School of Engineering Department of Chemical and Biological Engineering 73 Generation of Adversarial Chemical Reactions Supervisor: GAO Hanyu / CBE Student: OH Jintaek / BIEN Course: UROP1000, Summer Conventional generative models for the chemical reaction generation often uses the Variational Autoencoder (VAE). Whereas the Generative Adversarial Network (GAN) which is showing great performances in image and text generation, is not actively utilized as the generative model for the chemical reaction. This is due to the highly unstable training process and low reproducibility of GAN. Through the UROP 1000, different types of GAN were studied. Furthermore, latent-GAN which was originally intended for the novel drug design was examined and modified. Specifically, autoencoder part of the latent-GAN was reassembled to be used as the chemical reaction generation. In the future, reassembled autoencoder and GAN part will be trained and outputs will be analysed. Designing of the Chemical Process for Polyethylene Furanoate-based Materials Supervisor: HU Xijun / CBE Student: YANG Jianbo / CENG Course: UROP3100, Fall Now wood waste has accounted for a significant number of landfill capacity in Hong Kong, nearly up to 10%. Most disposed wood waste are wood pallets for cargo transportation and old-fashioned furniture. An applicable chemical process to transform wood waste into useful organic substrate is expected so that the pressure on landfill space in Hong Kong can be alleviated. Except for catalytic pyrolysis, there is also an advanced solution treating process that can fabricate transparent wood, which is a potential construction material. This process takes advantage of the hierarchical structure of wood and reduce influence on environment. The rest of the report will discuss about the transparent composite fabricated this semester and its potential modifications for more complicated functions.
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