INVESTIGADORES
RUBIANES Maria Dolores
capítulos de libros
Título:
Applications of Carbon Nanotubes Based Electrochemical Sensing Strategies for Heavy Metals and Arsenic Quantification
Autor/es:
MARCELA C. RODRÍGUEZ, M. DOLORES RUBIANES, FABIANA A. GUTIERREZ, MARCOS EGUÍLAZ, PABLO R. DALMASSO, M. LAURA RAMÍREZ, CECILIA S. TETTAMANTI, ANTONELLA MONTEMERLO, PABLO GALLAY AND GUSTAVO A. RIVAS
Libro:
Pure and Functionalized Carbon Based Nanomaterials: Analytical, Biomedical, Civil and Environmental Engineering Applications
Editorial:
CRC Press Taylor & Francis Group
Referencias:
Año: 2020;
Resumen:
The outbreak of industrialization and urbanization in recent decades is a subject of great global concern due to its contribution to the increase of environmental pollution. Heavy metals and arsenic derived from industrial and agricultural activities lead the ranking of harmful environmental chemicals. In this sense, the biggest challenge is the production of powerful analytical tools with high sensitivity and reliability, rapid response, selectivity, precision, lower manufacturing costs and reagents, production of waste, consumption of samples as well as miniaturization processes that allow the construction of point-of-care devices and in-situ analysis. In recent years, the application of nanomaterials for the design of analytical platforms has become a new strategy to improve construction technologies and the associated performance of chemical sensors and biosensors. Among nanomaterials, carbon nanotubes (CNT) have been used for the development of electrochemical sensors due to their interesting physical, chemical and electrical properties. This review focuses on the critical discussion of the most representative electrochemical sensors based on CNT for the quantification of different markers of environmental damage (arsenic, lead, cadmium, mercury, chromium and other priority detrimental heavy metals) reported in the 2014-2018 period. We focus our attention on the role of CNT during the recognition event and signal transduction as well as on the overall analytical performance of the resulting platforms.