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Lignin‐carbohydrate complexes (LCCs) present a unique opportunity for harnessing the synergy between lignin and carbohydrates for high‐value product development. However, producing LCCs in high yields remains a significant challenge. In this study, we address this challenge with a novel approach for the targeted production of LCCs. We optimized the AquaSolv Omni (AqSO) biorefinery for the synthesis of LCCs with high carbohydrate content (up to 60/100 Ar) and high yields (up to 15 wt%) by employing machine learning (ML). Our method significantly improves the yield of LCCs compared to conventional procedures, such as ball milling and enzymatic hydrolysis. The ML approach was pivotal in tuning the biorefinery to achieve the best performance with a limited number of experimental trials. Through Pareto front analysis, we identified optimal trade‐offs between LCC yield and carbohydrate content, discovering extensive regions that produce LCCs with yields of 8‐15 wt% and carbohydrate contents ranging from 10‐40/100 Ar. We measured the glass transition temperature (Tg), surface tension, and antioxidant activity of the LCCs. Notably, we found that LCCs with high carbohydrate content generally exhibit low Tg and surface tension. Our biorefinery concept, augmented by ML‐guided optimization, represents a significant step toward scalable production of LCCs with tailored properties.
Lignin‐carbohydrate complexes (LCCs) present a unique opportunity for harnessing the synergy between lignin and carbohydrates for high‐value product development. However, producing LCCs in high yields remains a significant challenge. In this study, we address this challenge with a novel approach for the targeted production of LCCs. We optimized the AquaSolv Omni (AqSO) biorefinery for the synthesis of LCCs with high carbohydrate content (up to 60/100 Ar) and high yields (up to 15 wt%) by employing machine learning (ML). Our method significantly improves the yield of LCCs compared to conventional procedures, such as ball milling and enzymatic hydrolysis. The ML approach was pivotal in tuning the biorefinery to achieve the best performance with a limited number of experimental trials. Through Pareto front analysis, we identified optimal trade‐offs between LCC yield and carbohydrate content, discovering extensive regions that produce LCCs with yields of 8‐15 wt% and carbohydrate contents ranging from 10‐40/100 Ar. We measured the glass transition temperature (Tg), surface tension, and antioxidant activity of the LCCs. Notably, we found that LCCs with high carbohydrate content generally exhibit low Tg and surface tension. Our biorefinery concept, augmented by ML‐guided optimization, represents a significant step toward scalable production of LCCs with tailored properties.
A series of Pd-based catalysts supported on oxides with different acid−base properties has been prepared, characterized, and used for the valorization of furfural into value-added chemicals, with polymethylhydroxysiloxane (PMHS) as a reducing agent being an alternative to the use of hydrogen gas and alcohols as hydrogen donors. PMHS is a polymeric waste product of the silicone industry and is also recognized as a sustainable reductant for epoxide hydrosilylations and diastereoselective radical reduction in organic chemistry. Moreover, it is nontoxic, water/air insensitive, and soluble in most organic solvents due to its low viscosity. So, PMHS has been employed for the reduction of furfural in the presence of supported Pd catalysts. Although all catalysts tested are catalytically active, those supported on Al 2 O 3 and SiO 2 showed complete conversion after 30 min of reaction at 303 K, whereas 27.7% was attained using MgO as support. 1PdAl 2 O 3 (1 wt % Pd) catalyst was initially very selective to furfuryl alcohol (80.4% yield of FOL), which was hydrogenated to tetrahydrofurfuryl alcohol (THFA) at longer reaction times, reaching yields of 48.8 and 33.4% of FOL and THFA, respectively, after 6 h. By increasing the amount of Pd until 5 wt %, the reaction evolves toward the formation of more hydrogenated products (THFA), although the amount of nondetected products also increases. The best FOL productivity data has been achieved with 1PdAl 2 O 3 , with a TOF value of 397 h −1 (mol FOL •mol Pd −1 •h −1 ) at 303 K after 30 min, with 180 μL of PMHS in ethanol, as solvent, while increasing both the amount of Pd to 2.5 wt % (2.5PdAl 2 O 3 ) and that of PMHS until 360 μL; under similar experimental conditions, 100 mol THFA mol Pd −1 •h −1 can be produced.
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