Related Experiment Video
Updated: Jun 6, 2026

Engineering Molecular Recognition with Bio-mimetic Polymers on Single Walled Carbon Nanotubes
Published on: January 10, 2017
Mussel-inspired molecular strategy for ultrasensitive detection of multiple chiral molecules
Yanhong Liu1, Yang Li1, Chengcheng Suo1
1State Key Laboratory of Utilization of Woody Oil Resource, Northeast Forestry University, Harbin, 150040, China; Key Laboratory of Bio-based Material Science and Technology (Ministry of Education), College of Material Science and Engineering, Northeast Forestry University, Harbin, 150040, China.
None:
Chiral recognition in racemic mixtures remains a critical challenge in life and environmental sciences, while the practical development of chiral electrochemical sensors is often restricted by complicated fabrication processes and insufficient signal transduction. Herein, a chiral electrochemical recognizer was developed by integrating molecular imprinting with mussel-inspired dopamine chemistry, using biomass-derived porous carbon as a conductive scaffold to achieve both high enantioselectivity and enhanced electrochemical response. Benefiting from the strong interfacial adhesion of dopamine, the imprinted layer was stably immobilized on the electrode surface without additional binders, generating stereospecific cavities complementary to target enantiomers. Meanwhile, the porous carbon framework, featuring a high specific surface area and abundant microporous structure, facilitated rapid electron and mass transfer and thus markedly enhances signal output. The fabricated sensor delivered reliable enantioselective recognition in both aqueous and ethanolic media. Specifically, for l-tryptophan (L-Trp), the platform exhibited a linear range of 0.1-10000 μg L-1 with a LOD of 25.32 μg L-1. For other amino acids, such as d-ascorbic acid, L-ascorbic acid, R-limonene, and S-limonene, the platform demonstrated a linear range of 0.1-1000 μg L-1 (R2 > 0.982), with LODs of 73.36 μg L-1, 40.31 μg L-1, 71.97 μg L-1, and 99.13 μg L-1, respectively. Furthermore, when applied to real food samples, including soybean flour and milk powder, the platform achieved satisfactory recoveries ranging from 83.8% to 112.0%. These results highlight the broad applicability of the platform for detecting chiral molecules in both model compounds and real-world food samples.

