The paper "Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems" by Roujuan Li, Di Wei, and Zhonglin Wang reviews the integration of machine learning (ML) and deep learning (DL) algorithms with triboelectric nanogenerators (TENGs) to enhance the performance of self-powered sensing systems. TENGs, which convert mechanical energy into electrical energy through contact electrification, offer a sustainable power source for ubiquitous sensing applications. The paper discusses the advantages of TENGs, including their energy harvesting capabilities and the ability to collect complex and diverse data. It highlights the challenges of processing large and complex datasets, such as environmental noise and signal interference, and how ML and DL algorithms can address these issues. The authors review the latest advances in ML algorithms for solid-solid (S-S) and liquid-solid (L-S) TENG sensors, analyzing the pros and cons of different algorithms based on the sample size and data complexity. They present various application scenarios, such as tactile sensing, gesture recognition, and environmental monitoring, and discuss the main challenges and future prospects of combining hardware (TENG sensors) with software (ML algorithms) in complex environments. Key ML algorithms discussed include support vector machines (SVM), random forests (RF), K-nearest neighbors (KNN), convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN). The paper provides detailed examples of how these algorithms have been applied to TENG sensors, demonstrating their effectiveness in improving sensing performance and reducing environmental impact. The paper concludes by emphasizing the importance of selecting appropriate ML methods based on the specific characteristics of the interaction medium, signal types, and intended sensing tasks. It also highlights the need for further research to optimize and adapt ML models to diverse application scenarios, ensuring more effective and accurate sensor systems.The paper "Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems" by Roujuan Li, Di Wei, and Zhonglin Wang reviews the integration of machine learning (ML) and deep learning (DL) algorithms with triboelectric nanogenerators (TENGs) to enhance the performance of self-powered sensing systems. TENGs, which convert mechanical energy into electrical energy through contact electrification, offer a sustainable power source for ubiquitous sensing applications. The paper discusses the advantages of TENGs, including their energy harvesting capabilities and the ability to collect complex and diverse data. It highlights the challenges of processing large and complex datasets, such as environmental noise and signal interference, and how ML and DL algorithms can address these issues. The authors review the latest advances in ML algorithms for solid-solid (S-S) and liquid-solid (L-S) TENG sensors, analyzing the pros and cons of different algorithms based on the sample size and data complexity. They present various application scenarios, such as tactile sensing, gesture recognition, and environmental monitoring, and discuss the main challenges and future prospects of combining hardware (TENG sensors) with software (ML algorithms) in complex environments. Key ML algorithms discussed include support vector machines (SVM), random forests (RF), K-nearest neighbors (KNN), convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN). The paper provides detailed examples of how these algorithms have been applied to TENG sensors, demonstrating their effectiveness in improving sensing performance and reducing environmental impact. The paper concludes by emphasizing the importance of selecting appropriate ML methods based on the specific characteristics of the interaction medium, signal types, and intended sensing tasks. It also highlights the need for further research to optimize and adapt ML models to diverse application scenarios, ensuring more effective and accurate sensor systems.