Citation: Jinlong Ren, Zhuang Li, Guangcun Shan, Wei Huang, Kunpeng Dou. AI-powered dissecting of carbohydrate isomers by a flexoelectric gated nanopore tweezer[J]. Chinese Chemical Letters, ;2026, 37(4): 111624. doi: 10.1016/j.cclet.2025.111624 shu

AI-powered dissecting of carbohydrate isomers by a flexoelectric gated nanopore tweezer

Jinlong Ren ^a ,
Zhuang Li ^a ,
Guangcun Shan ^{b, e, f, *,} ,
Wei Huang ^{b, d} ,
Kunpeng Dou ^{a, c, *,}

a.
College of Physics and Optoelectronic Engineering, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
b.
School of Flexible Electronics, Sun Yat-sen University, Shenzhen 518107, China
c.
Engineering Research Center of Advanced Marine Physical Instruments and Equipment of Education Ministry of China & Key Laboratory of Optics and Optoelectronics of Qingdao, Ocean University of China, Qingdao 266100, China
d.
Shaanxi Institute of Flexible Electronics, Northwestern Polytechnical University, Xi’an 710072, China
e.
Institute of Precision Instrument and Quantum Sensing, Beihang University, Beijing 100191, China
f.
Boheng Technology (Hangzhou) Co., Ltd., Hangzhou 310016, China

gshan2-c@my.cityu.edu.hk

doukunpeng@ouc.edu.cn

Received Date: 12 March 2025
Revised Date: 22 July 2025
Accepted Date: 22 July 2025
Available Online: 23 July 2025

Figures(3)

Carbohydrates play essential roles in the physiological and pathological functions of cells. However, carbohydrate structures involve numerous levels of isomerism, which has posed significant challenges to advancements in glycomics. The technique for carbohydrate recognition needs to be precise in determining all aspects of the stereodiversity for both fundamental research and practical applications. Via quantum tunneling simulations and model analysis, we show that a carbon nanotube based nanopore as a molecular tweezer to trap a single target analyte with controlled dwell time achieved through reversible flexoelectric gating. Under mechanical deformation, the pore walls act as dynamic electrostatic binding sites to capture analyte enabling ample but fast sampling. After establishing Fano resonance as the sensing mechanism to quantitatively evaluate the interaction between the pore wall and analyte, random forest classifier algorithm is employed to classify the quantum transport data. This sensing strategy provides a general discrimination accuracy of higher than 99.4% for identifying carbohydrate isomers. Our findings highlight the efficacy of this combined physics and machine learning-based method in addressing the stereochemical complexity of carbohydrates. The approach not only improves observation time per molecule but also operates in a high-throughput format, offering a powerful artificial intelligence (AI)-empowered biomolecule sensing tool for glycomics research.
- Carbohydrate isomers,
- Nanopore tweezer,
- Flexoelectric gating,
- Fano resonance,
- Artificial intelligence,
- Single-molecule bioelectronic sensing
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