Search results for: Kallol%20K.%20Ghosh

Authors: Arezoo Sadrinezhad, Kallol Sett, S. I. Hariharan

Abstract:

In this paper, a probabilistic framework based on Fokker-Planck-Kolmogorov (FPK) approach has been applied to simulate triaxial cyclic constitutive behavior of uncertain soils. The framework builds upon previous work of the writers, and it has been extended for cyclic probabilistic simulation of triaxial undrained behavior of soils. von Mises elastic-perfectly plastic material model is considered. It is shown that by using probabilistic framework, some of the most important aspects of soil behavior under cyclic loading can be captured even with a simple elastic-perfectly plastic constitutive model.

Keywords: elasto-plasticity, uncertainty, soils, fokker-planck equation, fourier spectral method, finite difference method

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Authors: Srishti Sinha, Deepti Tikariha, Kallol K. Ghosh

Abstract:

Over the past few decades protein-surfactant interactions have been a subject of extensive studies as they are of great importance in wide variety of industries, biological, pharmaceutical and cosmetic systems. Protein-surfactant interactions have been explored the effect of surfactants on structure of protein in the form of solubilization and denaturing or renaturing of protein. Globular proteins are frequently used as functional ingredients in healthcare and pharmaceutical products, due to their ability to catalyze biochemical reactions, to be adsorbed on the surface of some substance and to bind other moieties and form molecular aggregates. One of the most widely used globular protein is bovine serum albumin (BSA), since it has a well-known primary structure and been associated with the binding of many different categories of molecules, such as dyes, drugs and toxic chemicals. Protein−surfactant interactions are usually dependent on the surfactant features. Most of the research has been focused on single-chain surfactants. More recently, the binding between proteins and dimeric surfactants has been discussed. In present study interactions of one dimeric surfactant Butanediyl-1,4-bis (dimethylhexadecylammonium bromide) (16-4-16, 2Br-) and the corresponding single-chain surfactant cetyl trimethylammonium bromide (CTAB) with bovine serum albumin (BSA) have been investigated by surface tension and spectrofluoremetric methods. It has been found that the bindings of all gemini surfactant to BSA were cooperatively driven by electrostatic and hydrophobic interactions. The gemini surfactant carrying more charges and hydrophobic tails, showed stronger interactions with BSA than the single-chain surfactant.

Keywords: bovine serum albumin, gemini surfactants, hydrophobic interactions, protein surfactant interaction

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Authors: Toshikee Yadav, Deepti Tikariha, Jyotsna Lakra, Kallol K. Ghosh

Abstract:

Polycyclic aromatic hydrocarbons (PAHs) are potent atmospheric pollutants that consist of two or more benzene rings. PAHs have low solubility in water. Their slow dissolution can contaminate large amounts of ground water for long period. They are hydrophobic, non-polar and neutral in nature and are known to have potential mutagenic or carcinogenic activity. In current scenario their removal from the environment, water and soil is still a great challenge and scientists worldwide are engaged to invent and design novel separation technology and decontaminating systems. Various physical, chemical, biological and their combined technologies have been applied to remediate organic-contaminated soils and groundwater. Surfactants play a vital role in the solubilization of these hydrophobic organic compounds. In the present investigation Solubilization capabilities of structurally different gemini surfactants i.e. butanediyl-1,4-bis(dimethyldodecylammonium bromide) (C12-4-C12,2Br−), 2-butanol-1,4-bis (dimethyldodecylammonium bromide) (C12-4(OH)-C12,2Br−), 2,3-butanediol-1,4-bis (dimethyldodecylammonium bromide) (C12-4(OH)2-C12,2Br−) for three polycyclic aromatic hydrocarbons (PAHs); phenanthrene (Phe),fluorene (Fluo) and acenaphthene (Ace) have been studied spectrophotometrically at 300 K. The result showed that the solubility of PAHs increases linearly with increasing surfactant concentration, as an implication of association between the PAHs and micelles. Molar solubilization ratio (MSR), micelle–water partition coefficient (Km) and Gibb's free energy of solubilization (ΔG°s) for PAHs have been determined in aqueous medium. (C12-4(OH)2-C12,2Br−) shows the higher solubilization for all PAHs. Findings of the present investigation may be useful to understand the role of appropriate surfactant system for the solubilization of toxic hydrophobic organic compounds.

Keywords: gemini surfactant, molar solubilization ratio, polycyclic aromatic hydrocarbon, solubilization

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

Abstract:

Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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