Triboelectric nanogenerators (TENGs), using Maxwell's displacement current as the driving force, can effectively convert mechanical energy into electricity. In this work, an extensive review of theoretical models of TENGs is presented. Based on Maxwell's equations, a formal physical model is established referred to as the quasi-electrostatic model of a TENG. Since a TENG is electrically neutral at any time owing to the low operation frequency, it is conveniently regarded as a lumped circuit element. Then, using the lumped parameter equivalent circuit theory, the conventional capacitive model and Norton's equivalent circuit model are derived. Optimal conditions for power, voltage, and total energy conversion efficiency can be calculated. The presented TENG models provide an effective theoretical foundation for understanding and predicting the performance of TENGs for practical applications.

This paper describes an adaptable system which is able to perform manipulation operations (such as Peg-in-Hole or Laying-Down actions) with flexible objects. As such objects easily change their shape significantly during the execution of an action, traditional strategies, e.g, for solve path-planning problems, are often not applicable. It is therefore required to integrate visual tracking and shape reconstruction with a physical modeling of the materials and their deformations as well as action learning techniques. All these different submodules have been integrated into a demonstration platform, operating in real-time. Simulations have been used to bootstrap the learning of optimal actions, which are subsequently improved through real-world executions. To achieve reproducible results, we demonstrate this for casted silicone test objects of regular shape. Note to Practitioners - The aim of this work was to facilitate the setup of robot-based automation of delicate handling of flexible objects consisting of a uniform material. As examples, we have considered how to optimally maneuver flexible objects through a hole without colliding and how to place flexible objects on a flat surface with minimal introduction of internal stresses in the object. Given the material properties of the object, we have demonstrated in these two applications how the system can be programmed with minimal requirements of human intervention. Rather than being an integrated system with the drawbacks in terms of lacking flexibility, our system should be viewed as a library of new technologies that have been proven to work in close to industrial conditions. As a rather basic, but necessary part, we provide a technology for determining the shape of the object when passing on, e.g., a conveyor belt prior to being handled. The main technologies applicable for the manipulated objects are: A method for real-time tracking of the flexible objects during manipulation, a method for model-based offline prediction of the static deformation of grasped, flexible objects and, finally, a method for optimizing specific tasks based on both simulated and real-world executions.

Contact electrification (CE) involves a complex interplay of physical interactions in realistic material systems. For this reason, scientific consensus on the qualitative and quantitative importance of different physical mechanisms on CE remains a formidable task. The CE mechanism at a water/polymer interface is a crucial challenge owing to the poor understanding of charge transfer at the atomic level. First-principle density functional theory (DFT), used in the present work, proposes a new paradigm to address CE. Our results indicate that CE follows the same trend as the gap between the highest occupied and lowest unoccupied molecular orbitals (HOMO and LUMO) of polymers. Electron transfer occurs at the outmost atomic layer of the water/polymer interface and is closely linked to the functional groups and atom locations. When the polymer chains are parallel to the water layer, most electrons are transferred; conversely, if they are perpendicular to each other, the transfer of charges can be ignored. We demonstrate that a decrease in the interface distance between water and the polymer chains leads to CE in quantitative agreement with the electron cloud overlap model. We finally use DFT calculations to predict the properties of CE materials and their potential for triboelectric nanogenerator energy harvesting devices.

Energy harvesting from mechanical motions has immense applications such as self-powered sensors and renewable energy sources powered by ocean waves. In this context, the triboelectric nanogenerator is the cutting-edge technology that can effectively convert ambient mechanical energy into electricity through the Maxwell's displacement current. While further improvements of the energy conversion efficiency of triboelectric nanogenerators critically depend on theoretical modeling of the energy conversion process, to date only models based on single-relative-motion processes have been explored. Here, we analyze energy harvesting of triboelectric nanogenerators using a three-dimensional model in a linear-sliding mode and demonstrate a design of triboelectric nanogenerators that have a 77.5% enhancement in the average power in comparison with previous approaches. Moreover, our model shows the existence of a DC-like bias voltage contained in the basic AC output from the energy conversion, which makes the triboelectric nanogenerators an energy source more pliable than the traditional AC power generation systems. The present work provides a framework for systematic modeling of triboelectric nanogenerators and reveals the importance of obtaining direct analytical insight in understanding the current output characteristics of the triboelectric nanogenerators. Incorporating our model analysis in future designs of triboelectric nanogenerators is beneficial for increasing the energy conversion power and may provide insights that can be used in engineering the profile of the output current of the nanogenerators.