Exploration on Solvatochromic Sensing Probe of Phenol Blue for L-Lactic Acid Detection: Effective Strategy to Obtain Reliable Capacitive Sensor

This study systematically investigates a capacitive sensor applied with phenol blue (PhB)-based sensing medium for detection of L-lactic acid (LA), as a health monitoring indicator. PhB is a substance with solvatochromic effect, inducing the change in capacitance by exposure to polar molecules. However, the capacitive LA sensor with a flat-structured PhB/polyvinylchloride (PVC) composite-sensing medium is observed to have a problem in that sensing capacitance variation saturate quickly with increasing the LA solution concentration. This main cause can be analyzed that the interaction of proton from LA molecule with the lone pair electrons of the PhB molecule acts as a major factor on the sensing characteristics rather than the solvatochromic behavior of PhB molecule. Therefore, a strategy is adopted to introduce a porous structure to the PhB/PVC composite-sensing medium to maximize the interaction of PhB with protons, which is implemented through solvent and non-solvent exchange methods. Consequently, the sensitivity and linearity of the porous-structured LA sensor are 2.99 pF mm ⁻¹ and 0.966 over LA concentrations ranging from 0 to 100 mm , respectively, which is a significant improvement over that of the flat-structured one. Notably, the sensing performance remained unchanged even after a month of storage under normal ambient conditions.     

Nitration of nitrobenzene at high‐concentrations of sulfuric acid: Mass transfer and kinetic aspects

This article reports studies on mass transfer and kinetics of nitration of nitrobenzene at high concentrations of sulfuric acid in a batch reactor at different temperatures. The effects of concentration of sulfuric acid, speed of stirring, and temperature on mass transfer coefficient were investigated. The kinetics of nitration under homogenized conditions was studied at different sulfuric acid concentrations at these temperatures. The reaction rate constants were determined. The variation of rate constant with sulfuric acid concentration was explained by the M_c function. The activation energies of the reactions were determined from the Arrhenius plots. The regimes of the reactions were determined using the values of the mass transfer coefficients and the reaction rate constants. A model was developed for simultaneous mass transfer and chemical reaction in the aqueous phase. The yields of the three isomers of dinitrobenzene were determined, and the variation of isomer distribution with sulfuric acid concentration and temperature was analyzed. This work demonstrates that more than 90% conversion of nitrobenzene is possible at high‐sulfuric acid concentrations resulting in high yield of the product even at moderate temperatures and at low speeds of stirring. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Thermal and electrical properties of carbon nanotubes based polysulfone nanocomposites

Carbon nanotubes (CNTs)-reinforced polysulfone (PSU) nanocomposites were prepared through solution mixing of PSU and different weight percent of multi-walled carbon nanotubes (MWCNTs). Thermal properties of nanocomposites were characterized using thermo-gravimetric analysis (TGA) and differential scanning calorimetry (DSC). TGA studies revealed an increase in thermal stability of the PSU/MWCNTs nanocomposites, which is due to the hindrance of the nanodispered carbon nanotubes to the thermal transfer in nanocomposites and also due to higher thermal stability of CNTs. Morphological properties of nanocomposites were characterized by high resolution transmission electron microscopy (HRTEM) and field emission scanning electron microscope (FESEM). The influence of CNTs loading on electrical properties of PSU/MWCNTs nanocomposites was studied by the measurement of AC and DC resistivity. Dielectric study of nanocomposites was carried out at different frequencies (10 Hz–1 MHz) by using LCR meter. An increase in dielectric constant and dielectric loss was observed with increase in CNTs content, which is due to the interfacial polarization between conducting CNTs and PSU.     

Crosstalk overshoot/undershoot analysis and its impact on gate oxide reliability in multi-wall carbon nanotube interconnects

The work in this paper analyzes the crosstalk effects in Multi-wall Carbon Nanotube (MWCNT) based interconnect systems, and its impact on the reliability of the gate oxide of MOS devices. The electrical circuit parameters for interconnect are calculated using the existing models of MWCNT and the equivalent circuit has been developed to perform the crosstalk analysis. The crosstalk induced overshoot/undershoots have been estimated and the effect of the overshoot/undershoots on the gate oxide reliability is calculated in terms of failure-in-time (FIT) rate of the MOS devices. Single, double, and bundle of MWCNTs are considered for the analysis. The results are compared with that of traditional Cu based interconnects. It has been found that the average failure rate due to crosstalk overshoot/undershoots is ??10 to 100 times less in MWCNT based interconnect of length between 10 ??m to 50 ??m as compared to the copper based interconnects. Our analysis shows the applicability of MWCNTs in future VLSI circuits from the perspective of gate oxide reliability. The results also reveal that single or double MWCNT of large diameter is better than bundle of MWCNTs of smaller diameter.     

Nano-BN/Nano-TiO2 and micro Mg(OH)2 loaded hybrid ethylene propylene diene monomer elastomer composites for outdoor high-voltage insulation application

This work deals with the synthesis, characterization of hybrid ethylene propylene diene monomer (EPDM) composites loaded with nano-boron nitride (nano-BN)/nano-titanium dioxide (nano-TiO₂) and micro Mg(OH)₂ particles for its suitability towards high-voltage insulation application. The elastomer samples were prepared by carefully dispersing the micro and nano fillers during the mastication process of EPDM polymer using a two roll mill, followed by vulcanization. The samples were characterized for mechanical, morphological, thermal, and electrical insulation properties. The highest tensile strength among the composite samples was noted for 1 phr nanoparticles loaded samples. Fourier Transform Infrared (FTIR) results show no change in the chemical moiety upon addition of nano-BN/nano-TiO₂ in EPDM composites. Enhancement in hydrophobicity is observed for 3 phr nano-TiO₂ loaded composites, which shows a maximum static contact angle of 110°. Meanwhile remarkable enhancement in the thermal conductivity and volume resistance of the composites are contributed to the addition of nano-BN, thereby achieving maximum dielectric breakdown voltage (i.e., ~21 kV/mm for EMB3). Scanning electron microscope images and atomic force microscopy (AFM) topography highlight that low concentration (i.e., 1 phr) based composites have homogeneous dispersion in matrix and excessive nano filler addition deteriorates properties by forming filler aggregates and increasing surface roughness.     

Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past data to detect and classify PD. With this motivation, the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine (CBO-OKELM) for diagnosis and classification of PD. The goal of the proposed CBO-OKELM technique is to identify whether PD exists or not. CBO-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection (CBO-FS) technique for optimal subset of features. In addition, Water Strider Algorithm (WSA) with Kernel Extreme Learning Machine (KELM) model is also developed for the classification of PD. CBO algorithm is used to elect the optimal set of features whereas WSA is utilized for parameter tuning of KELM model which altogether helps in accomplishing the maximum PD diagnostic performance. The experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%, 96.34%, 92.49%, and 92.36% on Speech PD, Voice PD, Hand PD Meander, and Hand PD Spiral datasets respectively.