Optimizing electrical output performance of triboelectric nanogenerators by micro-/nano-morphology design and fabrication
Source abstract
Abstract
Abstract Fueled by the increasing imperative for sustainable energy solutions and the burgeoning emphasis on health awareness, self-powered techniques have undergone notable strides in advancement. Triboelectric nanogenerators (TENGs) stand out as a prominent device capitalizing on the principles of triboelectrification and electrostatic induction to generate electricity or electrical signals. In efforts to augment the electrical output performance of TENGs and broaden their range of applications, researchers have endeavored to refine materials, surface morphology, and structural design. Among them, physical morphological modifications play a pivotal role in enhancing the electrical properties of TENGs by increasing the contact surface area, which can be achieved by building micro-/nano-structures on the surface or inside the friction material. In this review, we summarize the common morphologies of TENGs, categorize the morphologies into surface and internal structures, and elucidate their roles in enhancing the electric output performance of devices. Moreover, we systematically classify the methodologies employed for morphological preparation into physical and chemical approaches, thereby furnishing a comprehensive survey of the diverse techniques. Subsequently, typical applications of TENGs with special morphology divided by energy harvesting and self-powered sensors are presented. Finally, an overview of the challenges and future trajectories pertinent to TENGs is conducted. Through this endeavor, the aim of this article is to catalyze the evolution of further strategies for enhancing performance of TENGs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Supercapacitor Materials and Fabrication
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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