Development and Characterization of Inorganic-Organic (Si-O-Al) Hybrid Geopolymeric Precursors via Solid State Method

Silicon - In this study, an innovative one part, solution free concept applied for the development of hybrid inorganic–organic geopolymeric precursors having Si-O-Al networks at the precursor...     

Uplift capacity of horizontal anchor plate in geocell reinforced sand

The paper investigates the uplift performance of horizontal anchor plate in geocell reinforced sand through a series of model tests. It is noted that the unreinforced anchor plate undergoes a clear failure at a displacement of about 3% of its width, whereas with the provision of geocell and a layer of geotextile right below the geocell mattress significantly increases the uplift capacity by about 4.5 times higher than that of unreinforced sand and could sustain anchor displacement of more than 60%. Results indicates that the geocell mattress by virtue of its rigidity distributes the uplift load in the lateral directions to a larger area, thereby reducing the stress in the overlying soil mass and hence increases the performance of anchor plate system. The provision of the additional geotextile layer right below the geocell mattress is found to be very effective in increasing the stiffness as well as load carrying capacity of anchor plate system. The optimum size (i.e., width and length) of geocell mattress giving adequate load carrying capacity of anchor plate is found to be 5.4 times of anchor width (5.4B). The comparison of model tests results with 3D numerical analysis shows good agreement, indicating that the proposed model is able to capture the uplift load-displacement behaviour of geocell reinforced anchor plate system.     

Proton Nuclear Magnetic Resonance-Based Method for the Quantification of Epoxidized Methyl Oleate

Epoxidized methyl esters (EMO) with their high oxirane ring reactivity, acts as a raw material in the synthesis of various industrial chemicals including polymers, stabilizers, plasticizers, glycols, polyols, carbonyl compounds, biolubricants etc. EMO has been generally quantified by the gas chromatography (GC) and high-performance liquid chromatography (HPLC) techniques. Taking into the account of the limitations of these techniques, two qHNMR-based equations have been proposed for the quantification of EMO in the mixture of EMO and methylesters (MO). The validity of the proposed method was determined using standard mixtures of MO and EMO having different molar concentrations. The developed equations have been applied on the samples of EMO prepared from oleic acid in two-step process viz., esterification followed by epoxidation. The qHNMR-based EMO quantification showed acceptable agreement with the results obtained from HPLC analysis.     

Contrast enhancement of MRI images using morphological transforms and PSO

Medical imaging plays a crucial role in correct extraction of the significant information for monitoring the patient’s health and providing the quality treatment. A deluge of medical images requires initial interpretation for the presence of any abnormality, however, the correct diagnosis requires the images to be of good quality. To cope with the problem of poor contrast in medical images, this paper presents a method based on morphological transforms to improve the quality of the images. The proposed method incorporates Particle Swarm Optimization to find an optimum value of a parameter which controls the enhancement of the resulting image. The proposed algorithm is executed on a set of MRI images for testing its efficacy. The experimental results are compared in terms of both qualitative and quantitative parameters. The mean opinion score is obtained with the help of experts, which clearly shows the better performance of the proposed method. Furthermore, the parameters like Contrast Improvement Ratio, signal-to-noise ratio, peak signal-to-noise ratio, PL, and Structural Similarity Index are evident of better performance of proposed method when compared with the state-of-the-art methods and few recent methods. The comparison shows that the performance of the proposed method based on morphological transforms incorporating Particle Swarm Optimization is better not only visually but also in terms of other evaluation parameters.

     

Modeling and simulation of temperature drift for ISFET-based pH sensor and its compensation through machine learning techniques

The paper presents modeling and simulation of ion-sensitive field-effect transistor (ISFET)-based pH sensor with temperature-dependent behavioral macromodel and proposes to compensate the temperature drift in the sensor using intelligent machine learning (ML) models. The macromodel is built using SPICE by introducing electrochemical parameters in a metal-oxide-semiconductor field-effect transistor (MOSFET) model to simulate ISFET characteristics. We account for the temperature dependence of electrochemical and semiconductor parameters in our macromodel to increase its robustness. The macromodel is then exported as a subcircuit element, which is used to design the readout interface circuit. A simple constant-voltage, constant-current (CVCC) topology is utilized to generate the data for temperature drift in ISFET pH sensor, which is used to train and test state-of-the-art ML-based regression models in order to compensate the drift behavior. The experimental results demonstrate that the random forest (RF) technique achieves the best performance with very high correlation and low error rate. Corresponding curves for output signal using the trained models show highly temperature-independent characteristics when tested for pH 2, 4, 7, 10, and 12, and we obtained a root mean squared error (RMS) variation of ΔpH ≤ 0.024 over a temperature range of 15°C to 55°C in comparison with ΔpH ≤ 1.346 for uncompensated output signal. This work establishes the framework for integration of ML techniques for drift compensation of ISFET chemical sensor to improve its performance.     

Thermal transport properties of carbon-assisted phase change nanocomposite

Abstract

In this study, stability, density, rheology, and thermal conductivity of the Multi-wall carbon nanotubes (MWCNTs) based PCM nanocomposites were experimentally investigated. The PCM OM08 has been selected for the air-conditioning application with a phase change temperature of 8–10?°C. PCM nanocomposites (without surfactant) in various concentrations namely 0.05, 0.1, 0.3 and 0.5?vol. % were prepared using a typical two-step method and high homogenous stability is achieved using a mechanical mixing technique (ultra-sonication). The density of PCM nanocomposites was measured using two different types of standard volumetric flask approach and the experimental values confirmed good conformity with the Pak and Cho mixing theory. The highest thermal conductivity augmentation of 24.03%and 64.04% were achieved in liquid and solid states, respectively, with 0.5?vol. % of MWCNT. While, the addition of MWCNT resulted in a changeover of Newtonian to Non-Newtonian behaviors at a low shear rate, and the dynamic viscosity enhancement was increased by 130% with 0.5?vol. % of MWCNT. Further, the results were compared with existing correlations and it was found that the experimental values were in line with existing correlations.     

Metal ion sensing and light activated antimicrobial activity of aloe-vera derived carbon dots

Carbon dots (CDs) have emerged out as a potential material amongst the carbon family for a wide range of applications including chemical/biological sensing, photocatalysis, bioimaging, etc. The green synthesis of these CDs from natural sources is gaining the significant interest of peer community for their wide utility. Herein, we present a facile one-step pyrolysis method for CDs synthesis from Aloe-Vera extract, which show bright blue luminescence under UV light with a quantum yield of 12.3%. Further, ex-situ morphological, structural and optical characterizations reveal their high quality and excitation independent emission behavior with the presence of carboxyl, hydroxyl functional groups. Furthermore, these CDs were studied for Fe(III) sensing in water without any surface modifications and assessed for their light activated antibacterial activity against E.Coli and Staphylococcus aureus.     

E-Learning during COVID-19 Outbreak: Cloud Computing Adoption in Indian Public Universities

In the COVID-19 pandemic situation, the need to adopt cloud computing (CC) applications by education institutions, in general, and higher education (HE) institutions, in particular, has especially increased to engage students in an online mode and remotely carrying out research. The adoption of CC across various sectors, including HE, has been picking momentum in the developing countries in the last few years. In the Indian context, the CC adaptation in the HE sector (HES) remains a less thoroughly explored sector, and no comprehensive study is reported in the literature. Therefore, the aim of the present study is to overcome this research vacuum and examine the factors that impact the CC adoption (CCA) by HE institutions (HEIs) in India. The scope of the study is limited to public universities (PUs) in India. There are, in total, 465 Indian PUs and among these 304 PUs, (i.e., 65% PUs) are surveyed using questionnaire-based research. The study has put forth a novel integrated technology adoption framework consisting of the Technology Acceptance Model (TAM), Technology-Organization-Environment (TOE), and Diffusion of Innovation (DOI) in the context of the HES. This integrated TAM-TOE-DOI framework is utilized in the study to analyze eleven hypotheses concerning factors of CCA that have been tested using structural equation modelling (SEM) and confirmatory factor analysis (CFA). The findings reveal that competitive advantage (CA), technology compatibility (TC), technology readiness (TR), senior leadership support, security concerns, government support, and vendor support are the significant contributing factors of CCA by Indian PUs. The study contends that whereas the rest of the factors positively affect the PUs’ intention towards CCA, security concerns are a significant reason for the reluctance of these universities against adopting CC. The findings demonstrated the application of an integrated TAM-TOE-DOI framework to assess determining factors of CCA in Indian PUs. Further, the study has given useful insights into the successful CCA by Indian PUs, which will facilitate eLearning and remote working during COVID-19 or similar outbreak.     

The role of electrical property in determining the response of 20H-80S composite thin films fabricated for biological applications

Journal of Materials Science - The exploration of exquisite electrical properties of biomaterials for clinical applications is driven by natural design. Inspired by the idea, composite thin films...