Such a clever way to look at coherence transfer pathways in a new and fresh way.
Beaton, Alec A., Alexandria Guinness, and John M. Franck. “A Modernized View of Coherence Pathways Applied to Magnetic Resonance Experiments in Unstable, Inhomogeneous Fields.” The Journal of Chemical Physics 157, no. 17 (November 7, 2022): 174204.
https://doi.org/10.1063/5.0105388.
This article presents a standardized alternative to the traditional phase cycling approach employed by the overwhelming majority of contemporary Nuclear Magnetic Resonance (NMR) research. On well-tested, stable NMR systems running well-tested pulse sequences in highly optimized, homogeneous magnetic fields, the hardware and/or software responsible for traditional phase cycling quickly isolate a meaningful subset of data by averaging and discarding between 3/4 and 127/128 of the digitized data. In contrast, the new domain colored coherence transfer (DCCT) approach enables the use of all the information acquired from all transients. This approach proves to be particularly useful where multiple coherence pathways are required, or for improving the signal when the magnetic fields are inhomogeneous and unstable. For example, the authors’ interest in the nanoscale heterogeneities of hydration dynamics demands increasingly sophisticated and automated measurements deploying Overhauser Dynamic Nuclear Polarization (ODNP) in low-field electromagnets, where phase cycling and signal averaging perform suboptimally. This article demonstrates the capabilities of DCCT on ODNP data and with a collection of algorithms that provide robust phasing, avoidance of baseline distortion, and the ability to realize relatively weak signals amid background noise through signal-averaged correlation alignment. The DCCT schema works by combining a multidimensional organization of phase cycled data with a specific methodology for visualizing the resulting complex-valued data. It could be extended to other forms of coherent spectroscopy seeking to analyze multiple coherence transfer pathways.