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Updated: Oct 7, 2026
Microwave-assisted Functionalization of Poly(ethylene glycol) and On-resin Peptides for Use in Chain Polymerizations and Hydrogel Formation
Published on: October 29, 2013
Microwave-driven engineering of starch-lipid complexes: Structural evolution, functional modulation, and molecular
1School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.
Abstract:
Driven by consumer demand for healthier foods, starch-lipid complexes have gained prominence for their enhanced resistance to enzymatic digestion. Microwave-assisted processing offers a highly efficient strategy to fabricate these complexes, utilizing electromagnetic effects to drive starch and lipid molecular reorganization. To elucidate the underlying mechanisms, this review establishes quantitative relationships between microwave processing parameters and the resulting structural and functional evolution. Furthermore, by integrating macroscopic experimental observations with molecular dynamics (MD) simulations, this work reveals atomic-scale interaction mechanisms, such as conformational changes, hydrogen bonding, and interfacial dynamics, that remain inaccessible to conventional experiments. Bridging experimental evidence with computational insights provides a robust theoretical basis for tailoring these assemblies and advancing the rational design of functional starchy foods with improved nutritional performance. Ultimately, this review emphasizes that future research should focus on deciphering competitive molecular interactions within intricate real-food matrices and establishing machine learning-driven predictive models to autonomously optimize processing parameters for scalable commercial applications.
