Break
AIChE Annual Meeting
2023
2023 AIChE Annual Meeting
Engineering Sciences and Fundamentals
Electrochemical Separations Toward Sustainability: Analytical Techniques and Emerging Applications II (Invited Talks)
Wednesday, November 8, 2023 - 4:40pm to 4:50pm
Apply and develop molecular simulation, statistical thermodynamics and machine learning methods to 1) investigate the self-assembly and phase transition of soft matter and biomolecule solutions; 2) engineer proteins for bioengineering applications such as biomaterial, antimicrobial therapeutics, cancer immunotherapy and clean energy applications such rare earth separation.
Teaching Interests
With holding both bachelor and PhD degrees in Chemical Engineering, I am confident to teach any core chemical engineering courses, at both undergraduate and graduate level. In addition, I am also interested in teaching courses related to my interdisciplinary research such as: numerical methods, high performance computing, data-driven drug design, statistical mechanics, and physical chemistry, computational biophysics, soft matter and interfacial phenomena.
Polypeptides are one of the most common and essential biomacromolecules for living systems. They have also become excellent candidates for a variety of engineering and biological applications due to their unique and tunable physical and biochemical properties. This talk demonstrates how a chemical engineer approaches and overcomes the challenges in the peptide engineering field, which include two essential aspects, fundamental mechanisms and end applications. First, we focus on combining molecular simulation and statistical mechanics to understand the thermodynamic consequence (phase transition and self-assembly) arising from complex molecular interactions between constituent building blocks spanning certain sequences. Quantitative phase diagrams are calculated for Alzheimerâs disease-related amyloid-forming peptides, and the results are validated by experimental measurements. The development of molecular models and investigations of fluid phase behavior for another common class of biomolecules such as phospholipid and small chiral molecules are also included. Second, we employ machine learning, and optimization methods to leverage our mechanistic understanding of peptide structure-activity relationship to engineer peptides for three multidisciplinary applications: (a) Design charge complementary co-assembly peptide pair (CATCH) that forms functional peptide nanofibers; (b) Design lanthanide-binding tag (LBT) peptides for green and selective separation of rare earth elements; (c) Design hybridized antimicrobial peptides (hAMP) against gram-negative and gram-positive bacteria.