journal article·2025-08-26

A Wearable Copper Ion Detection Sensor Enabled by Graphene Quantum Dot-Modified Vertical Aligned Carbon Nanotube Array for Potential Alzheimer’s Disease Screening

Yongsheng Yang, Qinqi Ren, Dexing Liu, Yang Zhu, Shengdong Zhang, Min Zhang

ACS Applied Electronic Materials· Volume 7· Issue 16
Publisher page

Abstract

With increasing severity of population aging, the incidence of Alzheimer’s disease (AD) is on the rise, significantly impacting the quality of life and safety of individuals. AD is characterized by multiple interrelated pathological factors, among which the accumulation of copper ions (Cu 2+ ) not only accelerates the aggregation of amyloid-β peptides (Aβ), but also generates reactive oxygen species through the Fenton Reaction, leading to oxidative damage in neurons. Currently, monitoring copper ion concentrations suffers from complex processes and insufficient detection limits. In this work, we propose a novel wearable Cu 2+ detection sensor (WCDS) based on graphene quantum dots (GQDs) integrated with a vertical carbon nanotube array (VACNT). By modification of the three-dimensional VACNT array with large specific surface area GQDs, high-precision detection of Cu 2+ has been achieved. The WCDS demonstrates an effective linear sensing range from 100 fM to 10 nM, with a detection limit as low as 16.9 fM. The simplicity of the preparation process, along with low detection limit and great stability, positions this WCDS as a promising solution for large-scale and high-precision copper ion detection in the population. This capability provides new possibilities for early screening and monitoring the progression of Alzheimer’s disease.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Electrochemical sensors and biosensors · Electrochemical Analysis and Applications · Conducting polymers and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Science and Engineering

The Chinese University of Hong Kong

School of Electronic and Computer Engineering

Peking University

Funding

National Key Research and Development Program of China · National Natural Science Foundation of China · CUHK-Shenzhen university development fund

References (45)

Show source-provided references
1. 10.1016/j.jalz.2011.03.00510.1016/j.jalz.2011.03.005 ↗
2. 10.1002/trc2.1220010.1002/trc2.12200 ↗
3. 10.1007/s10654-017-0326-z10.1007/s10654-017-0326-z ↗
4. 10.1021/cr300099410.1021/cr3000994 ↗
5. 10.3389/fncel.2018.0047110.3389/fncel.2018.00471 ↗
6. 10.1016/j.snb.2024.13676710.1016/j.snb.2024.136767 ↗
7. 10.1016/j.talanta.2020.12174510.1016/j.talanta.2020.121745 ↗
8. 10.1016/j.bioorg.2022.10630110.1016/j.bioorg.2022.106301 ↗
9. 10.1016/S0891-5849(96)00629-610.1016/S0891-5849(96)00629-6 ↗
10. 10.1016/j.bios.2021.11389510.1016/j.bios.2021.113895 ↗
11. 10.1016/j.bios.2023.11598810.1016/j.bios.2023.115988 ↗
12. 10.1002/jnr.1035010.1002/jnr.10350 ↗
13. 10.1002/adfm.20230201510.1002/adfm.202302015 ↗
14. 10.1002/adfm.20230267310.1002/adfm.202302673 ↗
15. 10.1021/acs.biochem.9b0077510.1021/acs.biochem.9b00775 ↗
16. 10.1021/acsomega.2c0693910.1021/acsomega.2c06939 ↗
17. 10.1016/j.jinorgbio.2021.11159110.1016/j.jinorgbio.2021.111591 ↗
18. 10.1016/j.ccr.2022.21497810.1016/j.ccr.2022.214978 ↗
19. 10.1016/j.saa.2021.12058710.1016/j.saa.2021.120587 ↗
20. 10.1016/j.bios.2021.11385810.1016/j.bios.2021.113858 ↗
21. 10.1002/adom.20240045810.1002/adom.202400458 ↗
22. 10.1021/acsanm.0c0142610.1021/acsanm.0c01426 ↗
23. 10.1016/j.molstruc.2023.13525910.1016/j.molstruc.2023.135259 ↗
24. 10.1016/j.chemosphere.2020.12780110.1016/j.chemosphere.2020.127801 ↗
25. 10.1002/adma.20230411910.1002/adma.202304119 ↗
26. 10.1016/j.bios.2021.11378510.1016/j.bios.2021.113785 ↗
27. 10.1016/j.carbon.2022.06.02710.1016/j.carbon.2022.06.027 ↗
28. 10.1016/j.colsurfa.2021.12659310.1016/j.colsurfa.2021.126593 ↗
29. 10.1021/acsanm.1c0281210.1021/acsanm.1c02812 ↗
30. 10.1016/j.apsusc.2022.15537910.1016/j.apsusc.2022.155379 ↗
31. 10.1016/j.diamond.2020.10774910.1016/j.diamond.2020.107749 ↗
32. 10.1039/D1NJ04551B10.1039/D1NJ04551B ↗
33. 10.1021/acsaelm.3c0152310.1021/acsaelm.3c01523 ↗
34. 10.1016/j.jhazmat.2024.13684510.1016/j.jhazmat.2024.136845 ↗
35. 10.1016/j.foodchem.2024.13973410.1016/j.foodchem.2024.139734 ↗
36. 10.1021/acsami.9b2078510.1021/acsami.9b20785 ↗
37. 10.1016/j.snb.2017.11.09810.1016/j.snb.2017.11.098 ↗
38. 10.1016/j.snb.2023.13377410.1016/j.snb.2023.133774 ↗
39. 10.1007/s00267-019-01234-y10.1007/s00267-019-01234-y ↗
40. 10.1016/j.jphotochem.2020.11297710.1016/j.jphotochem.2020.112977 ↗
41. 10.1016/j.carbon.2022.04.06810.1016/j.carbon.2022.04.068 ↗
42. 10.1016/j.colsurfb.2020.11083810.1016/j.colsurfb.2020.110838 ↗
43. 10.1016/j.ces.2018.02.01610.1016/j.ces.2018.02.016 ↗
44. 10.1016/j.apcata.2020.11771210.1016/j.apcata.2020.117712 ↗
45. 10.1021/ja406820f10.1021/ja406820f ↗