NMR diffusion analysis of catalytic conversion mixtures from lignocellulose biomass using PSYCHE-iDOSY

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The component analysis and structure characterization of complex mixtures of biomass conversion remain a challenging work. Hence, developing effective and easy to use techniques is necessary. Diffusion-ordered NMR spectroscopy (DOSY) is a non-selective and non-invasive method capable of achieving pseudo-separation and structure assignments of individual compounds from biomass mixtures by providing diffusion coefficients (D) of the components. However, the conventional 1H DOSY NMR is limited by crowded resonances when analyzing complex mixtures containing similar chemical structure resulting in similar coefficient. Herein we describe the application of an advanced diffusion NMR method, Pure Shift Yielded by CHirp Excitation DOSY (PSYCHE-iDOSY), which can record high-resolution signal diffusion spectra efficiently separating compounds in model and genuine mixture samples from cellulose, hemicellulose and lignin. Complicated sets of isomers (D-glucose/D-fructose/D-mannose and 1,2-/1,5-pentadiol), homologous compounds (ethylene glycol and 1,2-propylene glycol), model compounds of lignin, and a genuine reaction system (furfuryl alcohol hydrogenolysis with ring opening) were successfully separated in the diffusion dimension. The results show that the ultrahigh-resolution DOSY technique is capable of detecting and pseudo-separating the mixture components of C5/C6 sugar conversion products and its derivative hydrogenation/hydrogenolysis from lignocellulose biomass.

OriginalsprogEngelsk
TidsskriftGreen Energy and Environment
Vol/bind8
Udgave nummer5
Sider (fra-til)1409-1416
Antal sider8
ISSN2096-2797
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
The authors thank for National Natural Science Foundation of China ( 22075308 ) for financial support. We specially thanks to Dr. Bin Yuan provided valuable suggestion during the revision.

Publisher Copyright:
© 2022 Institute of Process Engineering, Chinese Academy of Sciences

ID: 343172003