Superhydrophobic Self-Cleaning Membranes Made by Electrospinning

A superhydrophobic and photocatalytic composite fiber material is developed using polystyrene polydimethylsiloxane and graphitic nitride and then thoroughly characterized. SEM is used to determine the nanostructure of the fiber material, and the contact and sliding angles are measured to test the obtained fibers for their hydrophobicity. The degradation of methylene blue is used to monitor the photocatalytic activity of the created materials. This serves to create a self-cleaning surface where hydrophilic pollutants are repelled from the surface due to the low sliding angle; the lower-surface-tension pollutants resist wetting the surface and can be thoroughly washed off; and photocatalytical oxidation can degrade pollutants that fully wet the fabric, allowing the surface to recover.     

What's in a Decade? Transforming Faces Through Time

How can one visually characterize photographs of people over time? In this work, we describe the Faces Through Time dataset, which contains over a thousand portrait images per decade from the 1880s to the present day. Using our new dataset, we devise a framework for resynthesizing portrait images across time, imagining how a portrait taken during a particular decade might have looked like had it been taken in other decades. Our framework optimizes a family of per-decade generators that reveal subtle changes that differentiate decades—such as different hairstyles or makeup—while maintaining the identity of the input portrait. Experiments show that our method can more effectively resynthesizing portraits across time compared to state-of-the-art image-to-image translation methods, as well as attribute-based and language-guided portrait editing models. Our code and data will be available at facesthroughtime.github.io.