal resistance of the buckling kinks, respectively. The weaker impact of the angular-dependent buckling kink conductance on the effective conductivity of the film is explained by the presence of a large fraction of kinks that have small buckling angles and correspondingly large values of conductance. The results of the simulations suggest that the finite conductance of the buckling kinks has a moderate, but non-negligible, effect on thermal conductivity of materials composed of short CNTs with length up to 1 $µ$m. The contribution of the buckling kink thermal resistance becomes stronger for materials composed of longer CNTs and/or characterized by higher density of buckling kinks.

36 MATERIALS SCIENCE↗
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↗
DOE OSTI2015

Electrical conduction mechanism of LaNi{sub x}Me{sub 1−x}O{sub 3−δ} (Me = Fe, Mn)

Graphical abstract: Compositional dependence of (a) electrical conductivity and (b) E{sub a} for hopping conduction of LaNi{sub x}Me{sub 1−x}O{sub 3} (Me = Fe, Mn). - Highlights: • Electrical conduction mechanism of LaNi{sub x}Me{sub 1−x}O{sub 3} (Me = Fe, Mn) was investigated. • Hopping conduction model could be applied for conductivity of both specimens. • The difference of E{sub a} due to that of energy level of Fe and Mn was observed. • Hole concentration estimated by iodimetry increases with increasing Ni content. - Abstract: Electrical conduction mechanism of LaNi{sub x}Fe{sub 1−x}O{sub 3−δ} and LaNi{sub x}Mn{sub 1−x}O{sub 3+δ} expected as Sr-free new cathode material for solid oxide fuel cells was analyzed. Electrical conduction behaviors of both specimens could be well fitted by small polaron hopping conduction model. The electrical conductivity of LaNi{sub x}Fe{sub 1−x}O{sub 3−δ} increased with increasing Ni content, showing agreement with decrease of activation energy for hopping conduction. The decrease of electrical conductivity and increase of activation energy of LaNi{sub x}Mn{sub 1−x}O{sub 3+δ} were observed with increasing Ni content for 0.0 ≤ x ≤ 0.4. Further Ni substitution increased electrical conductivity and decreased activation energy for 0.4 ≤ x ≤ 0.6. It was revealed using iodometry that the difference of hole carrier density between LaNi{sub x}Fe{sub 1−x}O{sub 3−δ} and LaNi{sub x}Mn{sub 1−x}O{sub 3+δ} was small. It was suspected that the origin of the difference of electrical conduction behavior of LaNi{sub x}Fe{sub 1−x}O{sub 3−δ} and LaNi{sub x}Mn{sub 1-x}O{sub 3+δ} was difference of energy level of e{sub g} band composed of Fe 3d or Mn 3d orbitals and their overlapping quantity with O 2p and Ni 3d band.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Effects of thermal conductivity of liquid layer in NIF wetted foam experiments

Numerical simulation of inertial confinement fusion (ICF) capsule implosion experiments needs many plasma parameters corresponding to different materials and their mixtures for a wide range of densities and temperatures. Thermal conduction plays a crucial role in coupling energy to the capsule, is one of the primary mechanisms of energy loss during implosion, has a significant effect on hot-spot formation, and impacts the growth of hydrodynamic instabilities. The determination of accurate thermal conductivity of ICF relevant materials is thus important for understanding capsule performance. Analytic models such as Spitzer or Lee-More models have been extensively used in simulations due to the limited availability of experimental data. First principles calculations have shown that these analytic models tend to underestimate electron thermal conductivity in the warm dense plasma regime for ICF related materials. Here, we numerically investigate the effects of different models for the electron heat conductivity coefficients, including both analytic and Quantum Molecular Dynamics (QMD)-based models, for mixed materials in ICF. We also investigate the impact of how conductivities are calculated in mixed cells from constituent material conductivities. We apply this to the modeling of recent wetted foam capsule implosions on the National Ignition Facility, in which a foam layer on the inside of the capsule is wetted with deuterium-tritium (DT) liquid. We have found that electron heat conductivity affects the initial hot-spot formation and its evolution. Strikingly, we observe that capsule performance is more sensitive to the method used to mix material conductivities in mixed cells than how individual material conductivity coefficients are calculated. Furthermore, we have found that using the first principles QMD-based conductivity model along with an appropriate model for mixed-cell conductivities yields better agreement with experimental results compared to the established modeling strategies. We also investigate the impact of mixed material conductivity modeling on the process of ablator material mixing with DT ice in a plastic ice-layer capsule. In our simulations, the heat conductivity model affects the calculated mix widths at the fuel-ablator interface, particularly near the tent scar. Additional mixing between the DT fuel and the ablator in turn increases the implosion adiabat, which results in a lower hot-spot pressure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Experimental Determination and Thermodynamic Modeling of Electrical Conductivity of SRS Waste Tank Supernate

SRS High Level Waste Tank Farm personnel rely on conductivity probes for detection of incipient overflow conditions in waste tanks. Minimal information is available concerning the sensitivity that must be achieved such that that liquid detection is assured. Overly sensitive electronics results in numerous nuisance alarms for these safety-related instruments. In order to determine the minimum sensitivity required of the probe, Tank Farm Engineering personnel need adequate conductivity data to improve the existing designs. Little or no measurements of liquid waste conductivity exist; however, the liquid phase of the waste consists of inorganic electrolytes for which the conductivity may be calculated. Savannah River Remediation (SRR) Tank Farm Facility Engineering requested SRNL to determine the conductivity of the supernate resident in SRS waste Tank 40 experimentally as well as computationally. In addition, SRNL was requested to develop a correlation, if possible, that would be generally applicable to liquid waste resident in SRS waste tanks. A waste sample from Tank 40 was analyzed for composition and electrical conductivity as shown in Table 4-6, Table 4-7, and Table 4-9. The conductivity for undiluted Tank 40 sample was 0.087 S/cm. The accuracy of OLI Analyzer™ was determined using available literature data. Overall, 95% of computed estimates of electrical conductivity are within ±15% of literature values for component concentrations from 0 to 15 M and temperatures from 0 to 125 °C. Though the computational results are generally in good agreement with the measured data, a small portion of literature data deviates as much as ±76%. A simplified model was created that can be used readily to estimate electrical conductivity of waste solution in computer spreadsheets. The variability of this simplified approach deviates up to 140% from measured values. Generally, this model can be applied to estimate the conductivity within a factor of two. The comparison of the simplified model to pure component literature data suggests that the simplified model will tend to under estimate the electrical conductivity. Comparison of the computed Tank 40 conductivity with the measured conductivity shows good agreement within the range of deviation identified based on pure component literature data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Thermal Conductivity of Metallic Uranium

This project has developed a modeling and simulation approaches to predict the thermal conductivity of metallic fuels and their alloys. We focus on two methods. The first method has been developed by the team at the University of Wisconsin Madison. They developed a practical and general modeling approach for thermal conductivity of metals and metal alloys that integrates ab-initio and semi-empirical physics-based models to maximize the strengths of both techniques. The second method has been developed by the team at Virginia Tech. This approach consists of a determining the thermal conductivity using only ab-initio methods without any fitting parameters. Both methods were complementary. The models incorporated both phonon and electron contributions. Good agreement with experimental data over a wide temperature range were found. The models also provided insight into the different physical factors that govern the thermal conductivity under different temperatures. The models were general enough to incorporate more complex effects like additional alloying species, defects, transmutation products and noble gas bubbles to predict the behavior of complex metallic alloys like U-alloy fuel systems under burnup. 3 Introduction Thermal conductivity is an important thermal physical property affecting the performance and efficiency of metallic fuels. Some experimental measurement of thermal conductivity and its correlation with composition and temperature from empirical fitting are available for U, Zr and their alloys with Pu and other minor actinides. However, as reviewed in by Kim, Cho and Sohn, due to the difficulty in doing experiments on actinide materials, thermal conductivities of metallic fuels have only been measured at limited alloy compositions and temperatures, some of them even being negative and unphysical. Furthermore, the correlations developed so far are empirical in nature and may not be accurate when used for prediction at conditions far from those used in the original fitting. Moreover, as fuels burn up in the reactor and fission products are built up, thermal conductivity is also significantly changed. Unfortunately, fundamental understanding of the effect of fission products is also currently lacking. In this project, we probe thermal conductivity of metallic fuels with ab initio calculations, a theoretical tool with the potential to yield better accuracy and predictive power than empirical fitting. This work will both complement experimental data by determining thermal conductivity in wider composition and temperature ranges than is available experimentally, and also develop mechanistic understanding to guide better design of metallic fuels in the future. So far, we focused on α-U perfect crystal, the ground-state phase of U metal. We focus on two methods. The first method has been developed by the team at the University of Wisconsin Madison. They developed a practical and general modeling approach for thermal conductivity of metals and metal alloys that integrates ab-initio and semi-empirical physics-based models to maximize the strengths of both techniques. The second method has been developed by the team at Virginia Tech. This approach consists of a determining the thermal conductivity using only ab-initio methods without any fitting parameters. Both methods were complementary and very helpful to understand the physics behind the thermal conductivity in metallic uranium and other materials with similar characteristics. In Section I, the combined model developed at UWM is explained. In Section II, the ab-initio method developed at VT is described along with the uranium pseudo-potential and its validation. Section III is devoted to the work done by Jianguo Yu at INL. Finally, we will present the performance of the project in terms of milestones, publications, and presentations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
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↗
DOE OSTI2012

The influence of Zn vacancy on thermal conductivity of {beta}-Zn{sub 4}Sb{sub 3}: A molecular dynamics study

The influence of Zn vacancy on lattice thermal conductivity of {beta}-Zn{sub 4}Sb{sub 3} is studied by non-equilibrium molecular dynamics approach. The lattice thermal conductivity of single-crystal bulk {beta}-Zn{sub 4}Sb{sub 3} decreases rapidly when there is Zn vacancy, and then when the vacancy grows, the lattice thermal conductivity decreases further but rather slowly, which suggests a scaling law of k{sub v}{approx}n{sub v}{sup -{alpha}} of Zn atom vacancy (n{sub v}) to lattice thermal conductivity (k{sub vac}). This phenomenon is attributed to the fact that the existence of vacancy scattering can significantly decrease the mean free path. When the Zn atom vacant proportion reaches 10%, that is the vacancy model of {beta}-Zn{sub 4}Sb{sub 3}, the lattice thermal conductivity is 1.32 W/mk along the x-axis and 1.62 W/mk along the z-axis, respectively, which drops by {approx}90% that of its full occupancy model. Therefore, our calculations show that the 10% Zn atom vacancy in {beta}-Zn{sub 4}Sb{sub 3} is the main reason for its exceptionally low thermal conductivity, and the interstitial Zn atoms have little effect on the thermal conductivity of single-crystal {beta}-Zn{sub 4}Sb{sub 3}. - Graphical abstract: The bulk thermal conductivity (k{sub pure}) is 11.88 W/mk along the x-axis and 20.00 W/mk the z-axis. When it is 10% vacancy, namely the vacancy model of {beta}-Zn{sub 4}Sb{sub 3}, the thermal conductivity of {beta}-Zn{sub 4}Sb{sub 3} is 1.32 W/mk along the x-axis and 1.62 W/mk along the z-axis, respectively, which reduces by {approx}90% that of its full occupancy model. Our calculations show that the 10% Zn atom vacancy in the crystal structure of {beta}-Zn{sub 4}Sb{sub 3} is the main reason for its exceptionally low thermal conductivity, and the interstitial Zn atoms have little effect on the thermal conductivity of single-crystal {beta}-Zn{sub 4}Sb{sub 3}. Highlights: Black-Right-Pointing-Pointer The lattice stability of {beta}-Zn{sub 4}Sb{sub 3} decreases remarkably with the growing vacancy. Black-Right-Pointing-Pointer 10% Zn vacancy leads to its low thermal conductivity and structural instability. Black-Right-Pointing-Pointer Interstitial Zn atoms in {beta}-Zn{sub 4}Sb{sub 3} mainly stabilize the crystal structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Electrical conductivity studies of Bi{sub 2}O{sub 3}-Li{sub 2}O-ZnO-B{sub 2}O{sub 3} glasses

Highlights: Black-Right-Pointing-Pointer Ac conductivity measurements and its analysis has been performed on Bi{sub 2}O{sub 3}-Li{sub 2}O-ZnO-B{sub 2}O{sub 3} glasses in the temperature range 30-300 Degree-Sign C and a frequency range of 100 Hz to 1 MHz. Black-Right-Pointing-Pointer The dc conductivity increased and the activation energy decreased with lithium content. Black-Right-Pointing-Pointer The frequency dependent conductivity has been analyzed employing conductivity and modulus formalisms. Black-Right-Pointing-Pointer The onset of conductivity relaxation shifts towards higher frequencies with temperature. Black-Right-Pointing-Pointer The Almond-West conductivity formalism is used to explain the scaling behavior, and the relaxation mechanism is independent of temperature. -- Abstract: Ac conductivity measurements and its analysis has been performed on xBi{sub 2}O{sub 3}-(65-x)Li{sub 2}O-20ZnO-15B{sub 2}O{sub 3} (0 {<=} x {<=} 20) glasses in the temperature range 30-300 Degree-Sign C and a frequency range of 100 Hz to 1 MHz. The dc conductivity increased and the activation energy decreased with lithium content. The frequency dependent conductivity has been analyzed employing conductivity and modulus formalisms. The onset of conductivity relaxation shifts towards higher frequencies with temperature. The Almond-West conductivity formalism is used to explain the scaling behavior, and the relaxation mechanism is independent of temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Defect structure, electronic conductivity and expansion of properties of (La{sub 1-x}Sr{sub x}){sub s}Co{sub 1-y}Ni{sub y}O{sub 3-{delta}}

This study reports on oxygen nonstoichiometry, electronic conductivity and lattice expansion of three compositions as function of T and P{sub O{sub 2}} in the (La{sub 1-x}Sr{sub x}){sub s}Co{sub 1-y}Ni{sub y}O{sub 3-{delta}}(x=0.1, y=0.4; x=0.1, y=0.3; x=0.2, y=0.2) materials system. The nonstoichiometry data were successfully fitted using the itinerant electron model which indicates the existence of delocalized electronic states. This was also reflected in the high electronic conductivities, above 1000 S cm{sup -1}, measured for all three compositions. The electronic conductivity was shown to decrease linearly with the oxygen nonstoichiometry parameter, {delta}, supporting that the conductivity is dependent on p-type charge carriers. Comparing calculated p-type mobilities with data reported in literature on La{sub 1-x}Sr{sub x}CoO{sub 3} indicated that Ni-substitution into (La{sub 1-x}Sr{sub x}){sub s}CoO{sub 3-{delta}}increases the p-type mobility. The electronic conductivity was also found to be dependent on intrinsic charge related to spin excitations and Ni substitution rather than the p-type charge. A conductivity mechanism is hypothesized including a metallic like conductivity of the p-type charge and a small polaron conductivity of the intrinsic charge. Lattice expansion as function of T and {delta} was successfully described using first and second order thermal and chemical expansion coefficients. Substituting 10% Co with Ni in (La{sub 0.6}Sr{sub 0.4}){sub 0.99}CoO{sub 3-{delta}}was found to decrease the apparent thermal expansion with about 25%. - Graphical Abstract: Electronic conductivity as function of temperature for (La{sub 0.8}Sr{sub 0.2}){sub 0.99}Co{sub 0.8}Ni{sub 0.2}O{sub 3-{delta}}. A model with metallic like conductivity for the extrinsic p-type charge carrier (due to Sr substitution) and small polaron conductivity for intrinsic charge carriers (due to Co spin transitions) was found to best describe the total conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microscopic theory, analysis, and interpretation of conductance histograms in molecular junctions

Molecular electronics break-junction experiments are widely used to investigate fundamental physics and chemistry at the nanoscale. Reproducibility in these experiments relies on measuring conductance on thousands of freshly formed molecular junctions, yielding a broad histogram of conductance events. Experiments typically focus on the most probable conductance, while the information content of the conductance histogram has remained unclear. Here we develop a microscopic theory for the conductance histogram by merging the theory of force-spectroscopy with molecular conductance. The procedure yields analytical equations that accurately fit the conductance histogram of a wide range of molecular junctions and augments the information content that can be extracted from them. Our formulation captures contributions to the conductance dispersion due to conductance changes during the mechanical elongation inherent to the experiments. In turn, the histogram shape is determined by the non-equilibrium stochastic features of junction rupture and formation. The microscopic parameters in the theory capture the junction’s electromechanical properties and can be isolated from separate conductance and rupture force (or junction-lifetime) measurements. The predicted behavior can be used to test the range of validity of the theory, understand the conductance histograms, design molecular junction experiments with enhanced resolution and molecular devices with more reproducible conductance properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Enhanced neurite outgrowth on electrically conductive carbon aerogel substrates in the presence of an external electric field

Previous works from our laboratory have firmly established that aerogels are a suitable substrate to elicit accelerated neurite extension. On non-conducting aerogels, in the presence of an externally-applied DC bias, neurons extended neurites which were preferentially aligned towards the anode. In this work, we sought to determine whether electrically-conductive carbon aerogels elicited a more robust alignment of neurites toward the anode than non-conductive aerogels due to the capacity of conductive aerogels to sustain a current, thereby providing a direct interface between neurons and the external electrical stimulus. To determine if this was the case, we plated PC12 neuronal cells on electrically conductive carbon aerolges derived from acetic acid-catalized resorcinol formaldehyde aerogels (ARF-CA) and subjected them to an external electric field. The voltages applied at the electrodes of the custom-built electro-stimulation chamber were 0 V, 15 V, and 30 V. For each voltage, the directionality and length of the neurites extended by PC12 cells were determined and compared to those observed when PC12 cells were plated on non-conductive aerogels subjected to the same voltage. The results show that the directionality of neurite extension was similar between conductive and non-conductive aerogels. A higher neurite length difference was observed on conductive aerogels with increasing voltage, 43% and 106% for 0–15 V and 0–30 V respectively, compared to non-conductive aerogels, 12% and 20%. These findings indicate that conductive carbon aerogels have a greater potential as scaffolds for nerve regeneration than non-conductive ones.

36 MATERIALS SCIENCE↗