Energy storage is the process of storing previously generated energy for future usage in order to meet energy demands. The need for high-power density energy storage materials is growing across the board. The high ionic transport, superior electronic conductivity, rapid ion diffusion, high current tolerance, etc. are few among the numerous factors that can be considered the versatilities of nanomaterials. This makes the nanomaterials suitable for energy storage applications. According to the allied market research, the global nanotechnology in energy industry was estimated at $139.7 million in 2020 and is anticipated to hit $384.8 million by 2030, registering a compound annual growth rate (CAGR) of 10.7% from 2021 to 2030. The extraordinary and improved properties of carbon-based nanomaterials and their tunable surface chemistry authorize them to be used in design of competent high-energy and high-power energy storage devices. Recent research and future progress focus on effective usage of low-dimensional carbon-based nanomaterials for energy conversion and storage systems. In particular, versatile carbon nanomaterials with multifunctional capabilities have attracted incredible attention in different types of batteries, solar cells, fuel cells, supercapacitors, and other energy storage devices. Engineering the carbon-based nanomaterials with efficient energy storage and remarkable conversion ability embraces the promise of creating a new path for their future development. This article reviews the role of few carbon-based nanomaterials in efficiently increasing the competence and dependability of energy storage applications.
In this study, the impacts of co-pyrolyzing wood-based biomass from Ficus benghalensis with PET on liquid oil output, reactivity, and heating values were investigated. The effects of temperature on the product distribution of individual pyrolysis and the biomass-plastic ratio on co-pyrolysis were investigated. For individual pyrolysis, a maximum amount of 40.8 wt (%) liquid oil was obtained from biomass at 450°C. On the other hand, a maximum of 59.5 wt (%) liquid oil was obtained from PET at 500°C. The co-pyrolysis experiments were conducted by blending PET with biomass at different percentages, such as 20%, 40%, 60%, and 80%. At 60% addition of PET, a more positive synergistic effect was identified due to radical secondary reactions. In addition, the physical and chemical characterization studies conducted on pyrolysis oil showed that biomass and plastic materials could be used to make valuable chemicals.
Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and classifying diabetic retinopathy on fundus retina images, numerous artificial intelligence-based algorithms have been put forth by the scientific community. Due to its real-time relevance to everyone’s lives, smart healthcare is attracting a lot of interest. With the convergence of IoT, this attention has increased. The leading cause of blindness among persons in their working years is diabetic eye disease. Millions of people live in the most populous Asian nations, including China and India, and the number of diabetics among them is on the rise. To provide medical screening and diagnosis for this rising population of diabetes patients, skilled clinicians faced significant challenges. Our objective is to use deep learning techniques to automatically detect blind spots in eyes and determine how serious they may be. We suggest an enhanced convolutional neural network (ECNN) utilizing a genetic algorithm in this paper. The ECNN technique’s accuracy results are compared to those of existing approaches like the K-nearest neighbor approach, convolutional neural network, and support vector machine with the genetic algorithm.
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