Amino acids are the building blocks for protein biosynthesis and find use in myriad industrial applications including in food for humans, in animal feed, and as precursors for bio-based plastics, among others. However, the development of efficient chemical methods to convert abundant and renewable feedstocks into amino acids has been largely unsuccessful to date. To that end, here we report a heterogeneous catalyst that directly transforms lignocellulosic biomass-derived α-hydroxyl acids into α-amino acids, including alanine, leucine, valine, aspartic acid, and phenylalanine in high yields. The reaction follows a dehydrogenation-reductive amination pathway, with dehydrogenation as the rate-determining step. Ruthenium nanoparticles supported on carbon nanotubes (Ru/CNT) exhibit exceptional efficiency compared with catalysts based on other metals, due to the unique, reversible enhancement effect of NH3 on Ru in dehydrogenation. Based on the catalytic system, a two-step chemical process was designed to convert glucose into alanine in 43% yield, comparable with the well-established microbial cultivation process, and therefore, the present strategy enables a route for the production of amino acids from renewable feedstocks. Moreover, a conceptual process design employing membrane distillation to facilitate product purification is proposed and validated. Overall, this study offers a rapid and potentially more efficient chemical method to produce amino acids from woody biomass components.
KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Ms. Kangjia Lu for providing the membrane distillation devices. This work was supported by the National University of Singapore Young Investigator award and the Ministry of Education, Singapore Tier-2 grant, respectively (R-279-000-462-112 and R-279-000-464-133), National Natural Science Foundation of China (Grants 91545203, 21690082, and 21473141), and the Fundamental Research Funds for the Central Universities (Grant 20720160029). SPring-8 is acknowledged for providing EXAFS and XANES analysis (Proposal 2017A1256). E.M.K., and G.T.B. thank the US Department of Energy (DOE) Energy Efficiency and Renewable Energy (EERE) Bioenergy Technologies Office (BETO) for funding under Contract DE-AC36-08GO28308 with National Renewable Energy Laboratory. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes.