Slow relaxation of longitudinal multispin orders in weakly and strongly coupled two-spin systems.

Longitudinal multispin order (LOMO) corresponds to a nonequilibrium population distribution in spin systems that exhibit scalar (J), dipolar, or quadrupolar coupling. We investigated the relaxation of longitudinal two-spin order (2-LOMO) in systems that had either weakly or strongly J-coupled spins. Our results indicated longer relaxation times for the 2-LOMO state compared with the corresponding longitudinal single-spin state (1-LOMO). Accessing nuclear spin states that have relaxation times longer than T(1), without the use of external contrast agents, is potentially useful for in vivo imaging and also for studying systems using dynamically hyperpolarized nuclear spins where longer life times are sought to increase the time available to study (bio)chemical events.

A combination of spectral re-alignment and BTEM for the estimation of pure component NMR spectra from multi-component non-reactive and reactive systems.

The deconvolution of multi-component mixtures in NMR spectroscopy is a challenging problem due to the spectral non-linearities. In the present contribution, two data sets were studied (A) 10 samples of a four-component non-reactive mixture measured with 1H, 13C, 19F, 31P NMR and (B) a three-solute cyclo-addition reaction measured with 13C NMR. Both data sets were treated with a re-alignment procedure to correct for the non-stationary chemical shifts, followed by band-target entropy minimization (BTEM) analysis. For data set A, quite good spectral estimates of the two hydrogen-containing species, four carbon-containing species, two fluorine-containing species and two phosphorus-containing species were obtained from the multi-component data. For data set B quite good spectral estimates of all three carbon-containing reactants were obtained as well as their relative concentration profiles. The present contribution using model systems indicates the usefulness of re-alignment procedures for correcting non-stationary characteristics, prior to self-modeling curve resolution (SMCR), and the potential for investigating more complex problems.

Development of 2D band-target entropy minimization and application to the deconvolution of multicomponent 2D nuclear magnetic resonance spectra.

Spectral reconstruction from multicomponent spectroscopic data is the frequent primary goal in chemical system identification and exploratory chemometric studies. Various methods and techniques have been reported in the literature. However, few algorithms/methods have been devised for spectral recovery without the use of any a priori information. In the present studies, a higher dimensional entropy minimization method based on the BTEM algorithm (Widjaja, E.; Li, C.; Garland, M. Organometallics 2002, 21, 1991-1997.) and related techniques were extended to large-scale arrays, namely, 2D NMR spectroscopy. The performance of this novel method had been successfully verified on various real experimental mixture spectra from a series of randomized 2D NMR mixtures (COSY NMR and HSQC NMR). With the new algorithm and raw multicomponent NMR alone, it was possible to reconstruct the pure spectroscopic patterns and calculate the relative concentration of each species without recourse to any libraries or any other a priori information. The potential advantages of this novel algorithm and its implications for general chemical system identification of unknown mixtures are discussed.