The combination of liquid chromatography (LC) and nuclear magnetic resonance (NMR) offers the potential of unparalleled chemical information from analytes separated from complex mixtures. However, the application of LC-NMR has been hindered by poor detection sensitivity. We develop a theoretical model for predicting signal-to-noise ratio (SNR) performance while scaling NMR detection cells for flowing experiments. The model includes the effects of separation parameters, coil geometry, and NMR acquisition parameters on SNR performance. Although the detector cell should be as large as possible to ensure adequate efficiency for a given separation, reducing the detector cell volume does not significantly degrade SNR. For example, our model predicts a 2-fold reduction in SNR for a 400-fold reduction in cell volume. The results of static NMR measurements of amino acids and peptides in a 50-nL-volume cell (~ 1 μg of each) demonstrate the performance of such a small volume NMR microcell. Using this 50-nL detector cell with microbore LC, two-dimensional LC-NMR chromatograms are shown for amino acid and peptide separations.
ASJC Scopus subject areas
- Analytical Chemistry