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Published in IntechOpen, 2019
Recommended citation: D. E. Christiansen, S. Mehraeen, Impact of active layer morphology, density of states, charge carrier concentration, and local charge density fluctuations on bimolecular recombination of bulk heterojunction solar cells: A theoretical perspective, IntechOpen, 978-1-78984-742-0, 2019. https://www.intechopen.com/chapters/66902
Published in Chemical Science, 2020
Recommended citation: H. Wang, D. E. Christiansen, S. Mehraeen, G. Cheng, Winning the fight against biofilms: the first six-month study showing no biofilm formation on zwitterionic polyurethanes, Chemical Science, 11, 2020. https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc06155j
Published in Biomaterials Science, 2020
Recommended citation: H. Wang, X. Liu, D. E. Christiansen, S. Fattahpour, K. Wang, H. Song, S. Mehraeen, G. Cheng, Thermoplastic polyurethane with controllable degradation and critical anti-fouling properties, Biomaterials Science, 9, 2021. https://pubs.rsc.org/en/content/articlelanding/2021/bm/d0bm01967d
Published in Chemical Engineering Research and Design, 2022
Recommended citation: D. E. Christiansen, G. Cheng, S. Mehraeen, Hydration and ion interactions of zwitterionic homopolymers with varying carbon spacer lengths, Chemical Engineering Research and Design, 186, 2022. https://www.sciencedirect.com/science/article/abs/pii/S0263876222003811
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Undergraduate/Graduate workshop series, University of Illinois at Chicago, 2022
Series of python-intensive workshops designed to teach data science subjects to chemistry-minded students
Undergraduate/Graduate lecture, Tulane University Department of Chemical Engineering, 2025
Generative artificial intelligence (GenAI) is rapidly emerging as a powerful new tool in chemical engineering, with the potential to fundamentally change how chemical processes are designed, documented, and evaluated. Unlike traditional machine-learning approaches focusing on prediction or optimization within predefined structures, GenAI models can generate new content—such as process flow diagrams, equipment specifications, design alternatives, and documentation—directly from learned patterns in historical data and engineering knowledge bases.