Novel catechol-derived phosphorus-based precursors for coating applications

Polymer Bulletin - Depletion of the petroleum resources and poor flame-retardant properties of the epoxy resins drive researchers to develop an epoxy resin with good heat stability from...     

Differential Physio-Biochemical and Metabolic Responses of Peanut (Arachis hypogaea L.) under Multiple Abiotic Stress Conditions

The frequency and severity of extreme climatic conditions such as drought, salinity, cold, and heat are increasing due to climate change. Moreover, in the field, plants are affected by multiple abiotic stresses simultaneously or sequentially. Thus, it is imperative to compare the effects of stress combinations on crop plants relative to individual stresses. This study investigated the differential regulation of physio-biochemical and metabolomics parameters in peanut (Arachis hypogaea L.) under individual (salt, drought, cold, and heat) and combined stress treatments using multivariate correlation analysis. The results showed that combined heat, salt, and drought stress compounds the stress effect of individual stresses. Combined stresses that included heat had the highest electrolyte leakage and lowest relative water content. Lipid peroxidation and chlorophyll contents did not significantly change under combined stresses. Biochemical parameters, such as free amino acids, polyphenol, starch, and sugars, significantly changed under combined stresses compared to individual stresses. Free amino acids increased under combined stresses that included heat; starch, sugars, and polyphenols increased under combined stresses that included drought; proline concentration increased under combined stresses that included salt. Metabolomics data that were obtained under different individual and combined stresses can be used to identify molecular phenotypes that are involved in the acclimation response of plants under changing abiotic stress conditions. Peanut metabolomics identified 160 metabolites, including amino acids, sugars, sugar alcohols, organic acids, fatty acids, sugar acids, and other organic compounds. Pathway enrichment analysis revealed that abiotic stresses significantly affected amino acid, amino sugar, and sugar metabolism. The stress treatments affected the metabolites that were associated with the tricarboxylic acid (TCA) and urea cycles and associated amino acid biosynthesis pathway intermediates. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and heatmap analysis identified potential marker metabolites (pinitol, malic acid, and xylopyranose) that were associated with abiotic stress combinations, which could be used in breeding efforts to develop peanut cultivars that are resilient to climate change. The study will also facilitate researchers to explore different stress indicators to identify resistant cultivars for future crop improvement programs.     

The level of polyaromatic hydrocarbons in kajal and surma of major Indian brands

Kajal and surma are eye cosmetics extensively used in Indian subcontinent. Kajal is prepared by burning of vegetable oil and butter oil while surma by grinding of the stones. High performance liquid chromatography and gas chromatography–mass spectrometry instruments were used for quantification and confirmation of 16 polyaromatic hydrocarbons (PAHs). Significant concentration of PAH was found in all the samples examined. The median concentration of PAH ranged from 0.14 (lowest, anthracene) to 31.18 μg g⁻¹ [dibenz(a,h)anthracene] in kajal sample and from not detectable concentration (naphthalene) to 197.47 μg g⁻¹ of benzo(a)pyrene in surma sample. Fifteen PAHs were detected in all the samples. Therefore the use of kajal and surma in eye should be strictly restricted.     

Kinetics of colour and texture changes in Gulabjamun balls during deep-fat frying

Gulabjamun, a popular Indian milk sweet, is prepared by deep-fat frying of balls of dough made of khoa, wheat flour and baking powder, and subsequent dipping in sugar syrup. Kinetics of colour and texture changes in gulabjamun balls were investigated with regard to frying temperature (120, 130, 140 °C). Crust colour was evaluated in terms of CIELAB parameters L*, a*, b*, and ΔE, and rheological properties in terms of hardness, stiffness and firmness. Frying-induced surface browning was reflected in a decreasing lightness value L* as well as the ratio of yellow hue index b* and red hue index a* and total colour expressed in terms of ΔE, L* following a zero-order change whereas the other parameters, a first-order change. Increase in the texture parameters hardness and firmness followed zero-order reaction kinetics whereas stiffness rise followed a first-order reaction. The temperature dependence of reaction constants could be explained by the Arrhenius equation. Activation energy was also obtained for both the colour and texture changes, which were in the range of 24.5-77.6 kJ/mol. High correlations between colour and texture parameters were observed and it was concluded that L* alone could be used to predict the firmness of deep-fat fried gulabjamun balls.     

Genome-wide bacterial toxicity screening uncovers the mechanisms of toxicity of a cationic polystyrene nanomaterial

By exploiting a genome-wide collection of bacterial single-gene deletion mutants, we have studied the toxicological pathways of a 60-nm cationic (amino-functionalized) polystyrene nanomaterial (PS-NH(2)) in bacterial cells. The IC(50) of commercially available 60 nm PS-NH(2) was determined to be 158 μg/mL, the IC(5) is 108 μg/mL, and the IC(90) is 190 μg/mL for the parent E. coli strain of the gene deletion library. Over 4000 single nonessential gene deletion mutants of Escherichia coli were screened for the growth phenotype of each strain in the presence and absence of PS-NH(2). This revealed that genes clusters in the lipopolysaccharide biosynthetic pathway, outer membrane transport channels, ubiquinone biosynthetic pathways, flagellar movement, and DNA repair systems are all important to how this organism responds to cationic nanomaterials. These results, coupled with those from confirmatory assays described herein, suggest that the primary mechanisms of toxicity of the 60-nm PS-NH(2) nanomaterial in E. coli are destabilization of the outer membrane and production of reactive oxygen species. The methodology reported herein should prove generally useful for identifying pathways that are involved in how cells respond to a broad range of nanomaterials and for determining the mechanisms of cellular toxicity of different types of nanomaterials.     

Analysis and assessment of a nanoparticle seeded small scale absorption refrigeration system driven by a low‐grade waste heat source

The thermoeconomic behaviour of a nanoparticle seeded single effect LiBr‐H₂O absorption refrigeration system (ARS) is investigated for a small scale application. In the proposed method, alumina nanoparticles with volume concentrations of 3%, 5%, and 7% are dispersed into an aqua lithium bromide solution. The multiobjective heat transfer search algorithm is employed to examine the design trade‐off between the coefficient of performance (COP) and total annualized cost (TAC). To analyze the overall performance of the system, the influence of five design parameters, namely the temperatures of the generator, absorber, evaporator, condenser and heat exchanger pipe diameter, are studied. It is found that with an increase in the COP, the TAC of the system is initially raised marginally, and after that, raised rigorously with further increment. The comparative results indicate that the COP and TAC of the nanofluid based ARS system are increased by about 7% and decreased by about 3.2%, respectively, corresponding to the Pareto points of the base ARS system. A lower break‐even point of about 2.6 years is achieved for the ARS system containing nanoparticles compared to the base ARS system. Overall, the ARS system containing 5% nanoparticles is the best solution from a thermodynamic and economic point of view.     

Estimation of gross calorific value of coals using artificial neural networks

The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels.     

Increasing ionic conductivity and mechanical strength of a plastic electrolyte by inclusion of a polymer

In this contribution we present a soft matter solid electrolyte which was obtained by inclusion of a polymer (polyacrylonitrile, PAN) in LiClO₄/LiTFSI-succinonitrile (SN), a semi-solid organic plastic electrolyte. Addition of the polymer resulted in considerable enhancement in ionic conductivity as well as mechanical strength of LiX-SN (X = ClO₄, TFSI) plastic electrolyte. Ionic conductivity of 92.5%-[1 M LiClO₄-SN]:7.5%-PAN (PAN amount as per SN weight) composite at 25 °C recorded a remarkably high value of 7 × 10⁻³ Ω⁻¹ cm⁻¹, higher by few tens of order in magnitude compared to 1 M LiClO₄-SN. Composite conductivity at sub-ambient temperature is also quite high. At −20 °C, the ionic conductivity of (100 − x)%-[1 M LiClO₄-SN]:x%-PAN composites are in the range 3 × 10⁻⁵-4.5 × 10⁻⁴ Ω⁻¹ cm⁻¹, approximately one to two orders of magnitude higher with respect to 1 M LiClO₄-SN electrolyte conductivity. Addition of PAN resulted in an increase of the Young's modulus (Y) from Y → 0 for LiClO₄-SN to a maximum of 0.4 MPa for the composites. Microstructural studies based on X-ray diffraction, differential scanning calorimetry and Fourier transform infrared spectroscopy suggest that enhancement in composite ionic conductivity is a combined effect of decrease in crystallinity and enhanced trans conformer concentration.