Microfluidic biosensors have played an important and challenging role for the rapid detection of the new severe acute respiratory syndrome coronavirus-2 (S

In order to ensure the optimal functionality of biosensor devices across a diverse range of applications, it is crucial to accurately predict their detection times. This study delves into an in-depth exploration of the centrifugal and Coriolis effects that emerge due to the angular alignment and radial displacement of a rotating microfluidic biosensor specifically designed for detecting complex reactive proteins (CRPs). To address this challenge, we introduce an innovative hybrid model known as PSO-ANN, which combines the power of an artificial neural network (ANN) with the particle swarm optimization (PSO) algorithm. This pioneering model is aimed at predicting the response time of a Lab-on-a CD device by utilizing critical input variables such as rotational velocity (ω), biosensor position (X _S ), angular alignment (θ), and radial displacement (R _D ). Our research also involves a comprehensive performance evaluation of the PSO-ANN model, comparing it to an alternative multilayer perceptron model, ANN. This evaluation seeks to assess the impact of these models on improving the accuracy and reliability of predictions related to biosensor detection times, with potential applications spanning a wide spectrum of practical fields. Key metrics utilized in our evaluation include mean absolute error (MAE), root mean square error (RMSE), variance accounted for (VAF), and coefficient of determination (R ² ). The results highlight the remarkable predictive capabilities of the hybrid PSO-ANN model. This research carries significant implications for enhancing the performance of biosensor devices and advancing their utility in various domains, promising advancements in the field of biosensor technology.

This study presents the development of an innovative electrochemical sensor for the simultaneous detection of cadmium (Cd²⁺) and lead (Pb²⁺) ions in environmental samples. The sensor is developed based on a composite material of zeolite imidazolate framework ZIF-7 and polyaniline (PANI), referred to as ZIF-7@PANI, where ZIF-7 is rapidly synthesized at room temperature and polyaniline used to enhance the conductivity of the composite. Characterization via X-ray diffraction (DRX), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) confirmed successful synthesis. The composite was applied to a glassy carbon electrode (GCE) using drop-casting for heavy metal ion detection. Experimental parameters—including pH, incubation time, deposited quantity, and drying time—were optimized using the Box–Behnken design. Under optimal conditions, the ZIF-7@PANI/GCE sensor demonstrated a broad dynamic concentration range (0.002–1 µM for Pb²⁺ and 0.02–30 µM for Cd²⁺), with low detection limits (2.96 nM for Pb²⁺ and 10.6 nM for Cd²⁺). It also exhibited strong anti-interference properties and high recovery rates (85–110%), highlighting its potential for real practical applications.