Volume 1 | Issue 1 | January

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Test

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Test

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Creative Commons Attribution 4.0 and The Open Definition

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Reaction of [M(en)2]X2 (where M = Ni(II) and Cu(II) and en = 1,2-diaminoethane) with formaldehyde and ammonia in methanol results in the ready formation of 14 membered macrocyclic complex, [M(L)(ClO4)2] (where L = 1,3,6,8,10,13-tetraazacyclotetradecane). The complexes were characterized by IR, electronic spectral data, EPR and conductance measurements. The electrical conductivities of both chelates indicated them to be non-electrolytes in DMF. The EPR spectrum of the copper complex exhibited G at 3.77 which indicated a considerable exchange interaction in the complex. Spectroscopic evidence suggests that in both the complexes the metal ion is in octahedral environment.

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Macrocycles, epr, exchange interaction

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We have studied 0.1 M 1-(1'-methyl-2'-oxo-propyl)-2,3-dimethylimidazolium chloride [MOPDMIM][Cl] IL electrolyte with the hydrothermally synthesized copper oxide obtained from different concentrations of aqueous copper sulfate precursor solutions (CuO0.1 to CuO0.4 ). We optimized the pore size of copper oxide electrode for the ions of 0.1 M [MOPDMIM][Cl] IL. The synthesized CuO electrodes were characterized by Fourier transform infrared (FTIR), X-ray diffraction technique (XRD) and scanning electron microscopy (SEM). Electrochemical studies of CuO was carried out with respect to the cyclic voltammetry (CV), galvanostatic charge/discharge (GCD) and electrochemical impedance spectroscopy (EIS) which showed CuO0.3 electrode and 0.1M[MOPDMIM][Cl] IL combination works better and shown higher specific capacitance of 86 Fg-1 at 10 mV s-1 .

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Ionic liquid, Super capacitor, Electrolyte, Hydrothermal.

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According to this paper's principal objective, machine learning (ML) approaches may be used to forecast ground characteristics accurately. Geotechnical engineering relies heavily on soil shear strength as one of its primary design criteria. Geotechnical constructions are safe because of the soil's sturdiness. A foundation's shear strength impacts its deformation capacity, lateral loads, and bearing wall. This work studies the prospect of using the Random Forest (RF) algorithm, a well-known machine learning approach, to evaluate the ground's unconfined compressive strength (UCS). The UCS is among the most critical mechanical properties of soil. There has been a dramatic rise in the use of machine learning (ML) methods in numerous scientific domains during the last decade [1]. Ground information is becoming more readily accessible, both remotely and locally. Open-source approaches are making it easier to analyze that data using a machine learning approach. This technique is because there are so many publications that it is hard to manually evaluate all of the articles that pertain to the use of machine learning in soil science without first narrowing down a narrative the review of how ML is applied to a particular research subject [1]. The goal of this study is to evaluate the use of ML methods in geotechnical engineering with the help of an ML algorithm to predict ground properties.

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Machine learning, ground properties, Random Forest, Geological Investigation, Soil Cohesion, Excavation, Civil Engineering

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The primary purpose of this work is to undertake an empirical investigation to determine the impact that the spatial variability of block-type cement-treated soil has on the load-carrying capacity of foundations when subjected to inclined loads. In engineering structures, non-homogeneity of concrete characteristics due to spatial variability is an essential property. Analyzing it may be quite valuable in determining the location of damaged regions and determining the resiliency of a building. For the first step of the investigation, Non-destructive Testing (NDT) was used on laboratory concrete slabs outside an existing bridge [1]. As a result, the spatial variability of many NDT methods was measured at three levels: point (repeatability), local (local variation), and worldwide. DCM soils have a wide range of strengths, so the performance of DCM soil-improved buildings is very unclear. This process may lead to unanticipated points of failure and specific collapse locations that are not considered during the designing phase. In contrast to a gaussian distribution, DCM ground hardness is spatially interconnected. Many researchers have reported on various projects' spatial correlation lengths determined for DCM soil strength. There are multiple ways that the qualities of DCM soil are linked, and the claimed correlation lengths range from 0 to 12 m[1]. Such correlation lengths are minuscule compared to the proportions of a typical barrier. DCM soil's strength qualities might fluctuate over time within a single project, leading to isolated zones of low and high strength. Soft clayey soils stabilized, and their bearing capacity increased by using deep mixing procedures. Determining the bearing capacity for design may be complicated because the cement-treated ground exhibits geographic heterogeneity in its shear strength. Under inclining load situations, this research examines the dependability of block-type cement-treated foundations. The shear strength of the undrained soil is modeled as a random field with two distinguishing features: a log-normal dispersion and a spatial correlation strength.

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Spatial Variability, block-type cement-treated ground, DCM soil strength, NDT methods, bearing capacity, incline load

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Creative Commons Attribution 4.0 and The Open Definition

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THE STUDY OF ELECTRO-CHEMICAL BEHAVIOUR OF METALS IN WATER IRRADIATED BY HIGH INTENSITY ULTRASONIC FIELD

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THE STUDY OF ELECTRO-CHEMICAL BEHAVIOUR OF METALS IN WATER IRRADIATED BY HIGH INTENSITY ULTRASONIC FIELD

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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POST-COLONIALISM

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