Supplemental Material: Minerals Matter: Science, Technology, and Society

Animation flies through the mineral structure of analcime, a mineral with ionic to superionic conductivity. Structure is represented by a ball (showing atoms) and stick (showing bonds) model. The beginning view is a “surface cell” perpendicular to the channel axis looking down <111> to view the pseudo-trigonal representation. Channel axes is 273 Angstroms wide. First image is about 23 times the channel width or 1288 unit cells. Courtesy of David Palmer, CrystalMaker.

Supplemental Material: Minerals Matter: Science, Technology, and Society

Animation flies through the mineral structure of analcime, a mineral with ionic to superionic conductivity. Structure is represented by a ball (showing atoms) and stick (showing bonds) model. The beginning view is a “surface cell” perpendicular to the channel axis looking down <111> to view the pseudo-trigonal representation. Channel axes is 273 Angstroms wide. First image is about 23 times the channel width or 1288 unit cells. Courtesy of David Palmer, CrystalMaker.

Thermal Conductivity Structure and Anisotropy of the Colossal Carbon Mesotubes

A model of a tube with a square cross-section was compiled for the mathematical analysis of the mesotube in Cartesian coordinates, with the selection of an element of a representative volume. To estimate the effective thermal conductivity of the structure, the generalized theory of conductivity with linearization of heat flux streamlines was used. The presence of anisotropy leads to the division of the problem into a separate estimate of the longitudinal and transverse thermal conductivity. The cross-section of the model was divided into elementary sections by a system of auxiliary adiabatic and isothermal planes, then the sections of the model were presented in the form of thermal resistances connected in chains - electrical circuits. Using the analogy of the identity of thermal and electrical resistances, the total conductivity of the sections and the effective thermal conductivity of the structure were determined. This methodology satisfies the test for limit transitions.

Joint inversion of magnetotelluric tippers and geomagnetic depth sounding transfer functions constrains electrical conductivity beneath islands

&lt;p&gt;Geomagnetic field variations recorded at island geomagnetic observatories are one of the data sources that can be used to constrain the electrical conductivity beneath oceans. Hitherto, magnetotelluric (MT) tippers (period range from a few minutes to 3 hours) and geomagnetic depth sounding (GDS) transfer functions (TFs; period range from 6 hours to a few months) were inverted separately to reveal the electrical conductivity structure underneath island observatories. &amp;#160;In this study, we develop a quasi 1-D tool to simultaneously invert MT tippers and GDS TFs. To account for source complexity, we resort to GDS TFs that relates a set of spherical harmonics coefficients describing the source (of ionospheric or magnetospheric origin) to a locally measured vertical magnetic field component. Joint inversion of multiple data sets from different sources helps to improve vertical resolution and reduce uncertainties in the recovered conductivity models. The stochastic optimization method, known as Covariance Matrix Adaptation Evolution Strategy, is applied to solve the inverse problem. The term &amp;#8220;quasi&amp;#8221; is used here to stress the fact that during 1-D inversion the 3-D forward modeling operator is exploited to account for the ocean induction effect (OIE), which is known to strongly influence the island electromagnetic (EM) responses. To efficiently model MT tippers and GDS TFs, the Cartesian-to-Cartesian and spherical-to-Cartesian 3-D EM modeling engines, based on a nested integral equation approach, are adopted. We apply the developed tool to jointly invert MT tippers and GDS TFs estimated at Honolulu geomagnetic observatory, located at Oahu island (Hawaii) in Pacific Ocean, and discuss the recovered conductivity structure.&lt;/p&gt;

Synthesis of Few-layer MoS2@N-doped Carbon Core-shell Hollow Sphere using Cationic Surfactant as a Template for Highly Stable Lithium-ion Storage

Molybdenum disulfide (MoS2) is a promising anode material for lithium-ion batteries (LIBs) because of its high theoretical capacity. But its rapid capacity decay due to the poor conductivity, structure pulverization,...

Head phantoms for bioelectromagnetic applications: a material study

Background Assessments of source reconstruction procedures in electroencephalography and computations of transcranial electrical stimulation profiles require verification and validation with the help of ground truth configurations as implemented by physical head phantoms. Phantoms provide well-defined volume conduction configurations with realistic geometries.We aim to characterize the electrical conductivity of materials for modeling head compartments to establish reproducible and stable physical head phantoms. We analyzed sodium chloride (NaCl) solution, agarose hydrogel, gypsum and reed sticks as surrogate materials for the intracranial volume, scalp, skull and anisotropic conductivity structures. We measured the impedance of all materials when immersed in NaCl solution using a four-point setup. The electrical conductivity values of each material were calculated from the temperature compensated impedances considering the sample geometries. Results We obtained conductivities of 0.332 S/m (0.17 % NaCl solution), 0.0425 S/m and 0.0017 S/m (gypsum with and without NaCl), 0.314 S/m, 0.30 S/m, 0.311 S/m (2 %, 3 %, 4 % agarose). The reed sticks were tested in longitudinal and transversal direction and showed anisotropic conductivity with a ratio of 1:2.8. Conclusion We conclude that the tested materials NaCl solution, gypsum and agarose can serve as stable representation of the three main conductivity compartments of the head, intracranial volume, skull and scalp. An anisotropic conductivity structure such as a piece of white matter can be modeled using tailored reed sticks inside a volume conductor.

Metallized ε-FeOOH and the heterogeneous electrical conductivity structure in the lower mantle

Electrical heterogeneity at the depth of 900-1400 km in Earth’s interior is a key factor to constrain the minor phase composition of the lower mantle. However, prevailing mineralogical models including Fe- or Al-enriched silicates or ferropericlase are insufficient to explain the ultra-high electrical conductivity in local areas of subduction slabs. Here, we measure the electrical conductivity of ε-FeOOH up to 61 GPa. A 3-order abrupt jump of electrical conductivity is observed in 45-50 GPa, reaching 1.24±0.19 × 103 S/m at 61 GPa. Density mean field theory simulations suggest that ε-FeOOH undergoes a Mott-type electronic transition, which leads the conduction mechanism to switch from small polaron conduction to free electron conduction. Compared with bridgmanite, ferropericlase and conventional mantle compositional models, the electrical conductivity of the metallic ε-FeOOH is 1-3 orders of magnitude higher. Minor or moderate incorporation of metallic ε-FeOOH into the ambient lower mantle could reproduce the observed electrical heterogeneity derived from geomagnetic data at 900-1400 km depth.

Stripping induced polarization effects from airborne electromagnetics to improve 3D conductivity inversion of a narrow palaeovalley

The electrical conductivity distribution within wide palaeochannels is usually well-mapped from airborne electromagnetic data using stitched 1D algorithms. Such stitched 1D solutions are, however, inappropriate for narrow valleys. An alternative option is to consider 2D or 3D models to allow for finite lateral extent of conductors. In airborne electromagnetic data within the Musgrave block near the well-studied Valen conductor, strong induced polarization (IP) and superparamagnetic (SPM) effects make physical property and structure estimation even more uncertain for deep channel clays, particularly those whose channel widths are comparable to their depth of burial. We developed a recursive data fitting algorithm based on dispersive thin sheet responses. The separate IP and SPM components of the fit provide near-surface chargeability and SPM distributions, and the associated electromagnetic (EM) fit provides stripped data with monotonic decays compatible with a simple nondispersive conductivity model. The validity of this stripped data prediction was tested through a comparison of 1D conductivity-depth imaging and 3D inversion applied to the original data and the stripped data. Due to the forked geometry of the deep conductivity structure in the region we investigated, we successfully used 3D rather than 2D inversion to predict the conductivity distribution related to the EM data. We recovered from the stripped data a continuous conductivity structure consistent with a branching, clay-filled palaeovalley under cover.