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Chapitre D'ouvrage Année : 2023

Intrinsically quantitative 2D HSQC NMR: a tool for deciphering complex mixtures

Résumé

Analysis of complex mixtures is of main interest since they are ubiquitous in the nature. Thus, it becomes challenging to decipher their complexity by developing NMR methods able to identify but also to quantify molecules in the most accurate way. 1D 1H NMR is highly utilized for quantifying molecules but suffers from many overlaps making this approach limited. 2D NMR is a great manner to improve the signal dispersion over 2 dimensions and the 1H-13C HSQC experiment is the best choice to improve the spectral resolution despite a lower sensitivity. However, due to its multiple pulse nature, the quantitativity of HSQC is impacted and biased absolute concentrations are derived. In this chapter, we highlight the potential of a class of methods called intrinsically quantitative 2D HSQC. They are generated by designing pulse sequences able for each correlation peak to maintain constant or to compensate JHH, JCH, off resonances and T2 effects responsible for the degradation of the quantitativity. Pioneer experiments as Q-HSQC and HSQC0 are detailed as well as their improved versions and applicative examples are given. Then, these major techniques are compared to show their performances and limits. Finally, recent methodological developments are proposed and discussed to enhance the potential of future intrinsically quantitative 2D-based complex mixture analyses.
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Dates et versions

hal-04242500 , version 1 (15-10-2023)

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  • HAL Id : hal-04242500 , version 1

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Jonathan Farjon. Intrinsically quantitative 2D HSQC NMR: a tool for deciphering complex mixtures. Annual reports in NMR, 2023. ⟨hal-04242500⟩
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