DNP Enhanced NMR Spectroscopy

In 2010 we made a breakthrough by introducing a new technique called Dynamic Nuclear Polarisation Surface Enhanced NMR Spectroscopy.

NMR spectroscopy (often in conjunction with diffraction methods) is the method of choice for characterizing surfaces whenever it is possible, but many modern materials are simply below the sensitivity limit of detection for NMR.

The sensitivity of NMR thus poses the major limitation to characterization. Even when using highly porous nano-particles having high specific surface area, the concentration of NMR active nuclei often remains low, requiring many hours or even days to accumulate simple one-dimensional 13C NMR spectra with reasonable signal to noise ratios. This often prevents the acquisition of multi-dimensional correlation spectra, thus severely limiting the characterizing power of NMR.

In collaboration with C. Copéret at the ETH Zürich, we have addressed the question of how to selectively enhance the NMR signals form surfaces. The answer lies in transferring polarization from other sources. This quest led to a breakthrough in 2010.

Because it provides dramatic sensitivity enhancement, solid-state Dynamic Nuclear Polarization (DNP) NMR is currently emerging as a powerful tool to study samples that are available in limited amounts and previously inaccessible to NMR studies. Led notably by the Griffin group at MIT, impressive DNP enhancements have been observed in biological samples such as amyloid fibrils, or membrane proteins.

In October 2010 our group published a paper introducing the concept of Dynamic Nuclear Polarization Surface Enhanced NMR Spectroscopy (DNP SENS), where were showed how DNP can be use to selectively enhance the NMR signals from surfaces through a combination of incipient wetness impregnation and cross polarization methods. We have now applied this to inorganic and hybrid materials, and demonstrated that the gain in time provided by carbon-13 or silicon-29 DNP SENS (up to a factor 100′ 000 !) allows the fast and detailed structural characterization of surface bonding patterns and local conformations of surface species in hybrid mesoporous materials at natural abundance.

These elementary steps, made so far constitute a landmark achievement in the field, producing a loud echo in the community and paving the way for the fine structural characterization of increasingly challenging surfaces species in materials science. We are now building on this recent pioneering work, and hoping to further push back the barriers to the broadband applicability of DNP SENS to increasingly complex materials, including key modern heterogeneous catalysts currently not amenable to NMR characterization.

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