REFERENCES
1. Mentink, J.; Rasing, T.; Kösters, D.; Rijk, I.; Hilgenkamp, H.; Smink, S.; Noheda, B.; Gaydadjiev, G.; Hamdioui, S.; Dolas, S. Neuromorphic Computing in the Netherlands: White Paper. 2024. https://www.ru.nl/sites/default/files/2024-11/whitepaper-neuromorphic-computing-final_pdf.pdf. (accessed 2026-2-10).
2. Eisenberg, B. Ionic channels in biological membranes: natural nanotubes. Acc. Chem. Res. 1998, 31, 117-23.
4. Levitan, I. B. Signaling protein complexes associated with neuronal ion channels. Nat. Neurosci. 2006, 9, 305-10.
5. Drummond, H. A.; Gebremedhin, D.; Harder, D. R. Degenerin/epithelial Na+ channel proteins: components of a vascular mechanosensor. Hypertension 2004, 44, 643-8.
6. Bernèche, S.; Roux, B. Energetics of ion conduction through the K+ channel. Nature 2001, 414, 73-7.
7. Berkefeld, H.; Fakler, B.; Schulte, U. Ca2+-activated K+ channels: from protein complexes to function. Physiol. Rev. 2010, 90, 1437-59.
8. Ro, Y. G.; Na, S.; Kim, J.; et al. Iontronics: neuromorphic sensing and energy harvesting. ACS. Nano. 2025, 19, 24425-507.
10. Liu, Y.; Zhao, C.; Xiong, Y.; et al. Versatile ion-gel fibrous membrane for energy-harvesting iontronic skin. Adv. Funct. Mater. 2023, 33, 2303723.
12. Zhou, N.; Cui, T.; Lei, Z.; Wu, P. Bioinspired learning and memory in ionogels through fast response and slow relaxation dynamics of ions. Nat. Commun. 2025, 16, 4573.
13. Lin, S.; Jiang, J.; Huang, K.; et al. Advanced electrode technologies for noninvasive brain-computer interfaces. ACS. Nano. 2023, 17, 24487-513.
14. Yegnanarayana, B. Artificial neural networks; PHI Learning Pvt. Ltd., 2009.
15. Yamazaki, K.; Vo-Ho, V. K.; Bulsara, D.; Le, N. Spiking neural networks and their applications: a review. Brain. Sci. 2022, 12, 863.
16. Duan, X.; Cao, Z.; Gao, K.; et al. Memristor-based neuromorphic chips. Adv. Mater. 2024, 36, e2310704.
17. Rivnay, J.; Inal, S.; Salleo, A.; Owens, R. M.; Berggren, M.; Malliaras, G. G. Organic electrochemical transistors. Nat. Rev. Mater. 2018, 3, 17086.
18. Li, Y.; Bai, N.; Chang, Y.; et al. Flexible iontronic sensing. Chem. Soc. Rev. 2025, 54, 4651-700.
19. Han, S. H.; Oh, M.; Chung, T. D. Iontronics: aqueous ion-based engineering for bioinspired functionalities and applications. Chem. Phys. Rev. 2022, 3, 031302.
20. Xu, G.; Zhang, M.; Mei, T.; Liu, W.; Wang, L.; Xiao, K. Nanofluidic ionic memristors. ACS. Nano. 2024, 18, 19423-42.
21. Xia, Y.; Zhang, C.; Xu, Z.; et al. Organic iontronic memristors for artificial synapses and bionic neuromorphic computing. Nanoscale 2024, 16, 1471-89.
22. Li, L.; Chen, W.; Kong, X.; Wen, L. Nanofluidic neuromorphic iontronics: a nexus for biological signal transduction. Iontronics 2026, 2, 4.
23. Zhu, X.; Wu, Z.; Zhao, Z. Bio-inspired heterointerfacial ion-gating and iontronic neuromorphics. Iontronics 2025, 1, 4.
24. Han, S. H.; Kim, S. I.; Oh, M. A.; Chung, T. D. Iontronic analog of synaptic plasticity: Hydrogel-based ionic diode with chemical precipitation and dissolution. Proc. Natl. Acad. Sci. U. S. A. 2023, 120, e2211442120.
26. Hummer, G.; Rasaiah, J. C.; Noworyta, J. P. Water conduction through the hydrophobic channel of a carbon nanotube. Nature 2001, 414, 188-90.
27. Berezhkovskii, A.; Hummer, G. Single-file transport of water molecules through a carbon nanotube. Phys. Rev. Lett. 2002, 89, 064503.
28. Majumder, M.; Chopra, N.; Hinds, B. J. Mass transport through carbon nanotube membranes in three different regimes: ionic diffusion and gas and liquid flow. ACS. Nano. 2011, 5, 3867-77.
29. Samoylova, O. N.; Calixte, E. I.; Shuford, K. L. Selective ion transport in functionalized carbon nanotubes. Appl. Surf. Sci. 2017, 423, 154-9.
30. Lee, C. Y.; Choi, W.; Han, J. H.; Strano, M. S. Coherence resonance in a single-walled carbon nanotube ion channel. Science 2010, 329, 1320-4.
31. Min, H.; Kim, Y. T.; Moon, S. M.; Han, J. H.; Yum, K.; Lee, C. Y. High-yield fabrication, activation, and characterization of carbon nanotube ion channels by repeated voltage-ramping of membrane-capillary assembly. Adv. Funct. Mater. 2019, 29, 1900421.
32. Choi, W.; Ulissi, Z. W.; Shimizu, S. F.; Bellisario, D. O.; Ellison, M. D.; Strano, M. S. Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes. Nat. Commun. 2013, 4, 2397.
33. Li, C.; Xiong, T.; Yu, P.; Fei, J.; Mao, L. Synaptic iontronic devices for brain-mimicking functions: fundamentals and applications. ACS. Appl. Bio. Mater. 2021, 4, 71-84.
34. Han, S. H.; Kim, S. I.; Lee, H. R.; et al. Hydrogel-based iontronics on a polydimethylsiloxane microchip. ACS. Appl. Mater. Interfaces. 2021, 13, 6606-14.
35. Maram, R.; Howe, J. V.; Kong, D.; et al. Frequency-domain ultrafast passive logic: NOT and XNOR gates. Nat. Commun. 2020, 11, 5839.
37. Yang, J. J.; Strukov, D. B.; Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 2013, 8, 13-24.
38. van de Burgt, Y.; Lubberman, E.; Fuller, E. J.; et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 2017, 16, 414-8.
39. Dai, S.; Liu, X.; Liu, Y.; et al. Emerging iontronic neural devices for neuromorphic sensory computing. Adv. Mater. 2023, 35, e2300329.
40. Li, Y.; Li, Z.; Aydin, F.; et al. Water-ion permselectivity of narrow-diameter carbon nanotubes. Sci. Adv. 2020, 6.
42. Zhou, K.; Xu, Z. Ion permeability and selectivity in composite nanochannels: engineering through the end effects. J. Phys. Chem. C. 2020, 124, 4890-8.
43. Zhang, P.; Ma, X.; Dong, Y.; et al. An energy efficient reservoir computing system based on HZO memcapacitive devices. Appl. Phys. Lett. 2023, 123, 122104.
44. Alpaydin, E.; Kaynak, C. Optical recognition of handwritten digits. Repository, U. M. L., Ed.; University of California, School of Information and Computer Sciences: Irvine, CA, 1998. https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits. (accessed 2026-2-10).
45. Caporale, N.; Dan, Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 2008, 31, 25-46.
46. Squire, L. R.; Berg, D.; Bloom, F. E.; du Lac, S.; Ghosh, A.; Spitzer, N. C. Fundamental Neuroscience. 4th ed. Elsevier, 2012.
47. Paschek, D.; Ludwig, R. Specific ion effects on water structure and dynamics beyond the first hydration shell. Angew. Chem. Int. Ed. Engl. 2011, 50, 352-3.
48. Richards, L. A.; Schäfer, A. I.; Richards, B. S.; Corry, B. The importance of dehydration in determining ion transport in narrow pores. Small 2012, 8, 1701-9.
49. Robinson, R. A.; Stokes, R. H. Electrolyte solutions. Courier Corporation, 2002.
50. Tunuguntla, R.; Allen, F.; Kim, K.; Belliveau, A.; Noy, A. Ultra-fast proton transport in sub-1-nm diameter carbon nanotube porins. Biophys. J. 2016, 110, 338a.
51. Bukola, S.; Creager, S. E. A charge-transfer resistance model and Arrhenius activation analysis for hydrogen ion transmission across single-layer graphene. Electrochim. Acta. 2019, 296, 1-7.
52. Holden, A. V. Models of the stochastic activity of neurones, Vol. 12. Springer Science & Business Media, 2013.
53. Consul, P. C.; Jain, G. C. A generalization of the Poisson distribution. Technometrics 1973, 15, 791-9.
54. Gerstner, W.; Kistler, W. M.; Naud, R.; Paninski, L. Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge University Press, 2014; pp 417-20.
55. Samoylova, O. N.; Calixte, E. I.; Shuford, K. L. Molecular dynamics simulations of ion transport in carbon nanotube channels. J. Phys. Chem. C. 2015, 119, 1659-66.
56. Cui, G.; Xu, Z.; Zhang, S.; Siria, A.; Ma, M. Coupling between ion transport and electronic properties in individual carbon nanotubes. Sci. Adv. 2025, 11, eadu7410.
57. Xiao, K.; Jiang, L.; Antonietti, M. Ion transport in nanofluidic devices for energy harvesting. Joule 2019, 3, 2364-80.
58. Holt, J. K.; Park, H. G.; Wang, Y.; et al. Fast mass transport through sub-2-nanometer carbon nanotubes. Science 2006, 312, 1034-7.
59. Liang, X.; Tang, J.; Zhong, Y.; Gao, B.; Qian, H.; Wu, H. Physical reservoir computing with emerging electronics. Nat. Electron. 2024, 7, 193-206.


