Energy nexus: MXene–MOF–chalcogenide hybrids triboelectric nanogenerators (TENGs) for self-powered supercapacitor storage with machine learning insights

The growing demand for sustainable and self-sufficient energy systems has accelerated the development of triboelectric nanogenerators (TENGs) as promising candidates for converting ubiquitous mechanical energy into electricity. This review provides a comprehensive overview of next-generation TENGs that integrate advanced materials, metal chalcogenides, MXene, and metal–organic framework (MOF)-derived composites to achieve enhanced output performance, flexibility, and multifunctionality. Metal chalcogenides such as MoS 2 , WS 2 , and SnS 2 offer tunable electronic structures and high surface charge densities, enabling superior triboelectric enhancement. MXene (e.g., Ti 3 C 2 T x , Nb 2 C) contribute exceptional electrical conductivity, surface functionalization, and mechanical robustness, making them ideal candidates for hybrid interfaces and charge transport layers. Meanwhile, MOF-derived composites introduce hierarchical porosity and tunable chemical environments that boost interfacial charge transfer and energy conversion efficiency. The synergistic integration of these materials has resulted in advanced composite architectures with improved contact electrification, durability, and adaptability for flexible and wearable devices. Furthermore, this review highlights innovative design strategies, including surface engineering, heterostructure formation, and flexible device fabrication, to improve the performance of TENG-based energy harvesters and self-charging systems. Recent progress in coupling TENGs with supercapacitors and microbatteries is discussed, emphasizing their potential in self-powered sensors, Internet of Things (IoT) platforms, and sustainable electronics. Importantly, the emergence of machine learning (ML) and artificial intelligence (AI)-driven optimization frameworks has introduced powerful data-centric approaches for predicting material properties, guiding synthesis parameters, and autonomously optimizing TENG–supercapacitor integration. ML-assisted design now enables rapid screening of triboelectric materials, interpretable feature analysis, and real-time adaptive fabrication, paving the way toward self-evolving, high-efficiency hybrid energy systems. Finally, the review identifies critical challenges in scalability, material stability, and device integration while outlining future research directions toward intelligent, eco-friendly, and ML-assisted TENG systems for next-generation self-powered energy storage technologies.