Separation of water–isopropyl alcohol mixtures with novel hybrid composite membranes

New‐fangled hybrid composite membranes were prepared by the incorporation of 5, 10, and 15 mass % NaY–zeolite particles into blend membranes of carboxymethyl cellulose (CMC)‐g‐acrylamide/sodium alginate (NaAlg) and crosslinked with glutaraldehyde. The pervaporation (PV) separation performance of the hybrid composite membranes was explored for the dehydration of isopropyl alcohol from their aqueous solutions at 30°C. The effect of NaY–zeolite in these blend membranes was investigated in PV dehydration. From the experimental results, we found that NaY particles could be intercalated in the aqueous polymer solution. The obtained results show that both the flux and selectivity increased simultaneously with increasing zeolite content in the membrane. This was explained on the basis of an enhancement of the hydrophilicity, selective adsorption, and molecular sieving action by the creation of pores in the membrane matrix. The membranes were characterized by differential scanning calorimetry, scanning electron microscopy, and Fourier transform infrared spectroscopy. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2012     

Performance enhancement of extreme learning machine for multi-category sparse data classification problems

This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for multi-category sparse data classification problems. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for sparse data classification problem depends critically on three free parameters. They are, the number of hidden neurons, the input weights and the bias values which need to be optimally chosen. Selection of these parameters for the best performance of ELM involves a complex optimization problem.In this paper, we present a new, real-coded genetic algorithm approach called ‘RCGA-ELM’ to select the optimal number of hidden neurons, input weights and bias values which results in better performance. Two new genetic operators called ‘network based operator’ and ‘weight based operator’ are proposed to find a compact network with higher generalization performance. We also present an alternate and less computationally intensive approach called ‘sparse-ELM’. Sparse-ELM searches for the best parameters of ELM using K-fold validation. A multi-class human cancer classification problem using micro-array gene expression data (which is sparse), is used for evaluating the performance of the two schemes. Results indicate that the proposed RCGA-ELM and sparse-ELM significantly improve ELM performance for sparse multi-category classification problems.     

Electrospun Nanofibers: Materials,Synthesis Parameters,and Their Role in Sensing Applications

Since the last decade, electrospinning is garnering more attention in the scientific research community, industries, applications like sensing (glucose, H₂O₂, dopamine, ascorbic acid, uric acid, neurotransmitter, etc.), biomedical applications (wound dressing, wound healing, skin, nerve, bone tissue engineering, and drug delivery systems), water treatment, energy harvesting, and storage applications. This review paper provides a brief overview of the electrospinning method, history of the electrospinning, factors affecting the electrospun nanofibers, and their morphology with different materials and composites (metals, metal oxides, 2D material, polymers and copolymers, carbon-based materials, etc.) used in the electrospinning technique with optical spinning parameters. Moreover, this paper deliberates the application of electrospun nanofibers and fibrous mats for sensing (electrochemical, optical, fluorescence, colorimetric, mechanical, photoelectric, mass sensitive change, resistive, ultrasensitive, etc.) in most illustrative representations. In the end, the challenges, opportunities of the electrospun nanofibers, and new direction for future progress are also discussed.     

Importance of Adaptive Photometric Augmentation for Different Convolutional Neural Network

Existing segmentation and augmentation techniques on convolutional neural network (CNN) has produced remarkable progress in object detection. However, the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process, along with the context of the individual CNN algorithm. In this paper, we investigate the effect of a photometric variation like brightness and sharpness on different CNN. We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation. Our approach has been justified by the experimental result obtained from the PASCAL VOC 2007 dataset, with object detection CNN algorithms such as YOLOv3 (You Only Look Once), Faster R-CNN (Region-based CNN), and SSD (Single Shot Multibox Detector). Each CNN model shows performance loss for varying sharpness and brightness, ranging between −80% to 80%. It was further shown that compared to random augmentation, the augmented dataset with weak photometric changes delivered high performance, but the photometric augmentation range differs for each model. Concurrently, we discuss some research questions that benefit the direction of the study. The results prove the importance of adaptive augmentation for individual CNN model, subjecting towards the robustness of object detection.     

Junction properties of metal/polypyrrole Schottky barriers

Schottky barriers of the type Au/polypyrrole/Al (or In) were made in sandwich configuration. The conductivity of polypyrrole was tuned to be on the order of 10⁻³ ohm⁻¹ cm⁻¹ by its electrodeposition from a novel ambient temperature ternary eutectic melt consisting of acetamide, urea, and ammonium nitrate. The rectification characteristics were obtained from the current–voltage and capacitance–voltage measurements at room temperature. The analysis of data using thermionic emission theory gave improved values for the junction parameters of ideality factor, reverse saturation current, rectification ratio, and barrier potential when compared to the previously reported values for this polymer. Between Al and In metals used for the junction formation, the diode formed with Al metal is found to show better performance. The energy gap and work function of polypyrrole were also estimated. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 81: 2127–2135, 2001     

Optimization of Fruit Punch Using Mixture Design

ABSTRACT:  A highly acceptable dehydrated fruit punch was developed with selected fruits, namely lemon, orange, and mango, using a mixture design and optimization technique. The fruit juices were freeze dried, powdered, and used in the reconstitution studies. Fruit punches were prepared according to the experimental design combinations (total 10) based on a mixture design and then subjected to sensory evaluation for acceptability. Response surfaces of sensory attributes were also generated as a function of fruit juices. Analysis of data revealed that the fruit punch prepared using 66% of mango, 33% of orange, and 1% of lemon had highly desirable sensory scores for color (6.00), body (5.92), sweetness (5.68), and pleasantness (5.94). The aroma pattern of individual as well as combinations of fruit juices were also analyzed by electronic nose. The electronic nose could discriminate the aroma patterns of individual as well as fruit juice combinations by mixture design. The results provide information on the sensory quality of best fruit punch formulations liked by the consumer panel based on lemon, orange, and mango.     

Electrochemical preparation and characterization of conducting copolymers: poly(pyrrole-co-indole)

Copolymerization of pyrrole and indole was achieved electrochemically in acetonitrile containing lithium perchlorate as supporting electrolyte. The copolymer compositions can be altered by varying the comonomer feed ratios during preparation. The copolymers were characterized by conductivity measurements, cyclic voltammetry, scanning electron microscopy, UV-visible and IR spectroscopic techniques. A possible scheme for the copolymerization mechanism has been suggested.     

Application of poly(<Emphasis Type="Italic">o</Emphasis>-phenylenediamine) in rechargeable cells

The electrochemical synthesis of poly(o -phenylenediamine) (PoPD) from an aqueous medium was suitably modified by controlling the switching potential to enhance the growth of the polymer. The charge–discharge data for the cell Zn/1 M ZnSO₄ (pH 4)/PoPD are presented. The polymer was modified by incorporating Pt microparticles into its matrix during electropolymerization. The PoPD-Pt composite electrode was also characterized as a cathode active material in aqueous cells.     

Highly selective custom-made chitosan based membranes with reduced fuel permeability for direct methanol fuel cells

Custom-made nanocomposite proton exchange membranes (PEMs) are fabricated using the blends of sulfonated chitosan (S-Chitosan) and sulfonated graphene oxide (SGO) nanosheets for direct methanol fuel cells (DMFCs). Sulfonation of chitosan and GO are carried out by 1,3-propane sultone and sulfanilic acid, respectively. Scanning electron microscope (SEM) with energy dispersive X-ray investigation revealed that the thick, folded and wrinkled sheet-like morphology of SGO and the existence of elemental sulfur. SEM and atomic force microscopy images showed the uniform dispersion of hydrophilic SGO nanosheets. Besides the S-Chitosan/SGO membranes showed higher water uptake, swelling ratio and ion exchange capacity due to the enhancement in hydrophilicity. The modified PEMs displayed improvement in proton conductivity since the ion-exchangeable sulfonic acid groups facilitate the proton conduction and effectively resist the methanol permeability by forming a strong hydrogen bond network with chitosan and thus diminish the void volume. Particularly, S-Chiotsan-1 membrane showed superior proton conductivity of 4.86 × 10⁻³ Scm⁻¹ at (25°C), selectivity of 1.89 × 10⁵ Scm⁻³ s and lesser methanol permeability of 2.57 × 10⁻⁸ cm²s⁻¹. Overall results suggest that the S-Chitosan/SGO membranes found to be a suitable alternate for Nafion® in DMFCs.     

Reinforcement of ball shaped MoS2 nanoparticles in epoxy resin

The present work involves the synthesis of molybdenum disulfide (MoS2) nanoparticles by annealing the precursor obtained from simple reflux method. XRD, FESEM and HRTEM confirmed the formation of 2H-MoS2 with ball shaped particles, where some of them possess coalesced dumbbell morphology. The reinforcement of polysulphide modified epoxy resin (PSER) by MoS2 with varying amounts from 0.150 to 0.200 wt% provides unique combination of the improved thermal stability, tribological and mechanical properties. XRD studies indicate interaction between the sulphur containing nanoparticles and the epoxy resin. Maximum improvements in tensile strength (440%) and toughness (534%) are observed with ball shaped MoS2 nanoparticles (0.150 wt%)/PSER composite. Also the coefficient of friction and wear resistance show improvements of 60 and 78% respectively for 0.175 wt% loading in PSER compared to the neat resin matrix. Thermal stability is found to be improved maximum by 23 degrees C, when 5% weight loss is taken as a point of comparison. Similar studies on synthetic microcrystalline MoS2 filled PSER show that improvements in all these properties are very inferior.