Showing papers by "Shiv Nadar University published in 2020"

Chemistry, Structures, and Advanced Applications of Nanocomposites from Biorenewable Resources.

Abstract: Researchers have recently focused on the advancement of new materials from biorenewable and sustainable sources because of great concerns about the environment, waste accumulation and destruction, and the inevitable depletion of fossil resources. Biorenewable materials have been extensively used as a matrix or reinforcement in many applications. In the development of innovative methods and materials, composites offer important advantages because of their excellent properties such as ease of fabrication, higher mechanical properties, high thermal stability, and many more. Especially, nanocomposites (obtained by using biorenewable sources) have significant advantages when compared to conventional composites. Nanocomposites have been utilized in many applications including food, biomedical, electroanalysis, energy storage, wastewater treatment, automotive, etc. This comprehensive review provides chemistry, structures, advanced applications, and recent developments about nanocomposites obtained from biorenewable sources.

Endohedrally Doped Cage Clusters

Abstract: The discovery of carbon fullerene cages and their solids opened a new avenue to build materials from stable cage clusters as “artificial atoms” or “superatoms” instead of atoms. However, cage clust...

Carbon-Based Polymer Nanocomposite for High-Performance Energy Storage Applications.

Abstract: In recent years, numerous discoveries and investigations have been remarked for the development of carbon-based polymer nanocomposites. Carbon-based materials and their composites hold encouraging employment in a broad array of fields, for example, energy storage devices, fuel cells, membranes sensors, actuators, and electromagnetic shielding. Carbon and its derivatives exhibit some remarkable features such as high conductivity, high surface area, excellent chemical endurance, and good mechanical durability. On the other hand, characteristics such as docility, lower price, and high environmental resistance are some of the unique properties of conducting polymers (CPs). To enhance the properties and performance, polymeric electrode materials can be modified suitably by metal oxides and carbon materials resulting in a composite that helps in the collection and accumulation of charges due to large surface area. The carbon-polymer nanocomposites assist in overcoming the difficulties arising in achieving the high performance of polymeric compounds and deliver high-performance composites that can be used in electrochemical energy storage devices. Carbon-based polymer nanocomposites have both advantages and disadvantages, so in this review, attempts are made to understand their synergistic behavior and resulting performance. The three electrochemical energy storage systems and the type of electrode materials used for them have been studied here in this article and some aspects for example morphology, exterior area, temperature, and approaches have been observed to influence the activity of electrochemical methods. This review article evaluates and compiles reported data to present a significant and extensive summary of the state of the art.

Cellulose Nanocrystals/Graphene Hybrids-A Promising New Class of Materials for Advanced Applications.

Abstract: With the growth of global fossil-based resource consumption and the environmental concern, there is an urgent need to develop sustainable and environmentally friendly materials, which exhibit promising properties and could maintain an acceptable level of performance to substitute the petroleum-based ones. As elite nanomaterials, cellulose nanocrystals (CNC) derived from natural renewable resources, exhibit excellent physicochemical properties, biodegradability and biocompatibility and have attracted tremendous interest nowadays. Their combination with other nanomaterials such as graphene-based materials (GNM) has been revealed to be useful and generated new hybrid materials with fascinating physicochemical characteristics and performances. In this context, the review presented herein describes the quickly growing field of a new emerging generation of CNC/GNM hybrids, with a focus on strategies for their preparation and most relevant achievements. These hybrids showed great promise in a wide range of applications such as separation, energy storage, electronic, optic, biomedical, catalysis and food packaging. Some basic concepts and general background on the preparation of CNC and GNM as well as their key features are provided ahead.

Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging

Abstract: Purpose To evaluate the trustworthiness of saliency maps for abnormality localization in medical imaging. Materials and Methods Using two large publicly available radiology datasets (SIIM-ACR Pneumothorax Segmentation and RSNA Pneumonia Detection), we quantified the performance of eight commonly used saliency map techniques in regards to their 1) localization utility (segmentation and detection), 2) sensitivity to model weight randomization, 3) repeatability, and 4) reproducibility. We compared their performances versus baseline methods and localization network architectures, using area under the precision-recall curve (AUPRC) and structural similarity index (SSIM) as metrics. Results All eight saliency map techniques fail at least one of the criteria and were inferior in performance compared to localization networks. For pneumothorax segmentation, the AUPRC ranged from 0.024-0.224, while a U-Net achieved a significantly superior AUPRC of 0.404 (p Conclusion We suggest that the use of saliency maps in the high-risk domain of medical imaging warrants additional scrutiny and recommend that detection or segmentation models be used if localization is the desired output of the network. Supplemental material is available for this article. Summary The use of saliency maps to interpret deep neural networks trained on medical imaging fails several key criteria for utility and robustness, highlighting the need for scrutiny before clinical application. Key Points Eight popular saliency map techniques were evaluated for their utility and robustness in interpreting deep neural networks trained on chest radiographs. All the saliency map techniques fail at least one of the criteria defined in the paper, indicating their use for high-risk medical applications to be problematic. Instead, the use of detection or segmentation models are recommended if localization is the ultimate goal of interpretation.

Cost effective liquid phase exfoliation of MoS2 nanosheets and photocatalytic activity for wastewater treatment enforced by visible light

Abstract: Scalable production of high-quality MoS2 nanosheets remains challenging for industrial applications and research in basic sciences. N-methyl-2pyrrolidine (NMP) is a commonly used solvent for exfoliation of MoS2 nanosheets having further disadvantage of slow volatility rate. The present study demonstrates a cost-effective facile chemical route to synthesize few-layer MoS2 nanosheets using acetone as a solvent and by varying bulk initial concentration of samples to scale up the production in large scale to fulfill the demand for potential applications. In our study, we aim to obtain stable growth of high quality few layer MoS2 nanosheets by long sonication times. Optical absorption spectra, Raman spectra, size of nanosheets and layer thickness of as-grown MoS2 nanosheets were found to be matching with those obtained from other synthesis methods. Effective photocatalytic performance of MoS2 nanosheets without being consumed as a reactant was experimented by decomposing Methylene Blue dye in aqueous solution under irradiation of visible light. This study provides an idea to synthesize low-cost, sustainable and efficient photocatalytic material in large scale for the next generation to control water pollution quite efficiently by protecting the environment from the contamination coming from these dyes.

Consumer buying behaviour and purchase intention of organic food: a conceptual framework

Abstract: The paper aims to investigate existing research in factors impacting organic food purchase with special reference to eco-labels and identify the relative influence of various determinants.,A conceptual framework is proposed of organic food buying behaviour after analysing a sample of 154,072 consumers reported in 91 research studies from 2001–2020. The factors are categorised into four categories on the basis of relatedness. In addition, the factors were analysed based on time, region and national economic status.,The impact of consumer psychographics, socio-demographic and product-related factor categories were found to be more pronounced compared to supply-related factor category. The results show that among individual factors like health concern, environment concern, knowledge and awareness, eco-labels and price followed by trust in organic food are the most important factors in organic food purchase. The findings suggest that eco-labels increase trust in organic food by reducing information asymmetry in consumers. However, there were differences in perception and factors importance between high-income economies and emerging economies.,The study is unique, as it analyses secondary research based on criteria of high-income economies and emerging economies. The conceptual framework can also be incorporated further into different cognitive models like the theory of planned behaviour.

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs.

Abstract: Diabetic Macular Edema (DME) is an advanced stage of Diabetic Retinopathy (DR) and can lead to permanent vision loss. Currently, it affects 26.7 million people globally and on account of such a huge number of DME cases and the limited number of ophthalmologists, it is desirable to automate the diagnosis process. Computer-assisted, deep learning based diagnosis could help in early detection, following which precision medication can help to mitigate the vision loss. Method In order to automate the screening of DME, we propose a novel DMENet Algorithm which is built on the pillars of Convolutional Neural Networks (CNNs). DMENet analyses the preprocessed color fundus images and passes it through a two-stage pipeline. The first stage detects the presence or absence of DME whereas the second stage takes only the positive cases and grades the images based on severity. In both the stages, we use a novel Hierarchical Ensemble of CNNs (HE-CNN). This paper uses two of the popular publicly available datasets IDRiD and MESSIDOR for classification. Preprocessing on the images is performed using morphological opening and gaussian kernel. The dataset is augmented to solve the class imbalance problem for better performance of the proposed model. Results The proposed methodology achieved an average Accuracy of 96.12%, Sensitivity of 96.32%, Specificity of 95.84%, and F-1 score of 0.9609 on MESSIDOR and IDRiD datasets. Conclusion These excellent results establish the validity of the proposed methodology for use in DME screening and solidifies the applicability of the HE-CNN classification technique in the domain of biomedical imaging.

Migration and reverse migration in the age of covid-19

Abstract: The notion of the “migrant” in the current capitalist times and the world of migrants in it are explored The source to destination streams of migrant labour is outlined, and it is then argued that reverse migration will perhaps usher in the greatest crisis in the rural landscape of India, for which we are not yet prepared © 2020 Economic and Political Weekly All rights reserved

Solvent Organization and Rate Regulation of a Menshutkin Reaction by Oriented External Electric Fields are Revealed by Combined MD and QM/MM Calculations.

Abstract: When and how do external electric fields (EEFs) lead to catalysis in the presence of a (polar or nonpolar) solvent? This is the question that is addressed here using a combination of molecular dyna...

PEA2SnBr4: a water-stable lead-free two-dimensional perovskite and demonstration of its use as a co-catalyst in hydrogen photogeneration and organic-dye degradation

Abstract: A novel lead-free 2D perovskite, namely PEA2SnBr4, shows impressive water-resistance by retaining its original crystal structure and optical properties when placed in contact with water. Such key properties have been advantageously used for the fabrication of a novel co-catalytic system by coupling PEA2SnBr4 with graphitic carbon nitride. PEA2SnBr4/g-C3N4 composites at different metal halide perovskite loadings (5 and 15 wt%) have been prepared and tested in hydrogen photogeneration in an aqueous environment and organic dye degradation (methylene blue). The results show an impressive enhancement of H2 production of the composite with respect to the two separate components with hydrogen evolution rates up to 1600 μmol g−1 h−1 and analogous improvements in the efficiency of methylene blue degradation. The present results, providing a novel water-resistant perovskite and co-catalytic system, pave the way towards the safe, efficient and real use of metal halide perovskites in catalysis.

Q-Learning based Maximum Power Extraction for Wind Energy Conversion System With Variable Wind Speed

Abstract: This paper presents an intelligent wind speed sensor less maximum power point tracking (MPPT) method for a variable speed wind energy conversion system (VS-WECS) based on a Q-Learning algorithm. The Q-Learning algorithm consists of Q-values for each state action pair which is updated using reward and learning rate. Inputs to define these states are electrical power received by grid and rotational speed of the generator. In this paper, Q-Learning is equipped with peak detection technique, which drives the system towards peak power even if learning is incomplete which makes the real time tracking faster. To make the learning uniform, each state has its separate learning parameter instead of common learning parameter for all states as is the case in conventional Q-Learning. Therefore, if half learned system is running at peak point, it does not affect the learning of unvisited states. Also, wind speed change detection is combined with proposed algorithm which makes it eligible to work for varying wind speed conditions. In addition, the information of wind turbine characteristics and wind speed measurement is not needed. The algorithm is verified through simulations and experimentation and also compared with perturbation and observation (P&O) algorithm.

Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends

Abstract: The rapid development of remote sensing has made it possible to study environmental processes and changes in agriculture and also to provide important assistance in relevant practices, even operationally. This chapter describes the latest developments in remote sensing for precision agriculture with particular emphasis placed on the use of hyperspectral sensors. This chapter provides practical information regarding the identification of research challenges, limitations, and advantages of different platforms and sensors for precision agriculture. Hyperspectral remote sensing (HRS) is more effective as compared to multispectral remote sensing because it records radiation in narrow contiguous spectral channels reflected from any feature or target. More accurate spectral information retrieved using HRS can be combined with other techniques to retrieve useful information for precision agriculture. The chapter includes information about HRS sensors and also includes a discussion on the advancement and challenges of spaceborne satellites faced during agriculture monitoring. It concludes with summarizing the hurdles faced during agriculture research using hyperspectral data discussing possible pathways in which relevant research should be directed.

Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative

Abstract: Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.

DNA methylation reprogramming during seed development and its functional relevance in seed size/weight determination in chickpea.

Abstract: Seed development is orchestrated via complex gene regulatory networks and pathways. Epigenetic factors may also govern seed development and seed size/weight. Here, we analyzed DNA methylation in a large-seeded chickpea cultivar (JGK 3) during seed development stages. Progressive gain of CHH context DNA methylation in transposable elements (TEs) and higher frequency of small RNAs in hypermethylated TEs during seed development suggested a role of the RNA-dependent DNA methylation pathway. Frequency of intragenic TEs was higher in CHH context differentially methylated region (DMR) associated differentially expressed genes (DEGs). CG context hyper/hypomethylation within the gene body was observed for most of DMR-associated DEGs in JGK 3 as compared to small-seeded chickpea cultivar (Himchana 1). We identified candidate genes involved in seed size/weight determination exhibiting CG context hypermethylation within the gene body and higher expression in JGK 3. This study provides insights into the role of DNA methylation in seed development and seed size/weight determination in chickpea. Rajkumar et al. report progressive gain of CHH context DNA methylation in transposable elements during seed development in chickpea, of which hypermethylation is associated with small RNAs. The candidate genes that determine seed size/weight in chickpea show CG context hypermethylation in the gene body and higher expression in large-seeded cultivar.

Induced Vacancy-Assisted Filamentary Resistive Switching Device Based on RbPbI3-xClx Perovskite for RRAM Application.

Abstract: Halide perovskite (HP) materials are actively researched for resistive switching (RS) memory devices due to their current-voltage hysteresis along with low-temperature processability, superior charge mobility, and simple fabrication. In this study, all-inorganic RbPbI3 perovskite has been doped with Cl in the halide site and incorporated as a switching media in the Ag/RbPbI3-xClx/ITO structure, since pure RbPbI3 is nonswitchable. Five compositions of the RbPbI3-xClx (x = 0, 0.3, 0.6, 0.9, and 1.2) films are fabricated, and the conductivity was found to be increasing upon increase in Cl concentration, as revealed by dielectric and I-V measurements. The device with a 20% chloride-substituted film exhibits a higher on/off ratio, extended endurance, long retention, and high-density storage ability. Finally, a plausible explanation of the switching mechanism from iodine vacancy-mediated growth of conducting filaments (CFs) is provided using conductive atomic force microscopy (c-AFM). The c-AFM measurements reveal that pure RbPbI3 is insulating in nature, whereas Cl-doped films demonstrate resistive switching behavior.

Microwave-Assisted Rapid Synthesis of Reduced Graphene Oxide-Based Gum Tragacanth Hydrogel Nanocomposite for Heavy Metal Ions Adsorption

Abstract: Reduced graphene oxide (RGO) was synthesized in this research via Tour's method for the use of filler in the hydrogel matrix. The copolymerization of N,N-dimethylacrylamide (DMA) onto the gum tragacanth (GT) was carried out to develop gum tragacanth-cl-N,N-dimethylacrylamide (GT-cl-poly(DMA)) hydrogel using N,N'-methylenebisacrylamide (NMBA) and potassium persulfate (KPS) as cross-linker and initiator correspondingly. The various GT-cl-poly(DMA) hydrogel synthesis parameters were optimized to achieve maximum swelling of GT-cl-poly(DMA) hydrogel. The optimized GT-cl-poly(DMA) hydrogel was then filled with RGO to form reduced graphene oxide incorporated gum tragacanth-cl-N,N-dimethylacrylamide (GT-cl-poly(DMA)/RGO) hydrogel composite. The synthesized samples were used for competent adsorption of Hg2+ and Cr6+ ions. Fourier transform infrared, X-ray powder diffraction, field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy were used to characterize the gum tragacanth-cl-N,N-dimethylacrylamide hydrogel and reduced graphene oxide incorporated gum tragacanth-cl-N,N-dimethylacrylamide hydrogel composite. The experiments of adsorption-desorption cycles for Hg2+ and Cr6+ ions were carried out to perform the reusability of gum tragacanth-cl-N,N-dimethylacrylamide hydrogel and reduced graphene oxide incorporated gum tragacanth-cl-N,N-dimethylacrylamide hydrogel composite. From these two samples, reduced graphene oxide incorporated gum tragacanth-cl-N,N-dimethylacrylamide exhibited high adsorption ability. The Hg2+ and Cr6+ ions adsorption by gum tragacanth-cl-N,N-dimethylacrylamide and reduced graphene oxide incorporated gum tragacanth-cl-N,N-dimethylacrylamide were best suited for pseudo-second-order kinetics and Langmuir isotherm. The reported maximum Hg2+ and Cr6+ ions adsorption capacities were 666.6 mg g-1 and 473.9 mg g-1 respectively.