No abstract

Liquid–solid triboelectrification technology provides a new way to collect hydropower, while the high cost, complexity, and easily damaged microstructures of the triboelectric nanogenerator electrode materials highly limit their practical applications. In this study, a new type of organic coating triboelectric nanogenerator is fabricated using acrylate resin as the friction layer material. To further improve the solid–liquid triboelectrification performance and the hydrophobicity of the coating, fluorine-containing materials were added to the acrylic resin. As a non-microstructure-dependent film, its preparation process is simple and large area prepared, which can be achieved by modifying some commonly used anticorrosion and antifouling coatings in engineering. This packaged organic coating triboelectric nanogenerator provides good stability and high-output performance, which can easily light several commercial light-emitting diodes (LEDs) on a model ship by collecting the wave energy during the voyage. This new type of triboelectric nanogenerator based on the coating material has the advantages of simple process, low cost, and large-area preparation, which combines the performance of the coating itself with the power generation function, and have potential promising practical applications in ocean energy collection and utilization, self-powered sensing, and other fields.

In this paper, we firstly investigate issues of low carbon supply chain including one retailer and one manufacturer in the context of joint information asymmetry and cap-and-trade mechanism, where the retailer is a leader and faces a stochastic demand, and the manufacturer is a follower and keeps private information in carbon emissions. Our aim is to design an incentive contract to make the manufacturer disclose the carbon information. Furthermore, we quantify the effects of the carbon price, the carbon emissions and the carbon quota on the supply chain model and design an incentive contract for improving supply chain performance. Finally, we give a couple of numerical examples and undertake sensitivity analysis to illustrate the proposed model and provide some managerial inferences in the conclusions.