Bio‐Based Nitriles via Heterogeneously Catalyzed Oxidative Decarboxylation of Amino Acids
Bibliographic record
Abstract
Increasing amounts of proteins are available as waste streams from agro-industry and biofuel production. Although the average protein content of these byproducts varies from 20 to 40 wt%,[1] this fraction is rarely used for industrial applications. Hydrolytic depolymerization, as an essential part of waste protein valorization into bio-based chemicals, results in aqueous mixtures of amino acids; further separation is demanding due to their zwitterionic nature.[2] Here, oxidative decarboxylation of amino acids into nitriles is proposed as a useful link in the valorization chain, because it not only provides a way around the separation issue, but even allows to recycle nitrogen into - often bifunctional - nitrile platform molecules. For instance, they give access to amines, amides, acids. The reaction is generally mediated by hypobromite (‘Br+’) species, which are often produced from halogenated reagents like N-bromosuccinimide[3], or by NaOCl induced oxidation of NaBr[4], together with large amounts of (in)organic waste. On the other hand, bromoperoxidases catalyze this transformation using H2O2 as oxidant, but selectivity is difficult to control.[5] A heterogeneous catalytic system was developed to mimic the halide oxidation activity of these enzymes. Proximity effects exerted by the layered double hydroxide (LDH) supported tungstate catalyst facilitate both halide oxidation[6] and subsequent decarboxylation.[7] Selective defunctionalization of amino acids into nitriles is achieved in aqueous media using catalytic amounts of bromide and H2O2 as green oxidant. Many naturally occurring amino acids were converted with excellent selectivity, often resulting in yields > 90% (Figure 1). The system is compatible with alcohols, amides, and even carboxylic acids, amines or guanidines after an appropriate neutralization step; methionine can be transformed into a nitrile-sulfone derivative. In addition, this system was successfully applied to convert wheat gluten, as an example of a protein-rich byproduct from the starch industry, into useful bio-based N-containing chemicals, thereby demonstrating the potential for closing the N-loop. Figure 1. Heterogeneous catalytic system for oxidative decarboxylation of amino acids. [1] T.M. Lammens, M.C.R. Franssen, E.L. Scott, J.P.M. Sanders, Biomass Bioenerg. 2012, 44, 168. [2] Y. Teng, E.L. Scott, A.N.T. van Zeeland, J.P.M. Sanders, Green Chem. 2011, 13, 624. [3] G. Laval, B.T. Golding, Synlett 2003, 4, 542. [4] J. Le Nôtre, E.L. Scott, M.C.R. Franssen, J.P.M. Sanders, Green Chem. 2011, 13, 807. [5] A. But, J. Le Nôtre, E.L. Scott, R. Wever, J.P.M. Sanders, ChemSusChem 2012, 5, 1199. [6] B. Sels, D. De Vos, M. Buntinx, F. Pierard, A. Kirsch-De Mesmaeker, P. Jacobs, Nature 1999, 400, 855. [7] L. Claes, R. Matthessen, I. Rombouts, I. Stassen, T. De Baerdemaeker, D. Depla, J.A. Delcour, B. Lagrain, D.E. De Vos, ChemSusChem, accepted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".