Unique photoluminescence response of MoS2 quantum dots over wide range of As (III) in aqueous media

TL;DR

This study presents a solvothermal synthesis of MoS2 quantum dots that exhibit a unique, concentration-dependent photoluminescence response for detecting arsenic (III) in water across a wide range, with potential for sensitive environmental monitoring.

Contribution

The paper introduces a novel photoluminescence-based detection method for arsenic (III) using MoS2 quantum dots with a distinctive enhancement and suppression pattern over a broad concentration range.

Findings

Photoluminescence of QDs is enhanced up to 50 ppb and suppressed thereafter up to 1000 ppb.

The optical absorption pattern correlates with arsenic concentration, showing initial increase then decrease.

Detection calibration curves fit well with a modified Stern-Volmer equation.

Abstract

Solvothermal synthesis of MoS2 based quantum dots (QDs) and the performance evaluation of bare QDs for the detection of aqueous As (III) oxidative state at room temperature and neutral pH over an extremely wide range (0.1 ppb to 1000 ppb) is reported here. Concentration-dependent photoluminescence (PL) of the QDs was found to be enhanced up to 50 ppb and then suppressed till 1000 ppb, showing two distinctive slopes for enhancement and suppression. Passivation of trap states or defects of QDs may be the possible reason for enhancement, and the formation of extremely small glassy As2S3 particles on the QD surface may be the possible reason for suppression. The pattern of optical absorption of QDs is featureless but shows an enhanced absorbance in the near UV range below 300 nm, which increases with As (III) concentration up to 50 ppb and then decreases following the PL pattern. The MoS2 QDs were characterized by transmission electron microscopy (TEM), Xray diffraction (XRD), UV-Vis, and PL spectroscopy. The enhancement and suppression results can be fitted excellently with the modified Stern-Volmer equation, and the detection of arsenic is possible using these linear fit equations as calibration curves.