Inovasi material anoda dan algoritma deep learning sebagai strategi modern dalam memperpanjang umur operasional baterai smartphone - kholik, nanda rahmat (Maliki Interdisciplinary Journal, 2026)
The development of digital technology and smartphone has made lithium-ion batteries thr most important component in performance and convenience. However, lithium-ion batteries are prone to degradation caused by repeated charging and discharging, temperature stress, and energy sustainability. Furthermore, the dubrability samartphones is influenced by device designs that support modularity and repairability. Circular economy based designs can enchance the durability of physical devices and reduce electronic waste by enabling component repairs. Innovations in anode compositions, such as coating graphite with citric acid, can improve surface conductivity and electrochemical stability, which positively impacts cyclic capacity and resistance to degration. The application of characteristics such as XRD and SEM shows that the sintering temperature and citric acid compositions can affect the quality of graphite and the materials performance. The utilization of artificial intelligence (AI) in energy management is also present to predict power usage based on smartphone user behavior patterns for energy efficiency. Deep learning models can analyze application usage patterns and device states resulting in intelligent and responsive battery management systems. Overall, the sustainability and performance enhancement of lithium-ion batteries require approaches such as understanding degration, material innovation, device design, and the implementation of AL based smart technologies to produce more efficient and sustainable smartphone technologies in the future.