Machine Learning-Enhanced Nanozyme Sensor Array for Accurate Multiple Quinolone Antibiotics Recognition

The overuse of quinolone antibiotics (QNs) seriously endangers human health and the ecological environment. In this work, a copper dihydroxosulfate (Cu₂(OH)₂SO₄) nanosheet exhibiting notable peroxidase-like (POD) and laccase-like (LAC) activities has been developed in basic deep eutectic solvents (DES). The unique physicochemical properties of QNs allow them to enhance the POD activity of Cu₂(OH)₂SO₄, and with the extension of reaction time, this enhancement gradually intensifies. Conversely, when QNs are introduced into the LAC reaction system of Cu₂(OH)₂SO₄, they significantly inhibit its LAC activity, with the degree of inhibition growing increasingly evident as the reaction time increases. A nanozyme sensing array has been developed via reaction dynamics to identify eight QNs. This method cleverly achieves self-calibration through two reverse signals, further improving the sensing performance of the sensor array. Moreover, through the optimization of various machine learning (ML), the precision of the concentration-independent recognition model built upon this array has been enhanced from 39.08% to 91.95%. This improvement is advantageous for the identification of unknown samples within actual samples. This work carries significant implications for enhancing the discrimination of QNs in complex samples.