The agglomeration of reduced graphene oxide (rGO) in water makes the development of rGO inks for supercapacitor printing challenging. Cellulose nanofiber (CNF), a biodegradable and renewable nanomaterial, can act as a nanospacer, preventing the agglomeration and restacking of rGO flakes. In this work, rGO/CNF films were fabricated using an environmentally friendly water-based rGO/CNF ink. In the absence of an additional binder/surfactant, the rGO/CNF films demonstrated remarkably enhanced hydrophilicity while retaining good electrical conductivity. The concentration of CNF was varied to observe the variation in the electrochemical performance. At a current density of 1 mA/cm2, the rGO/CNF-15 film exhibited a maximum areal capacitance of 98.61 mF/cm2, closely matching that of pure rGO films. Because of its excellent electrical performance, ease of manufacturing, and environmental friendliness, this water-based rGO/CNF ink may have promising applications in the printing of supercapacitor electrodes.
Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg-Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of application tool of graphical user interface (GUI). Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain.