Shuai Zhang,Shuo Meng,Ke Zhang,Zhuang Wang,Xiaoyun Xu,Chuanwei Zhi,Shuo Shi,Jinlian Hu
Triboelectric nanogenerator (TENG) is one of the key research directions for future human-computer interaction (HCI), biomedicine, and environmental protection. Bio-based materials are an essential branch of many degradable materials. Keratin has attracted much attention due to its advantages of easy access, biodegradability, and good biocompatibility . A highly sensitive single-electrode TENG (S-TENG) based on CaCl 2 /PVA/keratin and Ecoflex with micro-domes is designed. The excellent stability (18,000 cycles) and stretchability (200%) of the sensor are confirmed by research. Moreover, the presence of CaCl 2 and keratin can significantly increase the S-TENG's output voltage, and the reason is also explored by the density functional theory (DFT) method. Through simulation, it is found that the decrease of the HOMO (highest occupied molecular orbital)-LUMO (lowest unoccupied molecular orbital) gap and the increase of the electrostatic potential are the root causes of the voltage increase. Relying on the excellent characteristics of the S-TENG, finger curvatures, gestures, and object shapes are recognized. Among them, the accuracy of object shape recognition by machine learning algorithm reached 98.1%. This study provides a new method for improving the output efficiency and prolonging the service life of S-TENG and confirms the feasibility of the electron cloud trap model to explain biomass triboelectric materials by keratin and CaCl 2 .
Shuai Zhang,Shuo Meng,Renjie Tan,Ke Zhang,Zhuang Wang,Xiaoyun Xu,Chuanwei Zhi,Xinshuo Liang,Jinlian Hu
Real-time monitoring and evaluation of muscle atrophy are vital for efficient treatment and effective diagnosis. Although some systems have been developed to monitor loss of muscle mass or strength, they are bulky and nonspecific, cannot be applied to the body, and cannot assess the degree of muscle atrophy. Our research focuses on developing a wireless multisensor wearable system (WMWS) for on-body monitoring and assessment of atrophy, which utilizes a single-electrode triboelectric nanogenerator (S-TENG) and electrochemical creatinine (CREA) sensor to achieve real-time acquisition of plantar pressure and interstitial fluid (ISF) CREA concentration. In addition, LSTM (long short-term memory) and SVM (support vector machine) machine learning (ML) algorithms also prove that our multisensor strategy can effectively improve the recognition rate of different degrees of atrophy (the highest accuracy reached 92.32%). Overall, our work makes it possible to monitor and grade muscle atrophy remotely in real time.
危险品化学品经营许可证（带存储）