A green synthetic route for the surface-passivation of carbon dots as an effective multifunctional fluorescent sensor for the recognition and detection of toxic metal ions from aqueous solution

Abstract

In this work, a green synthetic route was used to create a number of surface passivated fluorescent carbon quantum dots, which are explored as promising sensing probes, via facile one-pot hydrothermal methods. The functionalization of carbon dots (CDs) with various functional groups offers a diverse opportunity for the sensitive detection of multiple analytes. The improved synthesis procedure and surface passivation of CDs is a move one step forward to meet the tough task of multiple ion sensing. Herein we have used Coccinia indica as the carbonaceous source for the fluorescent CDs and the surface functionalization was improved with different organic precursors (L-cysteine (N, S), ethylene diamine (N) and glycine (N, O)) in an approach to synthesize a unique sensor with different binding abilities with different metal ions. We have compared the sensing ability of bare Coccinia indica CDs (self-passivated Iy-CDs) with those of surface tuned N,S/Iy-CDs, N/Iy-CDs and N,O/Iy-CDs (passivated CDs). The surface tuned CDs show high fluorescence quantum yields and their sensing probes are more adept in sensing various distinct heavy metal ions in aqueous systems. Self-passivated Iy-CDs shows selectivity for mercury ions (Hg²⁺) with a detection limit of 3.3 nM, while passivated N,S/Iy-CDs, N/Iy-CDs and N,O/Iy-CDs show selectivity for copper ions (Cu²⁺), lead ions (Pb²⁺) and ferric ions (Fe³⁺) with lower limits of detection of 0.045, 0.27 and 6.2 μM, respectively. The FT-IR and fluorescence spectra strongly support the distinct functionalization of the surface states of the CDs, allowing them to exhibit high selectivity for the detection of Hg²⁺, Cu²⁺, Pb²⁺ and Fe³⁺ based on inner filter effects and non-radiative electron transfer processes. We further explore the passivation of green synthesized CDs to conduct a practical assessment of real sample analysis. These results indicate that the sensors are superior for heavy metal recognition and they offer great potential for application in environmental monitoring sensors.