Structurally Intelligent Recognition and Enrichment of Photo‐Responsive Dual‐Monomer Imprinted Polymers for β‐Sitosterol in <i>Camellia</i> Oil

ABSTRACT To address the challenge of selectively extracting β‐sitosterol from Camellia oil—a complex, lipid‐rich matrix containing structurally similar sterols—a photo‐responsive molecularly imprinted polymer (MIP) was developed using a hybrid functional monomer system. Functional monomers were systematically screened via UV–Vis spectroscopy and density functional theory (DFT) to ensure molecular‐level selectivity and optimal cavity matching. The imprinting system combined methacrylic acid (MAA) with either acrylamide (AAm) or 3‐aminopropyltriethoxysilane (APTES), introducing synergistic hydrogen bonding and π‐π stacking. A photo‐responsive azobenzene monomer enabled reversible photo‐isomerization under 365/440 nm light, allowing precise spatiotemporal control of β‐sitosterol adsorption/release. Co‐assembly behavior was validated by 1 H‐NMR spectroscopy. The MIPs were characterized by FT‐IR, XPS, TGA, SEM, BET, DSC and UV‐DRS, while molecular docking elucidated the photo‐responsive mechanism. Adsorption performance was evaluated using isothermal, kinetic, and thermodynamic models, alongside imprinting factor analysis. When applied to solid‐phase extraction (SPE) from crude Camellia oil, the MIPs exhibited excellent selectivity, reusability, and enrichment efficiency. The maximum adsorption capacity reached 11.02 mg/g, and β‐sitosterol purity increased to 78.3% ~ 82.1%. This work offers a green, reusable, and light‐controllable platform for selective sterol recovery from edible oil systems.

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Publication Details

Journal
Polymer Engineering and Science
Published
2025-11-21
DOI
https://doi.org/10.1002/pen.70256
Primary Topic
Cholesterol and Lipid Metabolism
Type
article
Field-Weighted Citation Impact
0.00

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article

Structurally Intelligent Recognition and Enrichment of Photo‐Responsive Dual‐Monomer Imprinted Polymers for β‐Sitosterol in Camellia Oil

Bo Wang, Yunong Tian, Xuan He, Dandan Li et al.
Polymer Engineering and Science
Cholesterol and Lipid Metabolism
article

Structurally Intelligent Recognition and Enrichment of Photo‐Responsive Dual‐Monomer Imprinted Polymers for β‐Sitosterol in Camellia Oil

Bo Wang, Yunong Tian, Xuan He, Dandan Li, Yong Ye, Yuling Chen, Jun Ouyang
article en

Abstract

ABSTRACT To address the challenge of selectively extracting β‐sitosterol from Camellia oil—a complex, lipid‐rich matrix containing structurally similar sterols—a photo‐responsive molecularly imprinted polymer (MIP) was developed using a hybrid functional monomer system. Functional monomers were systematically screened via UV–Vis spectroscopy and density functional theory (DFT) to ensure molecular‐level selectivity and optimal cavity matching. The imprinting system combined methacrylic acid (MAA) with either acrylamide (AAm) or 3‐aminopropyltriethoxysilane (APTES), introducing synergistic hydrogen bonding and π‐π stacking. A photo‐responsive azobenzene monomer enabled reversible photo‐isomerization under 365/440 nm light, allowing precise spatiotemporal control of β‐sitosterol adsorption/release. Co‐assembly behavior was validated by 1 H‐NMR spectroscopy. The MIPs were characterized by FT‐IR, XPS, TGA, SEM, BET, DSC and UV‐DRS, while molecular docking elucidated the photo‐responsive mechanism. Adsorption performance was evaluated using isothermal, kinetic, and thermodynamic models, alongside imprinting factor analysis. When applied to solid‐phase extraction (SPE) from crude Camellia oil, the MIPs exhibited excellent selectivity, reusability, and enrichment efficiency. The maximum adsorption capacity reached 11.02 mg/g, and β‐sitosterol purity increased to 78.3% ~ 82.1%. This work offers a green, reusable, and light‐controllable platform for selective sterol recovery from edible oil systems.

Polymer Engineering and ScienceVol. 66(2)
Ganzhou People's Hospital (CN), Gansu Research Academy of Forestry Science and Technology (CN), Forestry Research Institute (UZ), Jiangxi Applied Engineering Vocational Institute (CN), South China University of Technology (CN)
Key Research and Development Program of Jiangxi Province
Openalex Percentile: Top 45%
Cholesterol and Lipid Metabolism
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