Application of nanogenerators in acoustics based on artificial intelligence and machine learning

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Abstract

As artificial intelligence (AI) advances, it is critical to give conventional electronics the capacity to “think,” “analyze,” and “advise.” The need for intelligent, self-powered devices has increased due to recent significant developments in the computer field, namely, in the fields of AI and machine learning (ML). The use of nanogenerators in the area of acoustics is examined in this Review, with an emphasis on how they might be integrated with ML and AI. Innovative energy-harvesting devices called nanogenerators are able to produce electrical power from outside sources, such as vibrations in the air or mechanical movements. The study examines a number of acoustic applications for nanogenerators, such as energy harvesting, sound detection, noise monitoring, and acoustic sensing. Furthermore, the research highlights how AI and ML techniques enhance the performance of nanogenerators and enable more efficient acoustic applications through data analysis and model training. At the end of this Review, the future development prospects of nanogenerators based on AI and ML were discussed.

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Flexible triboelectric generator

Feng-Ru Fan, Zhong-Qun Tian, Zhong Wang (2012)

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Quantifying the triboelectric series

Haiyang Zou, Ying. Zhang, Litong Guo … (2019)

Triboelectrification is a well-known phenomenon that commonly occurs in nature and in our lives at any time and any place. Although each and every material exhibits triboelectrification, its quantification has not been standardized. A triboelectric series has been qualitatively ranked with regards to triboelectric polarization. Here, we introduce a universal standard method to quantify the triboelectric series for a wide range of polymers, establishing quantitative triboelectrification as a fundamental materials property. By measuring the tested materials with a liquid metal in an environment under well-defined conditions, the proposed method standardizes the experimental set up for uniformly quantifying the surface triboelectrification of general materials. The normalized triboelectric charge density is derived to reveal the intrinsic character of polymers for gaining or losing electrons. This quantitative triboelectric series may serve as a textbook standard for implementing the application of triboelectrification for energy harvesting and self-powered sensing.

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Triboelectric nanogenerators as new energy technology and self-powered sensors - principles, problems and perspectives.

Zhong Wang (2014)

Triboelectrification is one of the most common effects in our daily life, but it is usually taken as a negative effect with very limited positive applications. Here, we invented a triboelectric nanogenerator (TENG) based on organic materials that is used to convert mechanical energy into electricity. The TENG is based on the conjunction of triboelectrification and electrostatic induction, and it utilizes the most common materials available in our daily life, such as papers, fabrics, PTFE, PDMS, Al, PVC etc. In this short review, we first introduce the four most fundamental modes of TENG, based on which a range of applications have been demonstrated. The area power density reaches 1200 W m(-2), volume density reaches 490 kW m(-3), and an energy conversion efficiency of ∼50-85% has been demonstrated. The TENG can be applied to harvest all kinds of mechanical energy that is available in our daily life, such as human motion, walking, vibration, mechanical triggering, rotation energy, wind, a moving automobile, flowing water, rain drops, tide and ocean waves. Therefore, it is a new paradigm for energy harvesting. Furthermore, TENG can be a sensor that directly converts a mechanical triggering into a self-generated electric signal for detection of motion, vibration, mechanical stimuli, physical touching, and biological movement. After a summary of TENG for micro-scale energy harvesting, mega-scale energy harvesting, and self-powered systems, we will present a set of questions that need to be discussed and explored for applications of the TENG. Lastly, since the energy conversion efficiencies for each mode can be different although the materials are the same, depending on the triggering conditions and design geometry. But one common factor that determines the performance of all the TENGs is the charge density on the two surfaces, the saturation value of which may independent of the triggering configurations of the TENG. Therefore, the triboelectric charge density or the relative charge density in reference to a standard material (such as polytetrafluoroethylene (PTFE)) can be taken as a measuring matrix for characterizing the performance of the material for the TENG.