Bovine Serum Albumin Template-Mediated Fabrication of Ruthenium Dioxide/Multiwalled Carbon Nanotubes: High-Performance Electrochemical Dopamine Biosensing in Human Serum.
Peng LeiShan ZhaoMuhammad AsifAyesha AzizYing ZhouChuan DongMinglu LiShao Min ShuangPublished in: Langmuir : the ACS journal of surfaces and colloids (2024)
The presence of abnormal dopamine (DA) levels may cause serious neurological disorders, therefore, the quantitative analysis of DA and its related research are of great significance for ensuring health. Herein, the bovine serum albumin (BSA) template method has been proposed for the preparation of catalytically high-performance ruthenium dioxide/multiwalled carbon nanotube (RuO 2 /MWCNT) nanocomposites. The incorporation of MWCNTs has improved the active surface area and conductivity while effectively preventing the aggregation of RuO 2 nanoparticles. The outstanding electrocatalytic performance of RuO 2 /MWCNTs has promoted the electro-oxidation of DA at neutral pH. The electrochemical sensing platform based on RuO 2 /MWCNTs has demonstrated a wide linear range (0.5 to 111.1 μM), low detection limit (0.167 μM), excellent selectivity, long-term stability, and good reproducibility for DA detection. The satisfactory recovery range of 94.7% to 103% exhibited by the proposed sensing podium in serum samples signifies its potential for analytical applications. The aforementioned results reveal that RuO 2 /MWCNT nanostructures hold promising aptitude in the electrochemical sensor to detect DA in real samples, further offering broad prospects in clinical and medical diagnosis.
Keyphrases
- carbon nanotubes
- label free
- molecularly imprinted
- gold nanoparticles
- healthcare
- walled carbon nanotubes
- ionic liquid
- solid phase extraction
- uric acid
- public health
- loop mediated isothermal amplification
- real time pcr
- mental health
- reduced graphene oxide
- high resolution
- electron transfer
- gene expression
- metabolic syndrome
- single cell
- hydrogen peroxide
- risk assessment
- tissue engineering
- neural network
- simultaneous determination
- prefrontal cortex