Arsenic contamination poses a significant public health risk worldwide, with chronic exposure leading to various health issues. Detecting and monitoring arsenic exposure accurately remains challenging, necessitating the development of sensitive detection methods. In this study, we introduce a novel approach using fast-scan cyclic voltammetry (FSCV) coupled with carbon-fiber microelectrodes (CFMs) for the electrochemical detection of As3+. Through an in-depth pH study using tris buffer, we optimized the electrochemical parameters for both acidic and basic media. Our sensor demonstrated high selectivity, distinguishing the As3+ signal from those of As5+ and other potential interferents under ambient conditions. We achieved a limit of detection (LOD) of 0.5 μM (37.46 ppb) and a sensitivity of 2.292 nA/μM for bare CFMs. Microscopic data confirmed the sensor’s stability at lower, physiologically relevant concentrations. Additionally, using our previously reported double-bore CFMs, we simultaneously detected As3+-Cu2+ and As3+-Cd2+ in tris buffer, enhancing the LOD of As3+ to 0.2 μM (14.98 ppb). To our knowledge, this is the first study to use CFMs for the rapid and selective detection of As3+ via FSCV. Our sensor’s ability to distinguish As3+ from As5+ in a physiologically relevant pH environment showcases its potential for future in vivo studies.
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Gold Nanoparticle-Modified Carbon-Fiber Microelectrodes for the Electrochemical Detection of Cd2+ via Fast-Scan Cyclic Voltammetry
Neurotoxic heavy metals, such as Cd2+, pose a significant global health concern due to their increased environmental contamination and subsequent detrimental health hazards they pose to human beings. These metal ions can breach the blood-brain barrierblood–brain barrier, leading to severe and often irreversible damage to the central nervous system and other vital organs. Therefore, developing a highly sensitive, robust, and rapid in vivo detection method for these hazardous heavy metal ions is of the utmost importance for early detection, thus initiating timely therapeutics. Detecting ultra-low levels of toxic metal ions in vivo and obtaining accurate speciation information remains a challenge with conventional analytical techniques. In this study, we fabricated a novel carbon carbon-fiber microelectrode (CFM)-based sensor that can detect Cd2+ ions using fast-scan cyclic voltammetry by electrodepositing gold nanoparticles (AuNP). We optimized electrochemical parameters that generate a unique cyclic voltammogram (CV) of Cd2+ at a temporal resolution of 100 ms with our novel sensor. All our experiments were performed in tris buffer that mimics the artificial cerebellum fluid. We established a calibration curve resulting in a limit of detection (LOD) of 0.01 µM with a corresponding sensitivity of 418.02 nA/ µM. The sensor’s selectivity was evaluated in the presence of other metal ions, and it was noteworthy to observe that the sensor retained its ability to produce the distinctive Cd2+ CV, even when the concentration of other metal ions was 200 times higher than that of Cd2+. We also found that our sensor could detect free Cd2+ ions in the presence of complexing agents. Furthermore, we analyzed the solution chemistry of each of those Cd2+–ligand solutions using a geochemical model, PHREEQC. The concentrations of free Cd2+ ions determined through our electrochemical data align well with geochemical modeling data, thus validating the response of our novel sensor. Furthermore, we reassessed our sensor’s LOD in tris buffer based on the concentration of free Cd2+ ions determined through PHREEQC analysis, revealing an LOD of 0.00132 µM. We also demonstrated the capability of our sensor to detect Cd2+ ions in artificial urine samples, showcasing its potential for application in actual biological samples. To the best of our knowledge, this is the first AuNP-modified, CFM-based Cd2+ sensor capable of detecting ultra-low concentrations of free Cd2+ ions in different complex matrices, including artificial urine at a temporal resolution of 100 ms, making it an excellent analytical tool for future real-time, in vivo detection, particularly in the brain.
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- Award ID(s):
- 2301577
- PAR ID:
- 10496841
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Micromachines
- Volume:
- 15
- Issue:
- 3
- ISSN:
- 2072-666X
- Page Range / eLocation ID:
- 294
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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