This study investigated a new approach to achieving high energy density supercapacitors (SCs) by using high surface area self-activated carbon from waste coffee grounds (WCGs) and modifying 3D printed electrodes' porous structure by varying infill density. The derived activated carbons' surface area, pore size, and pore volume were controlled by thermally treating the WCGs at different temperatures (1000˚C, 1100˚C, and 1200˚C) and post-treating with HCL to remove water-soluble ashes and contaminants that block activated carbon pores. Surface area characterization revealed that the carbon activated at 1000˚C had the highest surface of 1173.48 m2 g-1, and with the addition of HCL, the surface area increased to 1209.35 m2 g-1. This activated carbon was used for fabricating the electrodes based on the surface area and having both micropores and macropores, which are beneficial for charge storage. Direct ink writing (DIW) method was utilized for 3D printing SC electrodes and changing the electrode structure by increasing the infill densities at 30%, 50%, and 100%. Upon increasing the infill densities, the electrodes' mass increased linearly, porosity decreased, and the total surface area increased for the 30% and 50% infill electrodes but decreased for the 100% infill electrode. Cyclic voltammetry (CV) test on the assembled …

Interfacial mechanical properties of adhesive joints are very crucial in board applications, including composites, multilayer structures, and biomedical devices. Establishing traction-separation (T-S) relations for interfacial adhesion can evaluate mechanical and structural reliability, robustness, and failure criteria. Due to the short range of interfacial adhesion such as micro to nanoscale, accurate measurements of T-S relations remain challenging. The advent of machine learning (ML) became a promising tool to predict materials behaviors and establish data-driven mechanical models. In this study, we integrated a state-of-the-art ML method, finite element analysis (FEA), and standard experiments to develop data-driven models for characterizing the interfacial mechanical properties precisely. Macroscale force-displacement curves are derived from FEA with incorporation of double cantilever beam tests to generate the dataset for ML model. The eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are used to determine T-S relations with R2 score of 98.8% and locate imperfections at the interface with accuracy of around 80.8%. The outcome of the XGBoost models demonstrated accurate predictions and fast calculation speed, outperforming several other ML methods. Using 3D printed double cantilever beam specimens, the performance of the ML models is validated experimentally for different materials. Furthermore, a XGBoost model-based package is designed to …