Spray dryer processed graphene oxide/reduced graphene oxide for high-performance supercapacitor

This study deals with the utility of mini spray dryer process to improve the dispersibility, of graphene oxide(GO) and its application for high-performance supercapacitor. Initially, the neutral solution of GO was obtained using the modified Hummer's method. After this, the prepared GO solution was processed by mini spray dryer to obtain a more purified, lighter, and dispersed form of GO which is named as spray dryer processed GO (SPGO). The SPGO thus obtained showed excellent dispersibility behavior with various solvents, which is not found in case of conventional oven drying. Furthermore, utility of SPGO and its reduced form (r-SPGO) for supercapacitor applications have been investigated. Results obtained from the cyclic voltammetry(CV) analysis, impedance, and charge-discharge behavior of supercapacitor fabricated using r-SPGO shows enhanced features. Therefore, the simple spray dried GO and its reduced form, that is, r-SPGO can be utilized as a potential candidate for the supercapacitor application. Herein, as synthesized SPGO exhibited the specific capacitance of 12.07 and 37.6 F/g with PVA-H₃PO₄ and 1 mol/L H₃PO₄, respectively, at a scan rate of 5 mV/s. On the other hand, reduced form of SPGO, that is, r-SPGO showed the specific capacitance of 27.16 and 230 F/g with PVA-H₃PO₄ and 1 mol/L H₃PO₄, respectively.     

Effect of 100 KeV Ar+ ion beam irradiation on ZnO thin films – Influence of morphology vis-a-vis electrode/electrolyte interface and its impact on photoelectrochemical water splitting

The present study attempts quantitative determination of changes in the morphological surface features viz. fractal dimension, lower and upper cut off length scale through Power Spectral Density analysis prior to and after irradiation of 100 KeV Ar⁺ ion beam at incidence angles of 0°, 40° and 60° on ZnO thin films. All the unirradiated and irradiated samples are subjected to photoelectrochemical characterization and a correlation between photoelectrochemical performance and morphological parameters is established. Sample irradiated at 40° angle at the fluence of 5 × 10¹⁶ ions/cm² is found to possess maximum fractal dimension of 2.72, lower and upper cut off length scale of 3.16 nm and 63.00 nm respectively. This sample exhibits maximum photocurrent density of 3.19 mA/cm² and applied bias photon-to-current efficiency of 1.12% at 1.23 V/RHE. Hydrogen gas collected for duration of 1 h for the same sample was ~4.83 mLcm^?2.     

Carbon-based TiO2-x heterostructure nanocomposites for enhanced photocatalytic degradation of dye molecules

Application of brown titanium dioxide (TiO_2-x) and its modified composite forms in the photocatalytic decomposition of organic pollutants in the environment is a promising way to provide solutions for environmental redemption. Herein, we report the synthesis of effective and stable TiO_2-x nanoparticles with g-C₃N₄, RGO, and multiwalled carbon nanotubes (CNTs) using a simple hydrothermal method. Among all the as-synthesized samples, excellent photocatalytic degradation activity was observed for RGO-TiO_2-x nanocomposite with high rate constants of 0.075 min^?1_, 0.083 min^?1 and 0.093 min^?1 for methylene blue, rhodamine-B, and rosebengal dyes under UV–Visible light irradiation, respectively. The altered bandgap (1.8 eV) and the large surface area of RGO-TiO_2-x nanocomposite impacts on both absorption of visible light and efficiency of photogenerated charge electron (e^?)/hole (h⁺) pair separation. This resulted in enhanced photocatalytic property of carbon-based TiO_2-x nanocomposites. A systematic study on the influence of different carbon nanostructures on the photocatalytic activity of brown TiO_2-x is carried out.     

A machine learning system for identifying transmembrane domains from amino acid sequences

We present our machine learning system, that uses inductive logic programming techniques to learn how to identify transmembrane domains from amino acid sequences. Our system facilitates the use of operators such as ‘contains’, that act on entire sequences, rather than on individual elements of a sequence. The prediction accuracy of our new system is around 93%, and this compares favourably with earlier results. This work was carried out with the support of a research grant from ISIS, Fujitsu Laboratories.     

Characterization of ZnS:Cu, Br electroluminescent phosphor prepared by new route

Synthesis of electroluminescent ZnS:Cu, Br phosphor by a number of routes has been presented along with their brightness-voltage, brightness-frequency, brightness-waveform and spectral energy distribution studies. The sample fired in N₂ atmosphere with aluminum and bromine shows predominantly green emission with a peak around 530 nm whereas the sample prepared under H₂S and HBr shows the broadest emission spectrum with multiple peaks. These peaks may arise out of different possible bands of copper, self-activated luminescence of ZnS and association of copper with some of the donor levels formed because of the higher reactivity of HBr. All the samples have been found to obey the relation B=B₀ exp.(−b/V^0.5) which has been discussed using bipolar tunnel emission model. The frequency variation of brightness is linear. Samples containing bromine show multiple secondary peaks indicating that bromine helps in formation of multiple shallow traps.     

Oxidation of some aliphatic polyols on anodically deposited MnO2

Galvanostatic steady state current potential measurements were carried out for oxidation of a series of aliphatic alcohols having varying number of hydroxyl groups. The anodically deposited layer of MnO₂ on platinum was used as the electrode material. The deposit was characterised by scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDAX) and electrode potential measurements. The catalytic role of MnO₂ in the electro-oxidation of alcohols was indicated by the chronopotentiograms and the cyclic voltammograms. An analysis of the electrochemical data indicated a catalytic EC mechanism in which Mn (V) is generated electrochemically and consumed chemically in succession. Based on this and the hydrogen bonding interaction between alcoholic hydroxyl groups and MnO₂ layer, a mechanism was proposed which accounts for the variation in the observed electrochemical reaction orders. Tafel behaviour was found to be followed only approximately. Current efficiency of the electrochemical oxidation of polyols was studied. Replacement of platinum by carbon as current collector was found to leave the electrocatalytic activity of the MnO₂ deposit practically unaltered.     

Chitosan-based magnetic molecularly imprinted polymer: synthesis and application in selective recognition of tricyclazole from rice and water samples

A tricyclazole selective chitosan/Fe₃O₄ magnetic molecularly imprinted polymer (MMIP) was synthesized using non-covalent binding polymerization involving methacrylic acid (MAA) as functional monomer, divinylbenzene (DVB-80) as crosslinker, 2,2'-azobisisobutyronitrile as initiator, acetonitrile/toluene (75:25, v/v) as porogenic solvent and tricyclazole as template. Surface morphology and magnetic characterization of the prepared imprinted and non-imprinted polymers were done using scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectrometry and vibrating sample magnetometry, respectively. The adsorption kinetic data fitted best in pseudo-second-order model. The adsorption equilibrium was achieved in 30 min and the maximum binding capacity was 4579.9 µg/g. The Freundlich isotherm model was found suitable for explaining the binding isotherm data (R² > 0.99). Negative values of thermodynamic parameters ∆G (Gibb’s free energy), ∆H (enthalpy), and ∆S (entropy) revealed exothermic and spontaneous nature of adsorption processes. It also revealed decreased randomness at the solid–liquid interface during sorption. The scatchard plot analysis suggested heterogeneity of binding sites on MMIPs. The molecular recognition selectivity of MMIPs towards tricyclazole was much higher, as compared to its structural analogues, tebuconazole (α = 28.58) and hexaconazole (α = 37.16). The MMIPs were successfully applied to separate and enrich tricyclazole from fortified samples of rice and water, with a recovery percentage of 89.4% and 90.9%, respectively. These reusable imprinted polymers possessing high selectivity and specificity can be utilized as an adsorbent for solid-phase extraction in sample preparation for tricyclazole residue analysis in complex environmental matrices.

     

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.