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

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.