Abstract
With the rise of flexible electronics, the demand for advanced power sources has grown. Developing high-performance energy storage devices requires comprehensive consideration of various factors such as electrodes, electrolytes, and service conditions. Herein, a data-driven research framework is proposed to optimize the electrode-electrolyte system in supercapacitors. With the help of machine learning, we reveal the key factors affecting the capacitance performance of carbon-based materials. According to the algorithm analysis, a kind of 3D carbon network is prepared with controlled composition and structure, which is incorporated with a high-safety ionic liquid to obtain a supercapacitor device. This device with high energy density and impressive flexibility can maintain operational stability under extreme conditions such as humidity, shock, and localized damage. Overall, this work presents a typical pipeline for accelerating the design of energy-related devices.
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