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.

Ni/WS2/WC Composite Nanosheets as an Efficient Catalyst for Photoelectrochemical Hydrogen Peroxide Sensing and Hydrogen Evolution

It is highly attractive to develop a photoelectrochemical (PEC) sensing platform based on a non-noble-metal nano array architecture. In this paper, a PEC hydrogen peroxide (H2O2) biosensor based on Ni/WS2/WC heterostructures was synthesized by a facile hydrothermal synthesis method and melamine carbonization process. The morphology, structural and composition and light absorption properties of the Ni/WS2/WC catalyst were investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and UV-visible spectrophotometer. The average size of the Ni/WS2/WC nanosheets was about 200 nm. Additionally, the electrochemical properties toward H2O2 were studied using an electrochemical workstation. Benefiting from the Ni and C atoms, the optimized Ni/WS2/WC catalyst showed superior H2O2 sensing performance and a large photocurrent response. It was found that the detection sensitivity of the Ni/WS2/WC catalyst was 25.7 μA/cm2/mM, and the detection limit was 0.3 mmol/L in the linear range of 1-10 mM. Simultaneously, the synthesized Ni/WS2/WC electrode displayed excellent electrocatalytic properties in hydrogen evolution reaction (HER), with a relatively small overpotential of 126 mV at 10 mA/cm2 in 0.5 M H2SO4. This novel Ni/WS2/WC electrode may provide new insights into preparing other efficient hybrid photoelectrodes for PEC applications.

Fabrication of Carbon Nanofiber Incorporated with CuWO4 for Sensitive Electrochemical Detection of 4-Nitrotoluene in Water Samples

In the current work, copper tungsten oxide (CuWO₄) nanoparticles are incorporated with carbon nanofiber (CNF) to form CNF/CuWO₄ nanocomposite through a facile hydrothermal method. The prepared CNF/CuWO₄ composite was applied to the electrochemical detection of hazardous organic pollutants of 4-nitrotoluene (4-NT). The well-defined CNF/CuWO₄ nanocomposite is used as a modifier of glassy carbon electrode (GCE) to form CuWO₄/CNF/GCE electrode for the detection of 4-NT. The physicochemical properties of CNF, CuWO₄, and CNF/CuWO₄ nanocomposite were examined by various characterization techniques, such as X-ray diffraction studies, field emission scanning electron microscopy, EDX-energy dispersive X-ray microanalysis, and high-resolution transmission electron microscopy. The electrochemical detection of 4-NT was evaluated using cyclic voltammetry (CV) the differential pulse voltammetry detection technique (DPV). The aforementioned CNF, CuWO₄, and CNF/CuWO₄ materials have better crystallinity with porous nature. The prepared CNF/CuWO₄ nanocomposite has better electrocatalytic ability compared to other materials such as CNF, and CuWO₄. The CuWO₄/CNF/GCE electrode exhibited remarkable sensitivity of 7.258 μA μM^-1 cm^-2, a low limit of detection of 86.16 nM, and a long linear range of 0.2-100 μM. The CuWO₄/CNF/GCE electrode exhibited distinguished selectivity, acceptable stability of about 90%, and well reproducibility. Meanwhile, the GCE/CNF/CuWO₄ electrode has been applied to real sample analysis with better recovery results of 91.51 to 97.10%.

Photochemical synthesis of bimetallic CuNiSx quantum dots onto g-C3N4 as a cocatalyst for high hydrogen evolution

CuNiSx QDs were fabricated onto g-C3N4 by photochemical deposition method. The small size can expose more active S sites on the edge and the introduction of Cu2+ into NiSx can slightly modulate the electronic structure of Ni and S centers, thus weakening the S–Hads bonds.