Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids.

The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor's performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor's efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.

Ultrasonic-assisted preparation of novel ternary ZnO/AgI/Fe3O4 nanocomposites as magnetically separable visible-light-driven photocatalysts with excellent activity.

The present work demonstrates preparation of novel ternary ZnO/AgI/Fe3O4 nanocomposites, as magnetically separable visible-light-driven photocatalysts using ultrasonic irradiation method. The XRD, EDX, SEM, TEM, UV-vis DRS, FT-IR, PL, and VSM techniques was applied for characterization of structure, purity, morphology, optical, and magnetic properties of the resultant samples. The superior activity was seen for the nanocomposite with 8 weight ratio of ZnO/AgI to Fe3O4 in degradation of rhodamine B under visible-light irradiation. Photocatalytic activity of this nanocomposite in degradation of rhodamine B, methylene blue, and methyl orange is about 32, 6, and 5-fold higher than that of the ZnO/Fe3O4 nanocomposite. The highly enhanced activity of the ternary magnetic photocatalyst was mainly attributed to more visible-light absorption ability and efficiently separation of the charge carriers. Furthermore, it was revealed that the ultrasonic irradiation time and calcination temperature affect largely on the photocatalytic activity. Finally, the magnetic photocatalyst was successfully separated from the treated solution using external magnetic field.