The optically transparent electrospun fibrous membrane has been widely used in many fields due to its simple operation, flexible design, controllable structure, high specific surface area, high porosity, and unique excellent optical properties. This paper comprehensively summarizes the preparation methods and applications of an electrospun optically transparent fibrous membrane in view of the selection of raw materials and structure modulation during preparation. We start by the factors that affect transmittance among different materials and explain the light transmission mechanism of the fibrous membrane. This paper also provides an overview of the methods to fabricate a transparent nanofibrous membrane based on the electrospinning technology including direct electrospinning, solution treatment after electrospinning, heat treatment after electrospinning, and surface modification after electrospinning. It further summarizes the differences in the processes and mechanisms between different transparent fibrous membranes prepared by different methods. Additionally, we study the utilization of transparent as-spun membranes as flexible functional materials, namely alcohol dipstick, air purification, self-cleaning materials, biomedicine, sensors, energy and optoelectronics, oil–water separation, food packaging, anti-icing coating, and anti-corrosion materials. It demonstrates the high transparency of the nanofibers’ effects on the applications as well as upgrades the product performance.
The accurate prediction of storm‐time thermospheric mass density is always critically important and also a challenge. In this paper, an available prediction model is established by Long Short‐Term Memory (LSTM)‐based ensemble learning algorithms. However, the generalization ability of the deep learning model is often suspicious since training data and testing data are from the same data set in the conventional method. Therefore, in order to objectively validate the performance and generalization of the model, we utilize the GOCE data for training and the SWARM‐C data for testing to verify its performance mainly during the geomagnetic storm period. The results show that the LSTM‐based ensemble learning model (LELM) is robust under different geomagnetic activity levels and has good generalization ability for the different satellite data set. The prediction accuracy of the LELM is proved to be better than a common‐used empirical model (NRLMSISE‐00). Thus, our approach provides a promising way to give reliable and stable predictions of thermospheric mass density.
A naked-eye detector based on a rapid transmittance response to alcohol was designed to offer real-time and reusable detection of fruit freshness. To ensure the hydrophobicity of the fibrous membrane and high light transmission response to alcohol, fluorine-rich poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with low refractive index was selected as the shell layer while sodium alginate (SA) and polyvinyl alcohol (PVA) were selected as the core layer for coaxial electrospinning.The core-shell fibrous detector was obtained by treatment with CaCl2 to form a stable
The development of smart window and its accessories based on the notion of adaptability to provide somatoform and visual comfort played a critical role in enhancing indoor quality. Especially, the...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.