Cargando…

Carbon Quantum Dots’ Synthesis with a Strong Chemical Claw for Five Transition Metal Sensing in the Irving–Williams Series

Carbon quantum dots (CQDs) are an excellent eco-friendly fluorescence material, ideal for various ecological testing systems. Herein, we establish uniform microwave synthesis of the group of carbon quantum dots with specific functionalization of ethylenediamine, diethylenetriamine, and three types o...

Descripción completa

Detalles Bibliográficos
Autores principales: Yakusheva, Anastasia, Sayapina, Anastasia, Luchnikov, Lev, Arkhipov, Dmitry, Karunakaran, Gopalu, Kuznetsov, Denis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8912369/
https://www.ncbi.nlm.nih.gov/pubmed/35269294
http://dx.doi.org/10.3390/nano12050806
Descripción
Sumario:Carbon quantum dots (CQDs) are an excellent eco-friendly fluorescence material, ideal for various ecological testing systems. Herein, we establish uniform microwave synthesis of the group of carbon quantum dots with specific functionalization of ethylenediamine, diethylenetriamine, and three types of Trilon (A, B and C) with chelate claws -C-NH(3). CQDs’ properties were studied and applied in order to sense metal cations in an aquatic environment. The results provide the determination of the fluorescence quench in dots by pollutant salts, which dissociate into double-charged ions. In particular, the chemical interactions with CQDs’ surface in the Irving–Williams series (IWs) via functionalization of the negatively charged surface were ascribed. CQD-En and CQD-Dien demonstrated linear fluorescence quenching in high metal cation concentrations. Further, the formation of claws from Trilon A, Trilon B, and C effectively caught the copper and nickel cations from the solution due to the complexation on CQDs’ surface. Moreover, CQD-Trilon C presented chelating properties of the surface and detected five cations (Cu(2+), Ni(2+), Ca(2+), Mg(2+), Zn(2+)) from 0.5 mg/mL to 1 × 10(−7) mg/mL in the Irving–William’s series. Dependence was mathematically attributed as an equation (ML regression model) based on the constant of complex formation. The reliability of the data was 0.993 for the training database.