Join us for a webinar on “CASE (Computer-Assisted Structure Elucidation) Studies for Benchtop NMR system” presented by Dr. Ronald Soong, Senior Research Associate at the Environmental NMR Center, University of Toronto Scarborough. Explore the increasing popularity of benchtop NMR systems in undergraduate laboratories and gain insights into the selection of the most suitable system for educational objectives. Discover how artificial intelligence software, specifically Mnova Structure Elucidation, is employed to assess the impact of magnetic field strength on structure elucidation accuracy, comparing results with a high-field NMR spectrometer for comprehensive analysis of benchtop systems in undergraduate-level structure determination.
Dr. Ronald Soong
Senior Research Associate – Environmental NMR Center, University of Toronto Scarborough
Ronald Soong graduated from the University of Toronto with a Ph.D. in Physical Chemistry. Later, he did 2 years of postdoctoral studies at the University of Michigan where he developed solid-state NMR pulse sequences for membrane protein structure determination under the supervision of Prof. Ramamoorthy. In 2010, he joined the Environmental NMR Centre as Senior Scientist and NMR Manager. Currently, Dr. Ronald Soong is developing NMR techniques and applications for both high field and benchtop NMR system with focus on environmental science, mixture analysis and chemical education.
The increasing popularity of benchtop (BT) NMR systems has led to their widespread adoption in undergraduate laboratories globally. These systems, characterized by their affordability due to the absence of a superconducting magnetic core and user-friendly operation, align well with the learning goals set in undergraduate organic chemistry curricula. Given the range of available BT NMR systems (e.g., 43, 60, 80, and 100 MHz), instructors may find it challenging to select the most suitable system for their educational objectives. When employed as tools for structure elucidation, the primary focus often revolves around solving chemical structures, prompting questions about the magnetic field strength requirements for de novo structure elucidation for BT NMR system. To address this inquiry, we utilized an artificial intelligence (AI) software packages: Mnova Structure Elucidation (v 14.2.3) from Mestrelab Research. This software package offer impartial and distinct metrics to assess the impact of magnetic field strength on the accuracy of determined structure. To facilitate comparison, results obtained from these BT magnetic field strengths were juxtaposed with those derived from a high-field NMR (500 MHz) spectrometer. This comprehensive analysis provides insights into the advancements and limitations of the current BT systems for structure determination at a undergraduate level.