The 18th Annual ChEMS Research Forum will showcase departmental research advances in the areas of:
- Energy and Sustainability
- Nanotechnology and Materials
- Biotechnology and Biomedical Engineering
This one-day meeting will feature invited plenary speakers, oral presentations from faculty and students, and an extended poster session describing the latest department research results.
If you or your company shares an interest in chemical engineering and materials science, then this event offers a uniquely personal and informal view into the general research directions of the ChEMS department, its current research projects, and, most importantly, an opportunity to get to know the many talented graduate students that are at the heart of it all.
- Biomanufacturing Cardiac Cells from Human Pluripotent Stem Cells - Identification of Critical Quality Attributes and Process Parameters. Biomanufacturing cells and tissues from human pluripotent stem cells (hPSCs) typically strives to guide differentiation through developmentally relevant pathways in a well-defined, dynamic bioreactor environment. While great strides have been made in differentiating hPSCs to many somatic cell types, robust biomanufacturing remains a roadblock to the clinical progress of hPSC-derived cell and tissue therapies. In particular, scaling manufacturing to meet clinical needs, reducing cost, improving cell phenotypes, and improving process robustness are critical challenges. hPSC-derived cardiomyocytes have tremendous potential to restore cardiac function in heart failure patients. However, these cells suffer from poor survival and functional integration in preclinical models of heart disease. We have developed protocols to differentiate hPSCs to endothelial cells and cardiac fibroblasts, and demonstrated that the inclusion of these cells during cardiomyocyte biomanufacturing accelerates the acquisition of maturation phenotypes such as morphology, sarcomere protein expression, and calcium handling in the cardiomyocytes. Importantly, these heterotypic cell interactions must be provided to cardiac progenitor cells, allowing the cell types to co-differentiate. To reduce costs and improve the scale of cardiomyocyte biomanufacturing, we have transitioned 2D cardiomyocyte differentiation to 3D, reducing cost by approximately 85% and permitting the manufacturing of greater than one trillion cardiomyocytes in a 300 mL spinner flask bioreactor. To improve biomanufacturing process robustness, we have performed a multi-omic characterization of differentiating cardiomyocytes and utilized unbiased data analytics to identify genes, proteins, and metabolites that when measured before day 5 predict successful vs. failed batches at day 15, determined by the percentage of cells expressing cardiac troponin T. We envision that these multivariate predictive critical quality attributes can be used to more quickly identify failed batches and eventually lead to closed-loop control strategies to improve biomanufacturing process robustness.
- Effective strategies for hierarchically structured polymeric materials. Numerous natural materials have properties and performances that have inspired, intrigued, and motivated engineers. Examples include plant surfaces that are self-cleaning, adhesives that persist in aqueous environments, as well as insect shells that can harvest water from fog. Common amongst these natural materials are hierarchical structures, ranging from macromolecular design up to microscopic features and patterning. Unfortunately, an ongoing challenge with bio-inspired and bio-mimetic materials is identifying straight-forward, versatile techniques to recapitulate these intricate and complex designs. Research in Caroline Szczepanski’s group confronts this challenge using strategies based on polymer chemistry and polymer engineering to yield multi-scale ordering that improves performance for applications in coatings, adhesives, and biomaterials. This talk will highlight recent work from the Szczepanski group, including studies on how localized, in situ stress gradients can be leveraged to create multi-scale, hierarchical structures at an interface. Additionally, recent efforts employing bio-inspired chemistries to improve dental adhesive performance will also be presented.
- Machine Learning meets First Principles and Big Data: Towards a Periodic Table of Materials and Reactions. Computational algorithms are now powerful enough that they can predict many properties of materials and chemical processes before they are synthesized/performed. By implementing and developing new approaches to calculate materials and chemical properties in supercomputers, the Mendoza group has predicted over 300,000 materials for energy capture, conversion, and storage (e.g. batteries and catalysts). The computations predicted several new materials that were later synthesized and tested in the lab. The in-silico creation of our large amount of materials has prompted us to create our own type of atlas of materials and reactions. We have implemented different machine learning methods to find further (materials or reaction) design principles. Some of the applications of the design principles of materials have been used towards developing an alternative way to generate and store energy (e.g. next-generation Li-batteries, H2 storage), prediction of materials with new properties (mechanical and electronic) and chemical reactions paths (CO2 reduction, artificial photosynthesis).