Triboelectric nanogenerators (TENGs) offer a self-sustaining power solution for marine regions abundant in resources but constrained by energy availability. Since their pioneering use in wave energy harvesting in 2014, nearly a decade of advancements has yielded nearly thousands of research articles in this domain. Researchers have developed various TENG device structures with diverse functionalities to facilitate their commercial deployment. Nonetheless, there is a gap in comprehensive summaries and performance evaluations of TENG structural designs. This paper delineates six innovative structural designs, focusing on enhancing internal device output and adapting to external environments: high space utilization, hybrid generator, mechanical gain, broadband response, multi-directional operation, and hybrid energy-harvesting systems. We summarize the prevailing trends in device structure design identified by the research community. Furthermore, we conduct a meticulous comparison of the electrical performance of these devices under motorized, simulated wave, and real marine conditions, while also assessing their sustainability in terms of device durability and mechanical robustness. In conclusion, the paper outlines future research avenues and discusses the obstacles encountered in the TENG field. This review aims to offer valuable perspectives for ongoing research and to advance the progress and application of TENG technology.

Macquarie University researchers used CRISPR technology to challenge textbook explanations for why albinism is rare in nature

Research team led by POSTECH Professor Hyungyu Jin develops technology to convert waste heat into electricity by controlling oxygen vacancies.

Jari Isohanni’s doctoral research in computer science at the University of Vaasa, Finland, demonstrates how smart packaging could become more widely available to the packaging industry in the future. Color-changing printing inks in printed packages and the machine-based recognition of subtle color changes offer cost-effective solutions for industries such as food and beverage, healthcare, logistics, and electronics. Previously, there has been no research on which color recognition method should be used and when. Isohanni's research filled this gap by comparing the applicability of machine learning and color difference methodologies to different situations.