Fabrication of a Biomass-Derived Activated Carbon-Based Anode for High-Performance Li-Ion Batteries

Functionalized carbon electrocatalysts in energy conversion and storage applications: A review

Energy crises, along with environmental tampering, are currently a big topic on the global scene. The most promising strategy in the current research trend is the creation of ecologically pleasant renewable green alternative energy sources. Since adequate access to green, ecologically acceptable energy sources promotes industrialization and the well-being of human society. Hence, in this review, the most recent carbon electrocatalysts electrode materials prepared from porous activated carbon (PAC) in electrochemical energy conversion and storage systems due to its long life cycle, porosity and high surface area nature as well as low-cost nature have been clearly discussed. This review aims to demonstrate that the increasing interest in the synthesis of PAC is accompanied by extensive research into their application in supercapacitors, where they continue to be the preferred electrode material. Their challenges and current progress of PAC electrodes are also discussed. The systematic methods of modifying PAC from biomass as well as some commercially available carbon materials have been thoroughly summarized in this review as an alternate high-surface area electrode for the effective generation of energy in many energy conversion and storage devices. The critical assessment is also extends to evaluate its recent advancements, trends in research progress, and future prospects.

Fabrication of High-Performance Asymmetric Supercapacitors Using Rice Husk-Activated Carbon and MnFe2O4 Nanostructures

To meet the growing demand for efficient and sustainable power sources, it is crucial to develop high-performance energy storage systems. Additionally, they should be cost-effective and able to operate without any detrimental environmental side effects. In this study, rice husk-activated carbon (RHAC), which is known for its abundance, low cost, and excellent electrochemical performance, was combined with MnFe₂O₄ nanostructures to improve the overall capacitance of asymmetric supercapacitors (ASCs) and their energy density. A series of activation and carbonization steps are involved in the fabrication process for RHAC from rice husk. Furthermore, the BET surface area for RHAC was determined to be 980 m² g^-1 and superior porosities (average pore diameter of 7.2 nm) provide abundant active sites for charge storage. Additionally, MnFe₂O₄ nanostructures were effective pseudocapacitive electrode materials due to their combined Faradic and non-Faradic capacitances. In order to assess the electrochemical performance of ASCs extensively, several characterization techniques were employed, including galvanostatic charge -discharge, cyclic voltammetry, and electrochemical impedance spectroscopy. Comparatively, the ASC demonstrated a maximum specific capacitance of ~420 F/g at a current density of 0.5 A/g. The as-fabricated ASC possesses remarkable electrochemical characteristics, including high specific capacitance, superior rate capability, and long-term cycle stability. The developed asymmetric configuration retained 98% of its capacitance even after 12,000 cycles performed at a current density of 6A/g, demonstrating its stability and reliability for supercapacitors. The present study demonstrates the potential of synergistic combinations of RHAC and MnFe₂O₄ nanostructures in improving supercapacitor performance, as well as providing a sustainable method of using agricultural waste for energy storage.

A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model

For remanufacturing to be more economically attractive, there is a need to develop automatic disassembly and automated visual detection methods. Screw removal is a common step in end-of-life product disassembly for remanufacturing. This paper presents a two-stage detection framework for structurally damaged screws and a linear regression model of reflection features that allows the detection framework to be conducted under uneven illumination conditions. The first stage employs reflection features to extract screws together with the reflection feature regression model. The second stage uses texture features to filter out false areas that have reflection features similar to those of screws. A self-optimisation strategy and weighted fusion are employed to connect the two stages. The detection framework was implemented on a robotic platform designed for disassembling electric vehicle batteries. This method allows screw removal to be conducted automatically in complex disassembly tasks, and the utilisation of the reflection feature and data learning provides new ideas for further research.