AI-Assisted 3D-Printed Biomaterial Supercapacitors for Green Energy Storage - Kadao, Anjali Krushna; Prashant, Patil Manisha; Sardana, Sunaina (Malikussaleh University, Aceh, Indonesia, 2025)
Advancements of biomaterial-based supercapacitors have been fuelled by the growing demand for sustainable and high-performance energy storage solutions. This work suggests the use of artificial intelligence to develop an AI-assisted 3D printed biomaterial supercapacitor, namely comprising electrode materials optimised by artificial intelligence (AI), bio-based electrolytes, and intelligent performance monitoring to increase efficiency and sustainability. It is an AI-driven approach that selects and optimises the biomaterials: high conductivity, low internal resistance, and excellent charge retention. Porous electrodes can be deliberately engineered on microscales by advanced 3D printing techniques; these perform well in facilitating fast ion diffusion and high energy storage capacity. This is achieved through experimental results of a 45% increase in capacitance, 68% reduction in charge transfer resistance, and 18% improvement in cycle stability on conventional supercapacitors. Moreover, AI-powered predictive maintenance increases the life of the device by 60%, thereby reducing unplanned failure by 60%. The involvement of biodegradable and non-toxic inclusion of materials encourages environmental sustainability, and thus, this supercapacitor is a green alternative for next-generation energy storage applications. This solution is suitable for wearable electronics, renewable energy systems, as well as smart devices, with high efficiency, low environmental impact and intelligent monitoring capability. The energy storage technology presents instances where AI, biomaterials, and 3D printers have the potential to transform the energy storage technology into a scalable, eco-friendly, and intelligent supercapacitor for future energy demands, according to this study.