Sudeshna Bagchi

Classification of heavy metal ions present in multi-frequency multi-electrode potable water data using evolutionary algorithm

Access to potable water for the common people is one of the most challenging tasks in the present era. Contamination of drinking water has become a serious problem due to various anthropogenic and geogenic events. The paper demonstrates the application of evolutionary algorithms, viz., particle swan optimization and genetic algorithm to 24 water samples containing eight different heavy metal ions (Cd, Cu, Co, Pb, Zn, Ar, Cr and Ni) for the optimal estimation of electrode and frequency to classify the heavy metal ions. The work has been carried out on multi-variate data, viz., single electrode multi-frequency, single frequency multi-electrode and multi-frequency multi-electrode water samples. The electrodes used are platinum, gold, silver nanoparticles and glassy carbon electrodes. Various hazardous metal ions present in the water samples have been optimally classified and validated by the application of Davis Bouldin index. Such studies are useful in the segregation of hazardous heavy metal ions found in water resources, thereby quantifying the degree of water quality.

Bacteriorhodopsin–ZnO hybrid as a potential sensing element for low-temperature detection of ethanol vapour

Summary Zinc oxide (ZnO) and bacteriorhodopsin (bR) hybrid nanostructures were fabricated by immobilizing bR on ZnO thin films and ZnO nanorods. The morphological and spectroscopic analysis of the hybrid structures confirmed the ZnO thin film/nanorod growth and functional properties of bR. The photoactivity results of the bR protein further corroborated the sustainability of its charge transport property and biological activity. When exposed to ethanol vapour (reducing gas) at low temperature (70 °C), the fabricated sensing elements showed a significant increase in resistivity, as opposed to the conventional n-type behaviour of bare ZnO nanostructures. This work opens up avenues towards the fabrication of low temperature, photoactivated, nanomaterial–biomolecule hybrid gas sensors.

Optical Fiber Sensor With ZnO Hierarchical Nano-Structures Grown on Electrospun ZnO Mat Base on the Sensor for Trace Level Detection of Volatile Amines

This article demonstrates an optical fiber sensor probe coated with hierarchical nanostructures of zinc oxide (ZnO) for trace level detection of volatile amines. The nanostructure is grown by hydrothermal process on a multimode optical fiber probe coated with ZnO nanofibrous mat prepared by electrospinning technique. The high surface area of the hierarchical structures facilitated diffusion of amines, which, on interaction with ZnO surface, changes the effective refractive index of the optical fiber probe. The modulation of the transmitted intensity through the optical sensor probe in presence of the target analyte is monitored for sensing the amine. An increase in absorbance is observed in proportion to amine concentrations with the emergence of a broad peak at around 500nm. The sensor is able to detect as low as 25ppb of different volatile amines such as ammonia, triethylamine, aniline, and ethylenediamine with responses of 1, 0.65, 0.75, and 0.5, respectively. The sensor is further demonstrated for detection of volatile amines released during protein degradation in salmon fish to show its usability for real time applications.