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Default Prediction of flow effects in quantitative NMR measurements #DNPNMR

From The DNP-NMR Blog:

Prediction of flow effects in quantitative NMR measurements #DNPNMR

Friebel, Anne, Thomas Specht, Erik von Harbou, Kerstin Münnemann, and Hans Hasse. “Prediction of Flow Effects in Quantitative NMR Measurements.” Journal of Magnetic Resonance 312 (March 2020): 106683.


https://doi.org/10.1016/j.jmr.2020.106683.


A method for the prediction of the magnetization in flow NMR experiments is presented, which can be applied to mixtures. It enables a quantitative evaluation of NMR spectra of flowing liquid samples even in cases in which the magnetization is limited by the flow. A transport model of the nuclei’s magnetization, which is based on the Bloch-equations, is introduced into a computational fluid dynamics (CFD) code. This code predicts the velocity field and relative magnetization of different nuclei for any chosen flow cell geometry, fluid and flow rate. The prediction of relative magnetization is used to correct the observed reduction of signal intensity caused by incomplete premagnetization in fast flowing liquids. By means of the model, quantitative NMR measurements at high flow rates are possible. The method is predictive and enables calculating correction factors for any flow cell design and operating condition based on simple static T1 time measurements. This makes time-consuming calibration measurements for assessing the influence of flow effects obsolete, which otherwise would have to be carried out for each studied condition. The new method is especially interesting for flow measurements with compact medium field NMR spectrometers, which have small premagnetization volumes. In the present work, experiments with three different flow cells in a medium field NMR spectrometer were carried out. Acetonitrile, water, and mixtures of these components were used as model fluids. The experimental results for the magnetization were compared to the predictions from the CFD model and good agreement was observed.




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