Lignin-Derived Syringol and Acetosyringone from Palm Bunch Using Heterogeneous Oxidative Depolymerization over Mixed Metal Oxide Catalysts under Microwave Heating

Biomass valorization to building block chemicals in food and pharmaceutical industries has tremendously gained attention. To produce monophenolic compounds from palm empty fruit bunch (EFB), EFB was subjected to alkaline hydrothermal extraction using NaOH or K2CO3 as a promotor. Subsequently, EFB-derived lignin was subjected to an oxidative depolymerization using Cu(II) and Fe(III) mixed metal oxides catalyst supported on γ-Al2O3 or SiO2 as the catalyst in the presence of hydrogen peroxide. The highest percentage of total phenolic compounds of 63.87 wt% was obtained from microwave-induced oxidative degradation of K2CO3 extracted lignin catalyzed by Cu-Fe/SiO2 catalyst. Main products from the aforementioned condition included 27.29 wt% of 2,4-di-tert-butylphenol, 19.21 wt% of syringol, 9.36 wt% of acetosyringone, 3.69 wt% of acetovanillone, 2.16 wt% of syringaldehyde, and 2.16 wt% of vanillin. Although the total phenolic compound from Cu-Fe/Al2O3 catalyst was lower (49.52 wt%) compared with that from Cu-Fe/SiO2 catalyst (63.87 wt%), Cu-Fe/Al2O3 catalyst provided the greater selectivity of main two value-added products, syringol and acetosyrigone, at 54.64% and 23.65%, respectively (78.29% total selectivity of two products) from the NaOH extracted lignin. The findings suggested a promising method for syringol and acetosyringone production from the oxidative heterogeneous lignin depolymerization under low power intensity microwave heating within a short reaction time of 30 min.

Towards Experimental Handbooks in Catalysis

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.