Early diagnosis of Parkinson's disease by highly sensitive monitoring of glucose using an atomic-level defect-enriched MoS2/Au SERS sensor enabled by LSPR-PICT synergy
In Parkinson's disease (PD), approximately 60% of the nigrostriatal neurons in the substantia nigra have degenerated before neurologists can establish a diagnosis based on widely accepted clinical diagnostic criteria. Therefore, identifying patients at risk and in the earlier stages of the disease is crucial for neuroprotection. This study proposes a surface-enhanced Raman scattering (SERS) sensor based on –SH and Au nanoparticles coupled with atomic-level defect-enriched molybdenum disulfide (MoS2) sulfur vacancy bonding for the specific detection of glucose. Utilizing the SERS method, the sensor enables glucose monitoring for PD patients, addressing challenges such as the short effective response distance in SERS detection and the electronic transition difficulties from MoS2 to the glucose receptor 4-mercaptophenylboronic acid. The sensor achieves a detection limit of 10−9 M for glucose. Its performance was validated in saliva and urine samples, showing detection errors between 1.91% and 3.77% compared to commercial test kits and glucometers, with highly consistent results. This study lays the foundation for the development of portable, low-cost, and non-invasive glucose monitoring devices, providing research perspectives for the monitoring of other diseases.
Authors
- Kaixi Shi (ORCID: https://orcid.org/0000-0001-9143-096X)
- Wenhui Fang (ORCID: https://orcid.org/0000-0001-6075-2982)
- Yu Zhang (ORCID: https://orcid.org/0009-0000-8770-5166)
- Jinhua Li (ORCID: https://orcid.org/0000-0003-3505-6808)
- Fujun Liu (ORCID: https://orcid.org/0009-0008-7930-1071)
- Yingjiao Zhai
- Zhiying Wang (ORCID: https://orcid.org/0000-0003-0823-4066)
Institutions
- Jilin University (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1063/5.0276932
- Citations
- 2
- Primary Topic
- Electrochemical sensors and biosensors
- Type
- article
- Field-Weighted Citation Impact
- 1.46
Funders
- Jilin Province Key R&D Plan Project