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
1. [1]
  G. Nagy, N.L.B. Pohl, J. Am. Soc. Mass Spectrom. 26 (2015) 677–685. doi: 10.1007/s13361-014-1072-z
2. [2]
  I.A. Anastasiou, I. Eleftheriadou, A. Tentolouris, et al., Curr. Med. Chem. 28 (2021) 6110–6122. doi: 10.2174/0929867328666210311112240
3. [3]
  A. Sener, F. Malaisse-Lagae, P. Lebrun, et al., Res. Commun. 108 (1982) 1567–1573.
4. [4]
  Y. Lu, G.V. Levin, T.W. Donner, Diabetes Obes. Metab. 10 (2008) 109–134. doi: 10.1111/j.1463-1326.2007.00799.x
5. [5]
  S. Roy, J. Chikkerur, S.C. Roy, et al., J. Food Sci. 83 (2018) 2699–2709. doi: 10.1111/1750-3841.14358
6. [6]
  N. Nomura, G. Verdon, H.J. Kang, et al., Nature 526 (2015) 397–401. doi: 10.1038/nature14909
7. [7]
  K. Zhu, W.K. Wang, X.Q. Ye, S.G. Chen, TrAC Trends Anal. Chem. 176 (2024) 117721. doi: 10.1016/j.trac.2024.117721
8. [8]
  J. Hofmann, H.S. Hahm, P.H. Seeberger, K. Pagel, Nature 526 (2015) 241–244. doi: 10.1038/nature15388
9. [9]
  B. Schindler, L. Barnes, G. Renois, et al., Nat. Commun. 8 (2017) 973. doi: 10.1038/s41467-017-01179-y
10. [10]
  Y. Wu, J.J. Gooding, Chem. Soc. Rev. 51 (2022) 3862–3885. doi: 10.1039/d1cs00988e
11. [11]
  A.S. Shekhawat, B. Sahu, A. Diwan, et al., ACS Sens. 9 (2024) 5025–5051. doi: 10.1021/acssensors.4c02173
12. [12]
  Y.H. Wang, Y. Zhao, A. Bollas, Y.R. Wang, K.F. Au, Nat. Biotechnol. 39 (2021) 1348–1365. doi: 10.1038/s41587-021-01108-x
13. [13]
  Y.N. Zhao, B. Ashcroft, P.M. Zhang, et al., Nat. Nanotechnol. 9 (2014) 466–473. doi: 10.1038/nnano.2014.54
14. [14]
  P. Gehring, J.M. Thijssen, H.S.J. van der Zant, Nat. Rev. Phys. 1 (2019) 381–396. doi: 10.1038/s42254-019-0055-1
15. [15]
  A. Dorey, S. Howorka, Nat. Chem. 16 (2024) 314–334. doi: 10.1038/s41557-023-01322-x
16. [16]
  J. Ritmejeris, X. Chen, C. Dekker, Nat. Rev. Bioeng. 3 (2025) 303–316.
17. [17]
  W.J. Ramsay, H. Bayley, Angew. Chem. Int. Ed. 57 (2018) 2841–2845. doi: 10.1002/anie.201712740
18. [18]
  S.Y. Zhang, Z.Y. Cao, P.P. Fan, et al., Angew. Chem. Int. Ed. 63 (2024) e202316766. doi: 10.1002/anie.202316766
19. [19]
  J. Im, S. Biswas, H. Liu, et al., Nat. Commun. 7 (2016) 13868. doi: 10.1038/ncomms13868
20. [20]
  Y.P. Zhang, L.C. Chen, Z.Q. Zhang, et al., J. Am. Chem. Soc. 140 (2018) 6531–6535. doi: 10.1021/jacs.8b02825
21. [21]
  Y. Kim, A. Garcia-Lekue, D. Sysoiev, et al., Phys. Rev. Lett. 109 (2012) 226801. doi: 10.1103/PhysRevLett.109.226801
22. [22]
  J.P. Perdew, K. Burke, M. Ernzerhof, Phys. Rev. Lett. 77 (1996) 3865–3868. doi: 10.1103/PhysRevLett.77.3865
23. [23]
  S. Smidstrup, T. Markussen, P. Vancraeyveld, et al., J. Phys. Condens. Matter. 32 (2020) 015901. doi: 10.1088/1361-648x/ab4007
24. [24]
  M.J. van Setten, M. Giantomassi, E. Giantomassi, et al., Comput. Phys. Commun. 226 (2018) 39–54. doi: 10.1016/j.cpc.2018.01.012
25. [25]
  K. Lee, É.D. Murray, L. Kong, B.I. Lundqvist, D.C. Langreth, Phys. Rev. B. 82 (2010) 081101. doi: 10.1103/PhysRevB.82.081101
26. [26]
  S. Grimme, J. Comput. Chem. 27 (2006) 1787–1799. doi: 10.1002/jcc.20495
27. [27]
  Z.C. Zhou, J. Song, Y.H. Xie, et al., Chin. Chem. Lett. 36 (2025) 110906. doi: 10.1016/j.cclet.2025.110906
28. [28]
  M. Brandbyge, J.L. Mozos, P. Ordejón, J. Taylor, K. Stokbro, Phys. Rev. B. 65 (2002) 165401. doi: 10.1103/PhysRevB.65.165401
29. [29]
  S.L. Dudarev, G.A. Botton, S.Y. Savrasov, C.J. Humphreys, A.P. Sutton, Phys. Rev. B 57 (1998) 1505–1509. doi: 10.1103/PhysRevB.57.1505
30. [30]
  R. Meena, G. Li, M. Casula, J. Chem. Phys. 15 (2022) 084112.
31. [31]
  J. Ren, Y. Liu, X. Shi, et al., Research 2021 (2021) 9821905.
32. [32]
  L.F. Wang, S.H. Liu, X.L. Feng, et al., Nat. Nanotechnol. 15 (2020) 661–667. doi: 10.1038/s41565-020-0700-y
33. [33]
  M.H. Liu, X.Y. Zhou, X. Li, Z.J. Xue, T. Wang, Chin. Chem. Lett. 35 (2024) 108875. doi: 10.1016/j.cclet.2023.108875
34. [34]
  F. Banhart, Small 21 (2025) 2310462. doi: 10.1002/smll.202310462
35. [35]
  W. Dong, X. Li, S. Lu, et al., Small 20 (2024) 202308430.
36. [36]
  Y. Ding, Y. Liu, A. Klyushin, et al., Angew. Chem. Int. Ed. 63 (2024) e202318043. doi: 10.1002/anie.202318043
37. [37]
  J. Hou, R. Qi, Y. Liang, Y. Cheng, Q. Huang, Carbon 208 (2023) 338–344. doi: 10.52601/bpr.2023.230032
38. [38]
  H. Nakajima, K. Kobashi, Y. Zhou, M. Zhang, T. Okazaki, Carbon 216 (2024) 118495. doi: 10.1016/j.carbon.2023.118495
39. [39]
  Y. Lin, M.R. Funk, C.J. Campbell, et al., Adv. Funct. Mater. 27 (2017) 1700762. doi: 10.1002/adfm.201700762
40. [40]
  L. Merkel, A. Setaro, C.E. Halbig, et al., Carbon 229 (2024) 119454. doi: 10.1016/j.carbon.2024.119454
41. [41]
  D.Y. Kim, D. Jeong, P. Thangavel, et al., Chem 9 (2023) 3304–3318. doi: 10.1016/j.chempr.2023.07.007
42. [42]
  A.J. Samuels, J.D. Carey, ACS Nano 7 (2013) 2790–2799. doi: 10.1021/nn400340q
43. [43]
  C.H. Yeh, Y.C. Lin, Y.C. Chen, et al., ACS Nano 8 (2014) 6962–6969. doi: 10.1021/nn501775h
44. [44]
  S.C. Ho, C.H. Chang, Y.C. Hsieh, et al., Nat. Electron. 4 (2021) 116–125. doi: 10.1038/s41928-021-00537-5
45. [45]
  M.K. Jena, B. Pathak, Nano Lett. 23 (2023) 2511–2521. doi: 10.1021/acs.nanolett.2c04062
46. [46]
  S. Mittal, M.K. Jena, B. Pathak, ACS Cent. Sci. 10 (2024) 1689–1702. doi: 10.1021/acscentsci.4c00630
47. [47]
  K.K. Saha, M. Drndic, B.K. Nikolic, Nano Lett. 12 (2012) 50–55. doi: 10.1021/nl202870y
48. [48]
  C.J. Lambert, Chem. Soc. Rev. 44 (2015) 875–888. doi: 10.1039/C4CS00203B
49. [49]
  S.M. Lundberg, G. Erion, H. Chen, et al., Nat. Mach. Intell. 2 (2020) 56–67. doi: 10.1038/s42256-019-0138-9
50. [50]
  F. Pedregosa, G. Varoquaux, A. Gramfort, et al., J. Mach. Learn. Res. 12 (2011) 2825–2830.
1. [1]
  Yunxin Li , Jinghui Zhang , Jisen Chen , Feng Zhu , Zhiqiang Liu , Peng Bao , Wei Shen , Sheng Tang . Detection of SARS-CoV-2 based on artificial intelligence-assisted smartphone: A review. Chinese Chemical Letters, 2024, 35(7): 109220-. doi: 10.1016/j.cclet.2023.109220
2. [2]
  Bakr Ahmed Taha , Ali J. Addie , Luai Farhan Zghair Kolie , Saba Talib Wahhab , Sinan Adnan Abdulateef , Adawiya J. Haider , Khalid Ibnaouf , Norhana Arsad . Advancements in nanophotonics and smart nanomaterials integrated with artificial intelligence-driven gene editing: A paradigm shift in cancer diagnosis and therapeutic. Chinese Chemical Letters, 2026, 37(4): 111955-. doi: 10.1016/j.cclet.2025.111955
3. [3]
  Na Qin , Wenxin Guo , Fangxiu Li , Houfeng Zhang , Hong Liu , Chang Zhang , Lipiao Bao , Lei Liu , Muneerah Alomar , Siqi Zhao , Jian Zhang , Xing Lu . Recent advances in machine learning-driven discovery of alloy electrocatalysts for hydrogen evolution reaction. Chinese Chemical Letters, 2026, 37(3): 112021-. doi: 10.1016/j.cclet.2025.112021
4. [4]
  Siwei Wang , Wei-Lei Zhou , Yong Chen . Cucurbituril and cyclodextrin co-confinement-based multilevel assembly for single-molecule phosphorescence resonance energy transfer behavior. Chinese Chemical Letters, 2024, 35(12): 110261-. doi: 10.1016/j.cclet.2024.110261
5. [5]
  Jiliang Deng , Guoliang Shi , Zhihang Ye , Quan Xiao , Xiaoting Zhang , Lei Ren , Fangyu Yang , Miao Wang . Unveiling and swift diagnosing chronic wound healing with artificial intelligence assistance. Chinese Chemical Letters, 2025, 36(3): 110496-. doi: 10.1016/j.cclet.2024.110496
6. [6]
  Hui Li , Zhenjie Zhao , Bingqiang Ji , Jun Ma , Xuwu Zhang , Jingzhao Chen , Zhangran Ye , Zuankai Wang , Liqiang Zhang , Jianyu Huang , Yingdan Liu . Electric tweezer for single microscopic particle trapping. Chinese Chemical Letters, 2026, 37(3): 110655-. doi: 10.1016/j.cclet.2024.110655
7. [7]
  Qiyao Zhang , Yuting Li , Qishun Jin , Zhengwei Liu , Hongsong Chen , Jingqi Huang , Taoyi Liu , Xiaojuan Liu , Zhenghuai Tan , Shuheng Huang , Wu Dong , Zhipei Sang . Artificial intelligence-driven development of natural multi-target derivatives with BuChE inhibitory activity for treating Alzheimer’s disease. Chinese Chemical Letters, 2026, 37(1): 110964-. doi: 10.1016/j.cclet.2025.110964
8. [8]
  Haoran Zhang , Yaxin Jin , Peng Kang , Sheng Zhang . The Convergence and Innovative Application of Artificial Intelligence in Scientific Research: A Case Study of Electrocatalytic Carbon Dioxide Reduction in the Context of the Dual-Carbon Strategy. University Chemistry, 2025, 40(9): 148-155. doi: 10.12461/PKU.DXHX202412099
9. [9]
  Jun-Jie Fang , Yun-Peng Xie , Xing Lu . Organooxotin and cobalt/manganese heterometallic nanoclusters exhibiting single-molecule magnetism. Chinese Journal of Structural Chemistry, 2025, 44(4): 100515-100515. doi: 10.1016/j.cjsc.2025.100515
10. [10]
  Jinjiang Wu , Zhenhua Zhu , Jinkui Tang . Recent advancements of photo-responsive lanthanide single-molecule magnets. Chinese Chemical Letters, 2025, 36(12): 110577-. doi: 10.1016/j.cclet.2024.110577
11. [11]
  Ping Li , Chao Yin . Teaching Exploration and Practical Innovation of General Education Courses in the Context of Artificial Intelligence. University Chemistry, 2024, 39(10): 402-407. doi: 10.12461/PKU.DXHX202403075
12. [12]
  Ying Zhang , Fang Ge , Zhimin Luo . AI-Driven Biochemical Teaching Research: Predicting the Functional Effects of Gene Mutations. University Chemistry, 2025, 40(3): 277-284. doi: 10.12461/PKU.DXHX202412104
13. [13]
  Meirong Cui , Mo Xie , Jie Chao . Design and Reflections on the Integration of Artificial Intelligence in Physical Chemistry Laboratory Courses. University Chemistry, 2025, 40(5): 291-300. doi: 10.12461/PKU.DXHX202412015
14. [14]
  Yu Fang . AI-Empowered Education: A Case Study of Self-Directed Learning with ChatGPT-4. University Chemistry, 2025, 40(9): 1-4. doi: 10.12461/PKU.DXHX202502013
15. [15]
  Cheng-an Tao , Jian Huang , Yujiao Li . Exploring the Application of Artificial Intelligence in University Chemistry Laboratory Instruction. University Chemistry, 2025, 40(9): 5-10. doi: 10.12461/PKU.DXHX202408132
16. [16]
  Lei Qin , Kai Guo . Application of Generative Artificial Intelligence in the Simulation of Acid-Base Titration Images. University Chemistry, 2025, 40(9): 11-18. doi: 10.12461/PKU.DXHX202408123
17. [17]
  Weigang Zhu , Jianfeng Wang , Qiang Qi , Jing Li , Zhicheng Zhang , Xi Yu . Curriculum Development for Cheminformatics and AI-Driven Chemistry Theory toward an Intelligent Era. University Chemistry, 2025, 40(9): 34-42. doi: 10.12461/PKU.DXHX202412002
18. [18]
  Xiao Ma , Junjie Wang , Xin Chen , Jingcheng Li , Lihong Zhao , Xueping Sun , Shaojuan Cheng , Fang Wang . Exploring Innovative Approaches to Chemistry Instructional Organization Driven by Artificial Intelligence. University Chemistry, 2025, 40(9): 99-106. doi: 10.12461/PKU.DXHX202410085
19. [19]
  Run Yang , Huajie Pang , Huiping Zang , Ruizhong Zhang , Zhicheng Zhang , Xiyan Li , Libing Zhang . Artificial Intelligence-Enabled DNA Computing: Exploring New Frontiers in Bioinformatics. University Chemistry, 2025, 40(9): 107-117. doi: 10.12461/PKU.DXHX202412135
20. [20]
  Yan Zhang , Limin Zhou , Xiaoyan Cao , Mutai Bao . Exploring the Application of Artificial Intelligence in Marine-Themed Integrated Physical Chemistry Experiments. University Chemistry, 2025, 40(9): 118-125. doi: 10.12461/PKU.DXHX202503062

Get Citation

PDF

XML

Metrics

PDF Downloads(0)
Abstract views(104)
HTML views(12)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142