scholarly journals Structural, Optical and Frequency Dependent Electrical Behaviour of Aluminum Nitride (ALN) Nanopowder

2019 ◽  
Vol 8 (3) ◽  
pp. 7928-7932
Keyword(s):  
Electrical Conductivity ◽  
Particle Size ◽  
Dielectric Permittivity ◽  
Aluminum Nitride ◽  
Loss Tangent ◽  
Raman Shift ◽  
Crystallographic Structure ◽  
Electrical Parameters ◽  
Frequency Dependent ◽  
Relative Loss

Aluminum nitride (AlN) is ceramic material. It has very high thermal and low electrical conductivity. The Variation of Various Electrical Parameters viz. Impedance (Z), Admittance (Y), Dielectric Permittivity ('), Relative Loss (''), Electrical Conductivity (), and Loss Tangent (Tan ) with frequency Dependence of Aluminum Nitride (AlN) Nano powder were studied. Scanning electron microscopy (SEM); Raman Spectroscopy; and X-ray diffraction (XRD) were used to analyse the surfaces and structures of aluminum nirtride nanopowder. It has been found that the particle size is of 36.15 nm and the crystallographic structure is amorphous. The surface morphology of the studied compound has been investigated by Scanning Election Microscopy (SEM) indicating the particles are in nanosize and characteristic range of diameters are in nanoscale. The electrical studies of the studied compound have been examined in order to acquire the electrical parameters (mainly dielectric permittivity, loss, conductivity, loss-tangent, impedance, and admittance). Small rise in the conductivity (with frequency dependent) has been observed due to the decrease in the particle size of the material.it is also observed that the relative permittivity ('), relative loss '') and dissipation factor (Tan ) decreases with increase in frequency. The Raman shift variation with the intensity which shows the peaks of the compound are obtained at 506 cm-1 , 615 cm-1 656 cm-1 , 873 cm-1 , 882 cm-1 , 949 cm-1 , and 974 cm-1 using laser at 785 nm.

Interfacial polarization of disseminated conductive minerals in absence of redox-active species — Part 2: Effective electrical conductivity and dielectric permittivity

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. E159-E176 ◽  
Author(s):  
S. Misra ◽  
C. Torres-Verdín ◽  
A. Revil ◽  
J. Rasmus ◽  
D. Homan
Keyword(s):  
Electrical Conductivity ◽  
Dielectric Permittivity ◽  
Electric Field ◽  
Relative Permittivity ◽  
Effective Conductivity ◽  
Interfacial Polarization ◽  
Surface Conductance ◽  
Frequency Dependent ◽  
Mineral Inclusions ◽  
Effective Electrical Conductivity

Hydrocarbon-bearing conventional formations, mudrock formations, and source-rock formations generally contain clays, pyrite, magnetite, graphitelike carbon, and/or other electrically conductive mineral inclusions. Under redox-inactive conditions, these inclusions give rise to perfectly polarized interfacial polarization (PPIP) when subjected to an external electric field. Effective electrical conductivity and dielectric permittivity of geomaterials containing such inclusions are frequency-dependent properties due to the electric-field-induced interfacial polarization and associated charge relaxation around host-inclusion interfaces. Existing resistivity interpretation techniques do not account for PPIP phenomena, and hence they can lead to inaccurate estimation of water saturation, total organic content, and conductivity of formation water based on subsurface galvanic resistivity, electromagnetic (EM) induction, and EM propagation measurements in the presence of conductive mineral inclusions. In the first paper of our two-part publication series, we derived a mechanistic electrochemical model, the PPIP model, and we validated a coupled model that integrates the PPIP model with a surface-conductance-assisted interfacial polarization (SCAIP) model to quantify the frequency-dependent electrical complex conductivity of geomaterials. We have used the PPIP-SCAIP model to evaluate the dependence of effective complex-valued conductivity of geologic mixtures on (1) frequency, (2) conductivity of the host medium, and (3) material, size, and the shape of inclusions. Notably, we have used the PPIP-SCAIP model to identify rock conditions that give rise to significant differences in effective conductivity and effective relative permittivity of conductive-inclusion-bearing mixtures from those of conductive-inclusion-free homogeneous media. For a mixture containing as low as a 5% volume fraction of disseminated conductive inclusions, the low-frequency effective conductivity of the mixture is in the range of [Formula: see text] to [Formula: see text] with respect to the host conductivity for frequencies between 100 Hz and 100 kHz. Further, the high-frequency effective relative permittivity of that mixture is in the range of [Formula: see text] to [Formula: see text] with respect to the host relative permittivity for frequencies between 100 kHz and 10 MHz.

scholarly journals The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Katarzyna Pentoś ◽  
Krzysztof Pieczarka ◽  
Kamil Serwata
Keyword(s):  
Electrical Conductivity ◽  
Magnetic Susceptibility ◽  
Precision Agriculture ◽  
Soil Compaction ◽  
Soil Layer ◽  
Soil Parameters ◽  
Soil Electrical Conductivity ◽  
Electrical Parameters ◽  
Management Zones ◽  
Additional Information

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

Complex dielectric permittivity, electric modulus and electrical conductivity analysis of Au/Si3N4/p-GaAs (MOS) capacitor

2021 ◽  
Author(s):  
Sema Türkay ◽  
Adem Tataroğlu
Keyword(s):  
Electrical Conductivity ◽  
Dielectric Permittivity ◽  
Electric Modulus ◽  
Arrhenius Equation ◽  
Rf Magnetron Sputtering ◽  
Oxide Semiconductor ◽  
Complex Dielectric Permittivity ◽  
Mos Capacitor ◽  
Universal Power Law ◽  
Complex Electrical Conductivity

AbstractRF magnetron sputtering was used to grow silicon nitride (Si3N4) thin film on GaAs substrate to form metal–oxide–semiconductor (MOS) capacitor. Complex dielectric permittivity (ε*), complex electric modulus (M*) and complex electrical conductivity (σ*) of the prepared Au/Si3N4/p-GaAs (MOS) capacitor were studied in detail. These parameters were calculated using admittance measurements performed in the range of 150 K-350 K and 50 kHz-1 MHz. It is found that the dielectric constant (ε′) and dielectric loss (ε″) value decrease with increasing frequency. However, as the temperature increases, the ε′ and ε″ increased. Ac conductivity (σac) was increased with increasing both temperature and frequency. The activation energy (Ea) was determined by Arrhenius equation. Besides, the frequency dependence of σac was analyzed by Jonscher’s universal power law (σac = Aωs). Thus, the value of the frequency exponent (s) were determined.

Effects of layered sediments on the guided wave in crosswell radar data

Geophysics ◽  
1999 ◽  
Vol 64 (6) ◽  
pp. 1698-1707 ◽  
Author(s):  
Karl J. Ellefsen
Keyword(s):  
Electrical Conductivity ◽  
Dielectric Permittivity ◽  
Radar Data ◽  
Guided Wave ◽  
Sand Layer ◽  
Clay Layers ◽  
The Waves

To understand how layered sediments affect the guided wave in crosswell radar data, traces are calculated for a model representing a sand layer between two clay layers. A guided wave propagates if the wavelengths in the sand layer are similar to the thickness of the sand layer. The amplitude of the guided wave but not its initial traveltime is affected by the thickness of the sand layer. In contrast, both the amplitude and the initial traveltime are affected by the locations of the transmitting and receiving antennas, the electrical conductivity of the sand layer, and the dielectric permittivity of the sand layer. This permittivity can be estimated from the initial traveltime. The effects of the layering on the waves in these calculated traces also are observed in field traces, which were collected in layered sediments.

Simplified estimation of unsaturated soil hydraulic conductivity using bulk electrical conductivity and particle size distribution

Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 23 ◽  
Author(s):  
Mohammad Reza Neyshabouri ◽  
Mehdi Rahmati ◽  
Claude Doussan ◽  
Boshra Behroozinezhad
Keyword(s):  
Electrical Conductivity ◽  
Particle Size ◽  
Hydraulic Conductivity ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Unsaturated Soil ◽  
Soil Core ◽  
Soil Hydraulic Conductivity ◽  
Core Samples ◽  
Soil Hydraulic

Unsaturated soil hydraulic conductivity K is a fundamental transfer property of soil but its measurement is costly, difficult, and time-consuming due to its large variations with water content (θ) or matric potential (h). Recently, C. Doussan and S. Ruy proposed a method/model using measurements of the electrical conductivity of soil core samples to predict K(h). This method requires the measurement or the setting of a range of matric potentials h in the core samples—a possible lengthy process requiring specialised devices. To avoid h estimation, we propose to simplify that method by introducing the particle-size distribution (PSD) of the soil as a proxy for soil pore diameters and matric potentials, with the Arya and Paris (AP) model. Tests of this simplified model (SM) with laboratory data on a broad range of soils and using the AP model with available, previously defined parameters showed that the accuracy was lower for the SM than for the original model (DR) in predicting K (RMSE of logK = 1.10 for SM v. 0.30 for DR; K in m s–1). However, accuracy was increased for SM when considering coarse- and medium-textured soils only (RMSE of logK = 0.61 for SM v. 0.26 for DR). Further tests with 51 soils from the UNSODA database and our own measurements, with estimated electrical properties, confirmed good agreement of the SM for coarse–medium-textured soils (<35–40% clay). For these textures, the SM also performed well compared with the van Genuchten–Mualem model. Error analysis of SM results and fitting of the AP parameter showed that most of the error for fine-textured soils came from poorer adequacy of the AP model’s previously defined parameters for defining the water retention curve, whereas this was much less so for coarse-textured soils. The SM, using readily accessible soil data, could be a relatively straightforward way to estimate, in situ or in the laboratory, K(h) for coarse–medium-textured soils. This requires, however, a prior check of the predictive efficacy of the AP model for the specific soil investigated, in particular for fine-textured/structured soils and when using previously defined AP parameters.

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