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Properties, Synthesis, and Characterization of Cu-Based Nanomaterials
Copper-based nanomaterials offer a fascinating array of properties that make them pivotal in various technological applications. These materials, when scaled down to the nanoscale, exhibit enhanced electrical conductivity, surpassing their bulk counterparts. This book chapter primarily focuses on the properties, synthesis, and characterization of copper nanoparticles while also discussing metal and metal oxide nanoparticles. Their large surface-to-volume ratios enable efficient electron transport, making them valuable components in electronics and conductive inks for flexible devices. Furthermore, copper nanomaterials possess exceptional thermal conductivity, making them crucial for efficient heat management in electronics and advanced thermal interface materials. Copper and copper oxide have positive economic and environmental effects. Their catalytic properties render them important in diverse chemical reactions and as components in energy storage systems like batteries and supercapacitors. Additionally, the tunability of their optical properties makes them suitable for various photonic and optoelectronic applications, ranging from sensors to light-emitting devices. The multifaceted properties of copper-based nanomaterials continue to drive innovation across a broad spectrum of industries. 2024 American Chemical Society. -
Biomass-Based Functional Carbon Nanostructures for Supercapacitors
For the creation of next-generation biocompatible energy technologies, it is urgently necessary to examine environmentally acceptable, low-cost electrode materials with high adsorption, rapid ion/electron transit, and programmable surface chemistry. Because of their wide availability, environmentally friendly nature, and affordability, carbon electrode materials made from biomass have received a lot of interest lately. The biological structures they naturally possess are regular and accurate, and they can be used as templates to create electrode materials with precise geometries. The current study is primarily concerned with recent developments in research pertaining to biomass-derived carbon electrode materials for supercapacitor applications, including plant, fruit, vegetable, and microorganism-based carbon electrode materials. Also provided is a summary of alternative synthesis methods for the conversion and activation of biomass waste. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Risks and ethics of nanotechnology: an overview
Environmental nanotechnology is thought to be important to current environmental engineering and scientific techniques. The biomedical, textile, aerospace, manufacturing, cosmetics, oil, defense, agricultural, and electronics industries can all benefit from the use of nanotechnology to enhance a wide range of material properties, including physical, chemical, and biological properties. However, nanotechnology-based products or nanomaterials (e.g., nanofibers, nanowires, nanocomposites, and nanofilms) may be harmful to human health. Since nanomaterials are usually manufactured using novel manufacturing techniques and have a variety of sizes, shapes, and surface energies, there can also be uncertainties in their manufacture and handling. This chapter provides a detailed account of ethical issues related to nanotechnology, particularly environmental toxicity, risk management, health risk evolution, and environmental significance of nanomaterials. In addition, environmental challenges, toxic effect of nanoparticles on the environment, ethics of nanotechnology, and social, ecological, biological, and other legal issues are highlighted. The potential of nanomaterials in environmental remediation and their use in environmental protection is also emphasized. 2023 Elsevier Inc. All rights reserved. -
Renewable Musa Sapientum derived porous nano spheres for efficient energy storage devices
Biomass-based carbonaceous materials derived from Musa Sapientum have gained much attention in recent years for their application in energy storage devices, especially supercapacitors. In the present work, we synthesized carbonaceous material from banana peel as the biomass precursor by using a pyrolysis method carried out at various temperatures (600, 800, and 1000 C). The characterization of the prepared carbonaceous materials BP600, BP800 and BP1000 was done by using different characterization techniques such as FTIR, XRD, FE-SEM, and TEM, studies. The electrochemical study of the synthesized material was carried out by cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) electrochemical impedance spectroscopy (EIS). The supercapacitive performance of the material was studied using a 3-electrode system with 3M KOH as an electrolyte. As a result, the BP600 exhibited a better specific capacitance with higher energy and power densities along with a maximum cyclic stability of 16,000 cycles. To show the practical applicability of the material BP 600, two electrode system studies were carried out as well, which showed preferentially good values for specific capacitance with appreciable power and energy density values. The study provides us with a green approach for the fabrication of non-toxic, low-cost, and environmentally friendly potential porous carbonaceous electrode materials by converting bio-waste into a clean and renewable source of energy. 2024 The Author(s). Published by IOP Publishing Ltd. -
Zinc oxide/tin oxide nanoflower-based asymmetric supercapacitors for enhanced energy storage devices
Research on energy storage devices has focused on improving asymmetric supercapacitors (ASCs) by utilizing two different electrode materials. In this work, we have successfully prepared a unique material, ZnO/SnO2 nanoflower, via the hydrothermal method. Graphene oxide (GO) was synthesized by applying the modified Hummers' technique. The ZnO/SnO2 nanoflower was deposited on a polypyrrole (PPY) nanotube/graphene oxide composite (ZS/GP) in two steps: in situ chemical polymerization, followed by a hydrothermal method. Electrochemical properties of the prepared material nanocomposite were analyzed by applying cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS) techniques. An asymmetric supercapacitor (ASC) was constructed using ZS/GP nanocomposite as the positive electrode and Caesalpinia pod-based carbonaceous material as the negative electrode material, and its performance was investigated. As a result, the fabricated ASCs were found to have an excellent specific capacitance of 165.88 F g?1 at 1.4 V, with an energy density of 5.12 W h kg?1 and a power density of 2672 W kg?1. The prepared nanocomposite material for the ASC showed a cycle stability of 17k cycles at a current density of 5 A g?1. This study revealed that the electrode material ZS/GP nanocomposite is highly suitable for supercapacitor applications. The ASC device's extended cycle life experiments for 17k cycles produced a coulombic efficiency of 97% and a capacitance retention of 73%, demonstrating the promising potential of the electrode materials for greener as well as efficient energy storage applications while converting abundant bio waste into effective energy. 2024 The Royal Society of Chemistry. -
An Efficient Multi-Modal Classification Approach for Disaster-related Tweets
Owing to the unanticipated and thereby treacherous nature of disasters, it is essential to gather necessary information and data regarding the same on an urgent basis; this helps to get a detailed overview of the situation and helps humanitarian organizations prioritize their tasks. In this paper, "An Efficient Multi-Modal Classification Approach for Disaster-related Tweets,"the proposed framework based on Deep Learning to classify disaster-related tweets by analyzing text and image contents. The approach is based on Gated Recurrent Unit (GRU) and GloVe Embedding for text classification and VGG-16 network for image classification. Finally, a combined model is proposed using both text and image modules by the Late Fusion Technique. This portrays that the proposed multi-modal system performs significantly well in classifying disaster-related content. 2022 IEEE. -
Tracing the outer disk of NGC300: An ultraviolet view
We present an ultra-violet (UV) study of the galaxy NGC300 using GALEX far-UV (FUV) and near-UV (NUV) observations. We studied the nature of UV emission in the galaxy and correlated it with optical, HI and mid-infrared (3.6 ?m) wavelengths. Our study identified extended features in the outer disk, with the UV disk extending up to a radius of 12 kpc (> 2 R25). We estimated the FUV and NUV disk scale-length as 3.05 0.27 kpc and 2.66 0.20 kpc respectively. The scale-length in FUV is 2.3 times larger than that at 3.6 ?m, and we also find the disk to gradually become flatter from longer to shorter wavelengths. We performed a statistical source subtraction to eliminate the background contaminants and identified 261 unresolved UV sources between the radii 5.3 kpc and 10 kpc (1 ? 2 R25). The identified UV sources show an age range between 1300 Myr with a peak at 25 Myr and a mass range between 10 3M? to 10 6M?, estimated using Starburst99 models. The north-eastern spiral arm is found to be populated by young low mass sources suggesting that the star formation in this spiral arm is a recent phenomenon. The UV emission beyond the R25 radius has contribution from these low mass sources and is extended up to ? 2 R25 radius. We conclude that NGC300 has an extended UV disk, mainly populated by young low mass sources. The star formation rate is measured to be ?0.46M?/yr which is comparable to its near optical twin M33. 2019, Indian Academy of Sciences. -
Categorization of artwork images based on painters using CNN
Artworks and paintings has been an integral part of human civilization since the dawn of the Stone Age. Paintings gives more insight about any subject compared to the scriptures and documents. Archiving of digital form of paintings helps to preserve the artworks of different painters. The anticipated work is aimed for the classification of painters' artworks. The artworks of Foreign & Indian painters are considered for the proposed work. The foreign painters' artworks are obtained from [14]. At present, the Indian painters' artwork dataset is not readily available. The images were downloaded from the specific genuine website [13]. Conventional Neural Network is used for Feature learning and classification. Around 20k images of artworks is used for the experiment and got an average accuracy of 85.05%. Published under licence by IOP Publishing Ltd. -
Interpenetrated Robust Metal-Organic Framework with Urea-Functionality-Decked Pores for Selective and Ultrasensitive Detection of Antibiotics and Oxo-anions
Conjoining the benefits of structural diversity and deliberate implantation of task-specific sites inside the porous channels, metal-organic frameworks (MOFs) not only ensure environmental remediation via acute detection of organic as well as inorganic pollutants but also rationalize structure-performance synergies to devise smarter materials with advanced performances. Herein, we report a urea-functionality-grafted Co(II)-framework (UMOF) based on a mixed ligand approach. The 3-fold interpenetrated and [Co2(COO)4N4] building unit-containing structure exhibits high stability and free-carboxamide-site-decorated microporous channels. Assimilation of high-density hydrogen-bond donor groups plus the ?-electron-rich aromatic ligand benefits the UMOF acting as a selective fluoro-sensor for three noxious antibiotics through remarkable quenching, including nitrofurazone (NFT, Ksv: 3.2 104 M-1), nitrofurantoin (NFZ, Ksv: 3.0 104 M-1), and sulfamethazine (SMZ, Ksv: 3.3 104 M-1) with ppb level limits of detection (LODs, NFT: 110.42, NFZ: 97.89, and SMZ: 78.77). The mechanistic insight of luminescence quenching is supported from density functional theory calculations, which endorse the electron-transfer route via portraying variation in the energy levels of the urea group-affixed linker by individual organo-toxins, besides verifying analyte-linker noncovalent interactions. The framework further demonstrates highly discriminative turn-off detection of oxo-anions with extreme low LODs (Cr2O72-: 73.35; CrO42-: 189; and MnO4-: 49.96 ppb). Of note is the reusability of the UMOF toward multicyclic sensing of all the organic and inorganic analytes besides their fast-responsive detection, where variable magnitudes of energy-transfer contributions unequivocally authenticate the turn-off event. 2023 American Chemical Society. -
Spectral and temporal studies of Swift J1658.24242 using AstroSat observations with the JeTCAF model
We present the X-ray spectral and temporal analysis of the black hole X-ray transient Swift J1658.2-4242 observed by AstroSat. Three epochs of data have been analysed using the JeTCAF model to estimate the mass accretion rates and to understand the geometry of the flow. The best-fitting disc mass accretion rate (? d) varies between 0.90+-000102 and 1.09+-000304 M?Edd in these observations, while the halo mass accretion rate changes from 0.15+-000101 to 0.25+-000102 M?Edd. We estimate the size of the dynamic corona that varies substantially from 64.9+-3319 to 34.5+-1250 rg and a moderately high jet/outflow collimation factor stipulates isotropic outflow. The inferred high disc mass accretion rate and bigger corona size indicate that the source might be in the intermediate to soft spectral state of black hole X-ray binaries. The mass of the black hole estimated from different model combinations is ?14 M?. In addition, we compute the quasi-periodic oscillation (QPO) frequencies from the model-fitted parameters, which match the observed QPOs. We further calculate the binary parameters of the system from the decay profile of the light curve and the spectral parameters. The estimated orbital period of the system is 4.0 0.4 h by assuming the companion as a mid or late K-type star. Our analysis using the JeTCAF model sheds light on the physical origin of the spectrotemporal behaviour of the source, and the observed properties are mainly due to the change in both the mass accretion rates and absorbing column density. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Investigation on Electrode/Electrolyte Interfaces through Impedance Spectroscopy
In the present paper, impedance measurements of the battery configuration, Anode?lithium borophosphate glass electrolyte?LiCoO2 cathode, has been carried out to throw some light on the electrochemical interfacial behavior between the chosen electrodes and electrolyte. The cathode material, lithium cobalt oxide (LiCoO2) has been prepared by three different techniques and characterized. Sol-gel synthesized LiCoO2 showed uniformly distributed spherical shape particles with an average size of 500 nm and also exhibited better electrochemical performance. Charging and discharging (23 cycles) of the battery indicated an OCV of 2 V. However, the theoretical OCV of 4 V could not be achieved. The poor performance of the battery could be attributed to the electrochemical processes and SEI film formation at the electrode/electrolyte interfaces. Impedance spectroscopy shows that the major contributions to the impedance of the battery are the electrolyte resistance and the electrode/electrolyte interfacial resistance. With each recharging cycle, the value of electrolyte resistance remains almost constant; however, the interface resistance increases, during the passage of current, due to the interfacial passive layer formation. 2020 Taylor & Francis Group, LLC. -
Secure Decentralization: Examining the Role of Blockchain in Network Security
Blockchain generation has emerged as a novel answer for securing decentralized networks. This technology, which was first created for use in crypto currencies, has received enormous interest in recent years because of its capability for boosting protection in various industries and community protection. The essential precept at the back of block chain technology is the decentralization of statistics garage and control. In a decentralized network, no central authority may control the statistics. Rather, the facts are shipped amongst multiple nodes, making it immune to tampering and single factors of failure. One of the most important advantages of blockchain in community protection is its capacity to offer cozy and transparent communication amongst community customers. Through cryptographic techniques, block chain can affirm the identities of network participants and ensure the authenticity of records trade. This feature is extraordinarily valuable in preventing unauthorized access and facts manipulation. 2024 IEEE. -
Entrepreneurial Attitude and Entrepreneurial Intentions of Female Engineering Students: Mediating Roles of Passion and Creativity
Entrepreneurship holds a crucial function in addressing societal and economic issues like joblessness and inequalities between different regions. Acknowledging its significance, government officials and educational institutions exert considerable energy towards nurturing individuals into entrepreneurs. Multiple elements influence a person's path to becoming an entrepreneur. This research seeks to examine how one's entrepreneurial attitude (EA) impacts one's drive to become an entrepreneur, with passion and creativity serving as an intermediary in this connection. The research is explanatory and employs a survey-based approach. The findings convey that entrepreneurial attitude significantly influences the determination of female engineering students to pursue entrepreneurship. The study highlights the mediating roles of passion and creativity in the relationship between entrepreneurial attitude and intentions. While passion positively mediated the relationship, creativity had a negative mediating effect. 2024, Institute of Economic Sciences. All rights reserved. -
Nexus between Entrepreneurial Education, Entrepreneurial Mindset, and Entrepreneurial Passion on Entrepreneurial Intentions: Mediating Role of Self-efficacy
This study examines the complex dynamics of factors affecting self-efficacy (SE) and entrepreneurial intentions (EIs) among engineering students in India. It investigates the mediating role of SE in the relationships between entrepreneurial education (EE), entrepreneurial mindset (EM), entrepreneurial passion (EP), and EIs. The research reveals that SE remains stable across various personal characteristics, highlighting it as a robust individual trait less influenced by external factors. Gender significantly impacts EIs, underscoring its pivotal role in shaping entrepreneurial intentions, while other personal characteristics show limited influence. Passion and mindset appear to be consistent across demographics, suggesting they are intrinsic qualities. SE serves as a mediator in the connections between entrepreneurial mindset, passion, and intentions, elucidating its pivotal role in the entrepreneurial process. EE indirectly affects EIs and SE through other factors in the research model. Entrepreneurial passion directly influences both EIs and SE, emphasizing its role as a driving force for entrepreneurship. An entrepreneurial mindset doesn't directly affect intentions but significantly influences SE, indicating its importance in shaping self-efficacy, which in turn influences intentions. The findings can guide the development of educational programs and initiatives designed to promote entrepreneurship among engineering students in India while considering the impact of self-efficacy and gender-related factors. 2024, Iquz Galaxy Publisher. All rights reserved. -
Antecedents of Ethical Goods and Services Tax Culture among young adults - Special Reference to Maharashtra and Karnataka
Since the implementation of the Goods and Services Tax (GST) in 2017, it has become clear that this new Indian indirect tax system is here to stay. The Indian GST Council is continuously deliberating and making efforts to improve GST revenue collection at the state and central levels. The focus is now on the young adults in the country who will play a vital role in shaping the future of GST compliance. Their tax mentality and behaviour in contributing to GST revenue as daily consumers will determine the ethical tax culture in India. They need to understand how crucial their role is in discouraging evasive practices by sellers in the unorganised retail sector at the point of sale. The study utilized structural equation modelling to test the acceptability of the model. The process was supported by a structured questionnaire, with 324 respondents between the age group of 17-30 years. Understanding GST significantly influences acceptance of GST as a tax system, however, the acceptance of the GST tax system does not significantly lead to young adults discouraging the evasive behaviour of sellers in the unorganised retail sector at the point of sale. And, finally, the discouragement of evasive behaviour by young adults does influence the possibility of an ethical GST tax culture. The respondents majorly represented young adults between 17-20 years of age. The model has not measured the existence of covariance among the variables, nor has any mediating or moderating factors been identified, as GST tax culture in the Indian context is still unexplored and GST in itself is relatively new in the country. 2024 IEEE. -
Artificial intelligence and service marketing innovation
The integration of artificial intelligence (AI) into service marketing in India is expected to significantly impact marketing strategies and economic dynamics. The emphasis on personalization, automation, predictive analytics, and chatbots will enhance customer engagement and brand loyalty, leading to increased sales and revenue. Automation of marketing workflows will streamline operations, improve efficiency, and foster business growth. AI's predictive analytics capabilities will help businesses make informed decisions about their marketing strategies, particularly in a diverse market like India. AI-driven chatbots will enhance customer satisfaction and engagement, contributing to positive brand perception and loyalty. However, there may be concerns about job displacement, particularly in routine tasks. The growth of AI-driven service marketing can contribute to the development of a technologydriven ecosystem in India, attracting investments, fostering entrepreneurship, and stimulating innovation. 2024 by IGI Global. All rights reserved. -
Face and Emotion Recognition from Real-Time Facial Expressions Using Deep Learning Algorithms
Emotions are faster than words in the field of humancomputer interaction. Identifying human facial expressions can be performed by a multimodal approach that includes body language, gestures, speech, and facial expressions. This paper throws light on emotion recognition via facial expressions, as the face is the basic index of expressing our emotions. Though emotions are universal, they have a slight variation from one person to another. Hence, the proposed model first detects the face using histogram of gradients (HOG) recognized by deep learning algorithms such as linear support vector machine (LSVM), and then, the emotion of that person is detected through deep learning techniques to increase the accuracy percentage. The paper also highlights the data collection and preprocessing techniques. Images were collected using a simple HAAR classifier program, resized, and preprocessed by removing noise using a mean filter. The model resulted in an accuracy percentage for face and emotion being 97% and 92%, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Economic Burden and Productivity Loss of Employees with Lifestyle Diseases in Sedentary Occupations During Pandemic
Over the past few decades, the prevalence of Lifestyle Diseases or Non-Communicable Diseases (NCDs) have increased. There has been an increasing concern about these lifestyle diseases, with hypertension acting as the most prevalent lifestyle disease in the populace. It further exaggerates the issue as its prevalence increases exposure to other lifestyle diseases such as Diabetes and Cardiovascular Diseases (CVD). With health being an important component of human capital, the presence of lifestyle diseases has an economic impact on the individual and the organisation. The presence of an illness reduces the productivity level delivered by the individual to work, resulting in productivity loss. Apart from impacting an employee's productivity, the prevalence of lifestyle diseases incurs a significant monetary expense in the form of healthcare required to manage them. This monetary expense is called an economic burden or out-of-pocket expenditure. On these grounds, the current study examines the economic burden and impact on the productivity of employees suffering from lifestyle diseases (Hypertension, Diabetes and CVD) working in sedentary occupations. With lifestyle diseases majorly influenced by the lifestyle patterns of an individual, employees working in a sedentary occupation are at greater exposure to lifestyle diseases and hence were selected as the target population. A cross-sectional study was conducted among 426 employees of sedentary occupations in the Delhi-NCR region. The economic burden has been measured as a sum of the direct and indirect costs of the diseases incurred in a year. Using the estimates of economic burden, Catastrophic Healthcare Expenditure (CHE) was measured at different threshold levels. The study has also evaluated productivity loss through presenteeism and absenteeism approaches. An attempt was made to examine the relationship between the economic burden 7 and productivity loss through presenteeism and absenteeism approaches. The result of the study shows a significant share of the economic burden for lifestyle diseases and their comorbidities. CHE was highest at the 40% threshold level. The level of disparity in catastrophe among lower and high-income individuals was also highest at the 40% threshold level. Further statistical results show a high cost of absenteeism due to lifestyle diseases compared to presenteeism and found that economic burden has a strong positive relationship with absenteeism and presenteeism. Overall, the study concludes that lifestyle disease incurs a substantial economic burden and CHE for employees working in sedentary occupations. The estimate for the same increases if multiple lifestyle diseases are present. Further, the impact of catastrophe is more for low-income than high-income individuals due to the limited availability of resources to manage the health issue. Apart from causing monetary expense, the presence of lifestyle diseases also causes a high cost of absenteeism and presenteeism, increasing the economic cost of managing lifestyle diseases. -
Digital Transaction Cyber-Attack Detection Using Particle Swarm Optimization
The cyber digital world is an essential variant in day-to-day life in advanced technology. There is a better change in the lifestyle as intelligent technology. In larger excite to increase the advanced technology which can be developed to humans in major dependent on network and internet users. Now, in modern times, the internet has changed the primary need in human lifestyle by giving access to everything in the world while sitting in one place knowing and updating the information and usage of online subscribers or Revolution. The world is moving in Rapid and Faster communications within a fraction of a second, at a lesser cost, and it has minimal paper-based processes and relies on the digitization document instead of a paperless environment. The data is handled by finch security practices, which are used in security worldwide to establish protected data management systems like digital lending, credits, mobile Banking, and mobile payment. Cryptocurrency and blockchain, B-trading, and banking as a service are included. At the same time, leveraging the new technologies is to resist hacking cyber-attacks. This article is also involved in artificial intelligence and machine learning (AI&ML) in different cyber-attacks. This article focuses on genetic algorithms to detect the cyber-attack. The main aim of the detection is future to prevent these cyber-attacks. The comparison will take two sample genetic algorithms. The first one is taken for Ant Colony Optimization (ACO), and the proposed model is taken for Particle Swarm Optimization. The average attack detection of ACO algorithm is 45 packets at the same time PSO algorithm will detect 50 packets. 2023 IEEE. -
Structural Health Monitoring Using Machine Learning Techniques
Environmental factors, particularly vibrations and temperature can damage the structural health of the building. To avoid heavy damage to the building and to maintain the building's structural health this paper suggests monitoring of building using machine learning algorithms. Machine learning algorithms are used to predict temperature and vibration damages in buildings. Temperature and vibration values are obtained through the grove vibration sensor and NTC thermistor attached to Raspberry Pi 3B plus. In the Raspberry pi, Machine learning algorithms are executed. The activation functions used are Relu, Sigmoid, and Tanh. The experimental results reveal that the Sigmoid activation function gives the best results in terms of metrics with accuracy 94.25, Precision 0.951, Recall 0.912, and F1 score 0.388. The sigmoid function is used in machine learning algorithms for predicting temperature and vibrations. Predicted temperature and vibrations damages are sent to the server and viewed through the user mobile. K- Nearest Neighbor algorithm produced best results with an accuracy rate of 85.50, Precision of 0.922, Sensitivity of 0.830, Specificity of 0.840 and F1 score of 0.873. 2023 IEEE.