This review explores the recent progress on carbon xerogels (CXs) and highlights their development and use as efficient electrodes in organic electric double-layer capacitors (EDLCs). In addition, this work examines how the adjustment of synthesis parameters, such as pH, polymerization duration, and the reactant-to-catalyst ratio, crucially affects the structure and electrochemical properties of xerogels. The adaptability of xerogels in terms of modification of their porosity and structure plays a vital role in the improvement of EDLC applications as it directly influences the interaction between electrolyte ions and the electrode surface, which is a key factor in determining EDLC performance. The review further discusses the substantial effects of chemical activation with KOH on the improvement of the porous structure and specific surface area, which leads to notable electrochemical enhancements. This structural control facilitates improvement in ion transport and storage, which are essential for efficient EDLC charge–discharge (C–D) cycles. Compared with commercial activated carbons for EDLC electrodes, CXs attract interest for their superior surface area, lower electrical resistance, and stable performance across diverse C–D rates, which underscore their promising potential in EDLC applications. This in-depth review not only summarizes the advancements in CX research but also highlights their potential to expand and improve EDLC applications and demonstrate the critical role of their tunable porosity and structure in the evolution of next-generation energy storage systems.

In this study, water-soluble melanin was synthesized through the genetic recombination of Escherichia coli using gallic acid as a substrate. The recombinant host produced 2.83 g/L of gallic acid-based melanin (GA melanin) from 20 mM gallic acid. Notably, the isolated GA melanin demonstrated exceptional antioxidant and antimicrobial activities, exhibiting a 25.7 % inhibition ratio against Candida albicans. The structure and composition of GA melanin were analyzed using Fourier-transform infrared (FT-IR) spectroscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and X-ray diffraction (XRD). Remarkably, GA melanin displayed high thermal stability, maintaining integrity up to 1000 °C. Additionally, it exhibited unique electrical properties in terms of conductivity and resistivity compared to other common types of melanin. Subsequently, GA melanin was cross-linked with hydrogel to create a sensing template. The resulting GA melanin hydrogel demonstrated lower resistance (80.08 ± 3.0 kohm) compared to conventional hydrogels (108.62 ± 10.4 kohm), indicating an approximately 1.77-fold improvement in adhesion. Given its physical, biological, and electrical properties, the GA melanin hydrogel was further utilized as a flexible motion-sensing material to detect resistivity changes induced by knee, wrist, and finger bending, as well as vocal cord vibrations. In all cases, the sensing module displayed notable sensitivity to motion-induced resistivity variations.

Physics-informed neural networks (PINNs) represent a continuous and differentiable mapping function, approximating solution curves for given differential equations. Recent studies have demonstrated the significant potential of PINNs as an alternative or complementary approach to conventional numerical methods. However, their application in structural dynamics, such as cantilever dynamics and fluid-induced excitations, poses challenges. In particular, limited accuracy and robustness in resolving high-order differential equations, including fourth-order differential equations encountered in structural dynamics, are major problems with PINNs. To address these challenges, this study explores optimal strategies for constructing PINNs in the context of cantilever dynamics: (1) performing scaling analysis for the configuration, (2) incorporating the second-order non-linear term of the input variables, and (3) utilizing a neural network architecture that reflects a series solution of decomposed bases. These proposed methods have significantly enhanced the predictive capabilities of PINNs, showing an order-of-magnitude improvement in accuracy compared to standard PINNs in resolving the dynamic oscillation of cantilevers and fluid-induced excitation driven by added mass forces. Furthermore, this study extends to the domain of fluid-induced excitation in cantilever dynamics, representing an extreme case of coupled dynamics in fluid–structure interaction. This research is expected to establish crucial baselines for the further development of PINNs in structural dynamics, with potential applicability to high-order coupled differential equations.