Back to Search Start Over

Ultrafast Detection of Arsenic Using Carbon-Fiber Microelectrodes and Fast-Scan Cyclic Voltammetry.

Authors :
Manring, Noel
Strini, Miriam
Koifman, Gene
Xavier, Jonathan
Smeltz, Jessica L.
Pathirathna, Pavithra
Source :
Micromachines; Jun2024, Vol. 15 Issue 6, p733, 12p
Publication Year :
2024

Abstract

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 As<superscript>3+</superscript>. 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 As<superscript>3+</superscript> signal from those of As<superscript>5+</superscript> 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 As<superscript>3+</superscript>-Cu<superscript>2+</superscript> and As<superscript>3+</superscript>-Cd<superscript>2+</superscript> in tris buffer, enhancing the LOD of As<superscript>3+</superscript> 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 As<superscript>3+</superscript> via FSCV. Our sensor's ability to distinguish As<superscript>3+</superscript> from As<superscript>5+</superscript> in a physiologically relevant pH environment showcases its potential for future in vivo studies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2072666X
Volume :
15
Issue :
6
Database :
Complementary Index
Journal :
Micromachines
Publication Type :
Academic Journal
Accession number :
178193152
Full Text :
https://doi.org/10.3390/mi15060733