Lehigh University

About: Lehigh University is a education organization based out in Bethlehem, Pennsylvania, United States. It is known for research contribution in the topics: Catalysis & Fracture mechanics. The organization has 12684 authors who have published 26550 publications receiving 770061 citations.

XPS and FTIR Surface Characterization of TiO2 Particles Used in Polymer Encapsulation

Abstract: The surfaces of hydrophilic (P25) and hydrophobic (T805) TiO2 particles were characterized by X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR) to gain a better understanding of the adsorption mechanism of OLOA 370 (polybutene-succinimide pentamine) on TiO2 particles dispersed in styrene monomer prior to miniemulsion encapsulation polymerizations. XPS analysis revealed that both the P25 and T805 TiO2 particles had significant amounts of hydroxyl groups on their surfaces. The XPS results showed that the surface hydroxyl concentration on the hydrophilic (P25) particles was 3.3 OH/nm2, whereas the trimethoxy octyl silane (TMOS)-surface-modified hydrophobic (T805) particles unexpectedly contained 6.6 OH/nm2. This apparent increase in the hydroxyls was attributed to hydrolysis of −OCH3 on the TMOS. The majority of these groups, however, were apparently either not acidic or not accessible to the OLOA 370 in adsorption studies, where the concentration of reactive hydroxyls...

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Abstract: Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGANs) aimed at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and the text description as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and multiple discriminators arranged in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.

The attenuation of solar UV radiation in lakes and the role of dissolved organic carbon

Abstract: Diffuse attenuation coefficients (&) for solar UV radiation (UVR) (305, 320, 340, 380 nm, and PAR) were measured in the mixed layer of 65 lake sites in Alaska, Colorado, and Pennsylvania and the Bariloche region of Argentina. Integrated mixed layer samples of lake water were concurrently collected, and a multivariate approach was used to model Kd with a number of optical and chemical variables. Substantial variation in transparency was observed among lakes. Attenuation depths (zlYO) for UV-B radiation ranged from several centimeters to > 10 m. In some shallow, low DOC (dissolved organic C) lakes typical of high elevation ecosystems, substantial fluxes of UVR penetrated the entire water column. In deeper lakes with low DOC concentrations, high fluxes of UVR were found in a significant proportion of the mixed layer. Much of the among-lake variation in & (87-96%) was explained by differences in DOC concentration, which strongly influenced dissolved absorbance. On average, dissolved absorbance accounted for between 33% (for PAR) and 68% (for 305 nm) of Kd measured in situ. Throughout the solar UV-A and UV-B range, Kd was best estimated with a univariate power model based solely on DOC concentration. Models are also presented that relate absorption coefficients to Kd. These models can be used with archival DOC or color data to provide approximate estimates of UV transparency of lakes.