Matthew Yen

Due to demands for sustainability, the interest in energy storage devices constructed from green materials has increased immensely. These devices currently have yet to be satisfactory. Issues include high production costs and toxicity, limited dependability, and subpar electrochemical performance. In this research, low-cost, plant-based electroactive Cu₃Mo₂O₉ materials were synthesized via co-precipitation followed by an annealing method using two different structure-directing agents, i.e., the commonly used surfactant cetyltrimethylammonium bromide (CTAB) and the biomolecule deoxyribonucleic acid (DNA) as a greener alternative, and these materials were studied for the first time. Further, the Cu₃Mo₂O₉ nanoparticles developed using CTAB and DNA were integrated into the lignin matrix and studied as flexible electrodes for supercapacitor application. Here, the morphological advantages of the nanorods and nanosheets formed by varying the synthesis methods and their effects during supercapacitor studies were studied in detail. After 1200 cycles, the Al/lig-Cu₃Mo₂O₉@DNA supercapacitor exhibited higher capacitive performance when compared to the Al/lig-Cu₃Mo₂O₉@CTAB supercapacitor. The Al/Lig-Cu₃Mo₂O₉@DNA supercapacitor had an initial specific capacitance of 404.64 mF g⁻¹ with a ~70% retention, while the Al/Lig-Cu₃Mo₂O₉@CTAB supercapacitor had an initial specific capacitance of 309.59 mF g⁻¹ with a ~50% retention. This study offers a new approach to creating scalable, low-cost, green composite CuMoO₄-based electrodes for flexible supercapacitors.

This review compares machine learning approaches for property prediction of materials, optimization, and energy storage device health estimation. Current challenges and prospects for high-impact areas in machine learning research are highlighted.