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Our group focuses on the development of experimental NMR spectroscopy methods to determine atomic-level structure and dynamics in molecular and materials sciences. We use the methods we develop to solve problems that were previously inaccessible in complex systems such as enzymes, catalytic nanoparticles, photovoltaic materials, and pharmaceutical compounds.

Our everyday tasks involve working out how the dynamics of nuclear spins can be controlled in new ways to report on systems of ever increasing complexity. We focus on making new experimental observations, and  we invent and then implement new NMR experiments using state-of-the-art technology. Not only do we use fundamental concepts in spectroscopy and NMR spectrometers to discover structure and dynamics, but we are increasingly developing strategies that combine NMR with quantum chemical calculations (DFT) on the one hand, or machine learning on the other hand, to achieve our goals.

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Latest publications

2019

S. Bjorgyinsdottir; B. J. Walder; N. Matthey; L. Emsley : Maximizing nuclear hyperpolarization in pulse cooling under MAS; Journal Of Magnetic Resonance. 2019-03-01. DOI : 10.1016/j.jmr.2019.01.011.
D. J. Kubicki; D. Prochowicz; A. Pinon; G. Stevanato; A. Hofstetter et al. : Doping and phase segregation in Mn2+- and Co2+-doped lead halide perovskites from Cs-133 and H-1 NMR relaxation enhancement; Journal of Materials Chemistry A. 2019-02-07. DOI : 10.1039/c8ta11457a.
C. E. Avalos; B. J. Walder; J. Viger-Gravel; A. Magrez; L. Emsley : Chemical exchange at the ferroelectric phase transition of lead germanate revealed by solid state Pb-207 nuclear magnetic resonance; Physical Chemistry Chemical Physics. 2019-01-21. DOI : 10.1039/c8cp06507a.

2018

A. C. Pinon; U. Skantze; J. Viger-Gravel; S. Schantz; L. Emsley : Core-Shell Structure of Organic Crystalline Nanoparticles Determined by Relayed Dynamic Nuclear Polarization NMR; Journal Of Physical Chemistry A. 2018-11-08. DOI : 10.1021/acs.jpca.8b08630.
F. M. Paruzzo; A. Hofstetter; F. Musil; S. De; M. Ceriotti et al. : Chemical shifts in molecular solids by machine learning; Nature Communications. 2018-10-29. DOI : 10.1038/s41467-018-06972-x.