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This research article discusses the types of carbon nanotubes and some related properties, synthesis methods and applications. In this research article, the specificities of SWCNTs as well as MWCNTs are described along with the extraordinary properties of carbon nanotubes such as electrical conductance, resistivity, and thermal conductivity in the direction of axis of the tube. Overview of certain specific methods for the growth of carbon nanotubes has also been reviewed. Previously available methods for the formation of CNTs are also described which include: arc discharge, laser ablation, chemical vapor deposition (CVD) and plasma enhanced chemical vapor deposition (PECVD). Carbon nanotubes have a number of applications in the field of electronics, biomedicine, and chemical sensors etc. Some of the applications are also briefly described.

Transformer architectures have widespread applications, particularly in Natural Language Processing and Computer Vision. Recently, Transformers have been employed in various aspects of time-series analysis. This tutorial provides an overview of the Transformer architecture, its applications, and a collection of examples from recent research in time-series analysis. We delve into an explanation of the core components of the Transformer, including the self-attention mechanism, positional encoding, multi-head, and encoder/decoder. Several enhancements to the initial Transformer architecture are highlighted to tackle time-series tasks. The tutorial also provides best practices and techniques to overcome the challenge of effectively training Transformers for time-series analysis.

Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.

Electronic products are becoming an essential part of our daily life. There is a huge demand to produce small and portable but powerful electronic products. Carbon nanotubes (CNTs) have excellent electrical, mechanical and thermal properties which can be exploited to build next-generation electronics. This paper reviews different types and properties of CNTs and also presents the CNT-based electronics along with their advantage over the conventionally used products. CNT usage in electronics, such as biosensing, energy and data storage devices, is discussed. CNT-based field emission devices, which showed outstanding results are also discussed. The current challenges of CNT-based electronics and the future of CNT in electronics applications are mentioned.

The Industrial Revolution can be termed as the transformation of traditional industrial practices into new techniques dominated by the technologies available at that time. The first three industrial revolutions were driven respectively by mechanization, electrification, and automation which had gradually transformed the agrarian economy into a manufacturing-based economy. It helped in enhancing the lifestyle of the factory workers and the healthcare system, which improved the overall quality of living. The industries that adapted to the change witnessed a tremendous increase in the production of goods, competitive advantage, and cross-border business opportunities. While we are currently living to see the fourth industrial revolution (also known as Industry 4.0) unfolding around us, the world is poised for the next big leap, the fifth industrial revolution or Industry 5.0. Hence, the first half of the paper outlines the enabling technologies of Industry 4.0 and conceptualizes how they would act as the foundation for the fifth industrial revolution. The socio-economic challenges of the technologies and the need for Industry 5.0 technologies are also discussed. The second half of the paper outlines the prospective technologies of Industry 5.0, their potential applications from the perspective of industry leaders and scholars and conceptualizes how they can overcome the challenges of Industry 4.0. The definition of “sustainability trilemma” a new term coined by the authors, and the reasoning for calling the next industrial revolution “Industry 4.0S” (another new term) rather than Industry 5.0 are also presented.

Wearable devices have found numerous applications in healthcare ranging from physiological diseases, such as cardiovascular diseases, hypertension and muscle disorders to neurocognitive disorders, such as Parkinson’s disease, Alzheimer’s disease and other psychological diseases. Different types of wearables are used for this purpose, for example, skin-based wearables including tattoo-based wearables, textile-based wearables, and biofluidic-based wearables. Recently, wearables have also shown encouraging improvements as a drug delivery system; therefore, enhancing its utility towards personalized healthcare. These wearables contain inherent challenges, which need to be addressed before their commercialization as a fully personalized healthcare system. This paper reviews different types of wearable devices currently being used in the healthcare field. It also highlights their efficacy in monitoring different diseases and applications of healthcare wearable devices (HWDs) for diagnostic and treatment purposes. Additionally, current challenges and limitations of these wearables in the field of healthcare along with their future perspectives are also reviewed.

The introduction of 3D printing technology has revolutionised the manufacturing and electronic product design in the past few years, where it is used to even produce printed circuit boards. Printed electronics is one of the fastest-growing additive manufacturing technologies and is becoming invaluable to various industries. The evolution of several contact and non-contact types of fabrication techniques have been reported in the recent past. Leveraging these technologies, various types of printed electronic components have been realized. One method is inkjet printing technology, which has been widely accepted for printed electronics manufacturing. As 3D printing uses only those materials which are essential to create the product, it eliminates waste production, with a smaller equipment cost and minimizes the number of process steps, resulting in lower manufacturing costs with reduced turnaround time. Various kinds of conductive and non-conductive materials have emerged in the recent past in conjunction with many manufacturing techniques for printed electronics. Herein, we review the most commonly used substrates, electronic printing materials, and the widespread printing techniques employed at the industrial level, giving an overall vision for a better understanding and evaluation of their different features. The technical challenges of several contact and non-contact techniques with corresponding solutions are also presented. Finally, status on advances in the production of various kinds of materials employed in 3D printed electronics and the methods for producing them, shortcomings, technical challenges, applications, benefits, and the future opportunities pertaining to printed electronics are discussed in detail.

Flexible electronics have suggested tremendous potential to shape human lives for more convenience and pleasure. Strenuous efforts have been devoted to developing flexible organic field-effect transistor (FOFET) technologies for rollable displays, bendable smart cards, flexible sensors and artificial skins. However, these applications are still in a nascent stage for lack of standard high-performance material stacks as well as mature manufacturing technologies. In this review, the material choice and device design for FOFET devices and circuits, as well as the demonstrated applications are summarized in detail. Moreover, the technical challenges and potential applications of FOFETs in the future are discussed.

Wearable sensors have made significant progress in sensing physiological and biochemical markers for telehealth. By monitoring vital signs like body temperature, arterial oxygen saturation, and breath rate, wearable sensors provide enormous potential for the early detection of diseases. In recent years, significant advancements have been achieved in the development of wearable sensors based on two-dimensional (2D) materials with flexibility, excellent mechanical stability, high sensitivity, and accuracy introducing a new approach to remote and real-time health monitoring. In this review, we outline 2D materials-based wearable sensors and biosensors for a remote health monitoring system. The review focused on five types of wearable sensors, which were classified according to their sensing mechanism, such as pressure, strain, electrochemical, optoelectronic, and temperature sensors. 2D material capabilities and their impact on the performance and operation of the wearable sensor are outlined. The fundamental sensing principles and mechanism of wearable sensors, as well as their applications are explored. This review concludes by discussing the remaining obstacles and future opportunities for this emerging telehealth field. We hope that this report will be useful to individuals who want to design new wearable sensors based on 2D materials and it will generate new ideas.

The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.