June 1, 2021 Webinar Speakers

Our next webinar will take place via the internet on Tuesday June 1st at 11AM EDT/4PM GMT. Sign up on our mailing list to receive the Zoom link!

We hope to see/hear from you all at one of our sessions or as one of the next speakers. If you are an early career scientist and would like to present your research, don't hesitate to submit an abstract today! For now, please learn more about our current speakers and their research below. We also thank the generous support from Cell Reports Physical Science.

Our featured speakers this week are Margaret Gerthoffer (graduate student, Pennsylvania State University, USA) and Dr. Gabe Gomes (postdoc, University of Toronto, Canada). The seminar will be guest moderated by Prof. Bobby Arora from New York University.


Margaret Gerthoffer (on twitter @GerthofferM)

Biography: Margaret Gerthoffer graduated from Seton Hill University in 2014 with a B.S. in Biochemistry. She is currently a rising fourth-year PhD Candidate in Chemistry in the Elacqua Group at Penn State University, where she has been highly involved in an NSF-sponsored Center for Nanothread Chemistry (CNC) founded by Dr. John Badding since 2018. Her graduate work focuses on forming novel sequence-specific polymers of various scaffolds in the effort to form donor-acceptor networks and unique tensile-strength materials. She also volunteers as a part of the departmental Climate and Diversity Committee and is co-chair of Post-Doc/Graduate Student committee of the CNC, where she edits YouTube videos, develops professional development activities, and promotes equitable learning opportunities.

Title of Talk: Designing the Thinnest Threads of Diamond using Supramolecular Chemistry

Abstract: Carbon allotropes have long been praised for their unique electronic and mechanical properties, justifying their widescale use in materials expressing finely tuned hardness, electrical conductivity, and geometrical shape. The solid-state slow compression of aromatics has recently revealed the formation of a new carbon allotrope through a one-dimensional cycloaddition polymerization. This allotrope, known as a nanothread, is considered the thinnest thread of diamond due to its sp3 character and incredibly thin architecture. Nanothreads neighbor their carbon allotrope predecessors in possessing extraordinarily high tensile strength and remarkable crystallinity for a 1-D polymer. However, nanothread formation requires extraordinary pressures for their synthesis (c.a. 23 GPa), which prevents their use for industrial scale syntheses due to low volume production on the microgram scale. Here, I will discuss the current efforts to study the reaction progression from π-stacked sp2-hybridized monomers into one-dimensional sp3 architectures. This effort sought to preferentially design precursor monomers favoring nanothread formation by i) reducing aromaticity to lower polymerization barriers and ii) pre-organize desired external functionalities using noncovalent interactions from an sp2 monomer ‘sacrifice’. By using in situ X-ray diffraction and Raman spectroscopy, the crystallinity, evolution of changes in intermolecular interactions, and emergence of photoluminescence was monitored to study nanothread formation. Both approaches realized a reduced initiation of polymerization through selective monomer choice of furan and phenol:pentafluorophenol, respectively. Moreover, the inclusion of external functional units in the compression of phenol:pentafluorophenol has illustrated a glimpse towards reaction mechanism from keto-enol tautomerization evident from IR spectroscopy. The compression of these diverse monomer units from carefully selected monomers lends insight towards the further design of complex nanothreads, to contribute towards large-scale industrial syntheses by reducing pressure and targeted nanothread design from precursor crystal structures.

DOI: 1)


DR. GABE GOMES (on twitter @gabepgomes)

Biography: In January 2022, Gabe will start his independent career at Carnegie Mellon University, jointly appointed at the Department of Chemistry and Department of Chemical Engineering. The Gomes Group @ CMU research program will be the interface between machine learning, organic chemistry, and robotic synthesis, with aims to develop new platforms for reaction discovery with emphasis on catalysis. Gabe’s goal is to establish a program focused on the development of new chemical reactions, pioneering research and training the next generation of chemists and chemical engineers. Gabe earned his Ph.D. in 2018 from Florida State University, under the guidance of Professor Igor V. Alabugin. At FSU, Gabe's research was centered on the relationship between molecular structure and reactivity, focusing on the development and applications of stereoelectronic effects. In 2019, Gabe joined the University of Toronto as a Postdoctoral Research Fellow in the Matter Lab, led by Professor Alán Aspuru-Guzik. In 2020, Gabe was awarded the NSERC Banting Postdoctoral Fellowship with the project "Designing Catalysts with Artificial Intelligence."

Title of Talk: Mapping the Property Space of Monodentate Organophosphorus Ligands for Catalysis

Abstract: The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies by synthesizing new drugs. Not only has the rise in popularity of metal-catalyzed cross-coupling reactions enabled us to make existing processes more efficient, but it also has allowed us to synthesize novel and unexplored molecules and materials, unlocking the technologies of the future. In metal catalysis, the choice of ligand often leads to the most significant impact on the reaction outcomes, such as yield or product selectivity. Identifying optimal metal-ligand combinations can be a laborious experimental process. This practice is often held back by the difficulty of meaningfully comparing results with different ligands. I will introduce our efforts to develop a platform for inverse design of catalysts utilizing high-throughput virtual screening (HTVS) and machine learning (ML), coupled with an extensive ligands database. One of the most used strategies in ML for metal catalysis is the development of models based on reactant parameterization, pioneered by the Sigman group. Given that the space of potential ligands is immense, this tactic can be very challenging. We have developed a platform aimed at data-driven ligand optimization and their inverse design. At the center of this strategy is Kraken, a database of descriptors for organophosphorus ligands that encompasses features that are most important for catalysis including conformational flexibility and ability for both coordinative and non-covalent bonding. We characterize this chemical space systematically and demonstrate how these descriptors can be estimated for millions of ligands using ML, thus nearly completely mapping out an immensely relevant space of ligands.

91 views0 comments

Recent Posts

See All