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Updated: May 17, 2026

Microwave-Assisted Extraction of Phenolic Compounds and Antioxidants for Cosmetic Applications Using Polyol-Based Technology
Published on: August 23, 2024
A comparative study on in-situ and ex-situ microwave catalytic co-pyrolysis for phenolic bio-oil production through
Ahmed Elsayed Mahmoud Fodah1, Ahmed Mahmoud Ahmed2, Xiayu Liu2
1State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; College of Agricultural Engineering, Al-Azhar University, Cairo 11765, Egypt.
Abstract:
The production of phenolic-rich bio-oil from biomass is a promising route for sustainable chemical generation. This study investigates the effects of the co-feeding ratio of corn stover and rice husk and the Fe-biochar catalyst ratio on bio-oil and phenolic yields under microwave heating in both in-situ and ex-situ catalytic modes. Increasing the rice husk proportion and catalyst ratio in the in-situ mode enhanced microwave absorption, resulting in rapid internal heating (48 °C/min), higher reaction temperature (710 °C), and intensified secondary cracking, which favored gas formation. In contrast, the ex-situ mode enabled controlled vapor-phase upgrading, limiting excessive cracking and preserving condensable products. The highest bio-oil yield of 52 w.t% with 40 w.t% phenolic content was obtained at a corn stover to rice husk ratio of 25:75 with 15 % catalyst in the ex-situ configuration. Mechanistically, rice husk contributes lignin for phenolic formation and mineral components that enhance microwave absorption, while the catalyst promotes lignin depolymerization, deoxygenation, and aromatization. Statistical modelling showed that response surface methodology accurately predicted bio-oil and phenolic yields with high reliability (R2 = 0.90 and 0.94 vs R2 = 0.86 and 0.67), indicating structured reaction behavior. Machine learning models effectively captured nonlinear relationships among heating performance, gas yield, and biochar yield, but showed lower accuracy in predicting bio-oil due to complex multistep formation pathways. The combined approach provides a comprehensive understanding of process optimization and reaction mechanisms.

