Chemistry Summer Research Program
Bio Inspired Catalysis
The REU student will carry out the synthesis of copper complexes under anaerobic conditions (i.e. using the glovebox and/or Schlenk techniques) and these will be used as catalysts for various organic transformations. The REU student will also gain experience in: i) analyzing the organic products derived from the catalytic functionalization of C-H and C=C substrates (using GC, HPLC, NMR) and; ii) characterizing “reactive” Cu species using a wide array of structural and spectroscopic techniques (X-ray diffraction analysis, EPR, NMR, UV-vis, etc.).
This research will be conducted in the Garcia-Bosch lab.
Cartilage related injury is a huge source of disability and financial burden in the United States, especially in aging populations. Repair of articular cartilage is particularly difficult because of the complex structure that defines its function and its lack of self-healing capability. Hydrogels are particularly suited to this problem because of their three-dimensional structure which is able to impart similar mechanical properties and encourage encapsulated cells to develop chondrogenic phenotypes. Through the use of a methacrylated chondroitin sulfate, a key component of natural cartilage, and additives of graphene oxide and poly-lysine, we hope to create an injectable hydrogel that can be crosslinked in situ. This will allow for the creation of layers of hydrogel that can have differing compositions better able to mimic natural cartilage.
After synthesizing the gels, dynamic mechanical analysis will be used to determine the strength of the gels and how well they mimic cartilage. This will help determine the ideal composition of graphene oxide and polylysine as well as the ideal crosslinking density. In addition, cytocompatibility studies will be done with the possibility of the student collaborating on them if there is interest.
This research will be conducted in the Sydlik lab.
Machine Learning and Catalysis
Optimization and Automation of Chemical Reactions through Machine-Readable Representations of Synthetic Procedures
The growth of automation technologies has already demonstrated benefits such as efficiency and safety by removing the need for a human researcher during operation, while computer-assisted synthetic planners offer greener, more potent routes towards desired and often complex products. However, experimental methods in the literature are currently written in natural language, creating a barrier for data-driven approaches in chemistry. Without the development of a machine-readable representation, chemical synthesis and reaction optimization will remain laborious and error-prone, thus hindering the progress of chemical knowledge. Here, we will develop a standardized format for synthetic procedures that can be transferred onto different automated platforms in addition to being utilized for closed-loop optimization. At the end of this funding period, we will: 1) Integrate the format with the Emerald Cloud Labs software architecture and showcase a diverse range of automated reactions. 2) Design a closed-loop framework in which the format mediates the flow of process data between an automated platform and optimization algorithm. Our work will accelerate the expansion of chemical knowledge into regions unreachable by manual labor and inefficient optimization schemes by encouraging collaboration and enhancing the synthetic development process. Ultimately, this project will join other efforts in advancing the next scientific paradigm that is dominated by data.
This research will be conducted in the Gomes lab.
This research will be conducted in the Sullivan lab.
- Unofficial transcripts of each institution that you attended, regardless of whether you received a degree there.
- An official copy will be required in the event that you are accepted.
- Updated Resume or CV
- 1-page statement of purpose explaining previous experiences, your motivation for wanting to participate in this program, and your research interests. Please describe any barriers and challenges to gaining research experience that you have encountered.
- One letter of recommendation