Boron and Nitrogen Co-Doped Porous Graphene Nanostructures for the Electrochemical Detection of Poisonous Heavy Metal Ions
Abstract
Heavy metal poisoning has a life-threatening impact on the human body to aquatic ecosystems. This necessitates designing a convenient green methodology for the fabrication of an electrochemical sensor that can detect heavy metal ions efficiently. In this study, boron (B) and nitrogen (N) co-doped laser-induced porous graphene (LIGBN) nanostructured electrodes were fabricated using a direct laser writing technique. The fabricated electrodes were utilised for the individual and simultaneous electrochemical detection of lead (Pb2+) and cadmium (Cd2+) ions using a square wave voltammetry technique (SWV). The synergistic effect of B and N co-doping results in an improved sensing performance of the electrode with better sensitivity of 0.725 µA/µM for Pb2+ and 0.661 µA/µM for Cd2+ ions, respectively. Moreover, the sensing electrode shows a low limit of detection of 0.21 µM and 0.25 µM for Pb2+ and Cd2+ ions, with wide linear ranges from 8.0 to 80 µM for Pb2+ and Cd2+ ions and high linearity of R2 = 0.99 in case of simultaneous detection. This rapid and facile method of fabricating heteroatom-doped porous graphene opens a new avenue in electrochemical sensing studies to detect various hazardous metal ions.
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.