Chérif Dridi

Precise and real-time detection of dopamine (DA) is essential for biomedical diagnostics and for understanding its role in neurological and metabolic disorders. This study presents a high-performance photonic crystal fiber-based localized surface plasmon resonance (PCF-LSPR) biosensor for refractive index-based dopamine sensing, integrating experimentally measured refractive index data with detailed numerical simulations. The proposed sensor employs a D-shaped photonic crystal fiber geometry to enhance the interaction between the guided core mode and localized surface plasmons supported by gold (Au), silver (Ag), and bimetallic Au-Ag nanoparticles. The refractive index of dopamine solutions was experimentally measured over the concentration range of 25-150 mM using an Abbe refractometer and directly incorporated into the numerical model to ensure realistic and application-oriented performance evaluation. Finite element method (FEM) simulations were carried out to analyze the coupling between the fundamental core-guided mode and the surface plasmon polariton mode through confinement loss spectra. The results show that all three plasmonic configurations achieve a high wavelength sensitivity of 5000 nm/RIU with excellent linearity (R² = 1) and a resolution of 2 × 10^-6 RIU. While the wavelength sensitivity and resolution are identical, variations in full width at half maximum (FWHM), figure of merit (FOM), and amplitude sensitivity reveal the influence of plasmonic material selection. Silver nanoparticles exhibit the narrowest resonance peaks and highest FOM, whereas the bimetallic Au-Ag configuration provides an optimal balance between sensitivity, resonance stability, and chemical robustness. All configurations demonstrate a low limit of detection of 4 × 10^-10 RIU²/nm. These findings highlight the strong potential of the proposed PCF-LSPR biosensor for real-time dopamine monitoring in biomedical and neurochemical applications.

Wearable sweat pH sensors enable electrochemical and optical detection for non-invasive, real-time health monitoring and point-of-care diagnostics.

This study aims to develop a sustainable electrochemical sensor integrated with machine learning (ML) for the simultaneous prediction of serotonin (5-HT) and dopamine (DA) concentrations, two key biomarkers of neurological disorders. The novelty of this work lies in the combination of green-synthesized nanomaterials and advanced ML algorithms within a miniaturized, eco-friendly, and cost-effective sensing platform. This integration improves diagnostic accuracy, reduces analysis time, and minimizes reliance on manual interpretation, offering significant potential for clinical applications. Monitoring neurotransmitters is crucial for the early diagnosis of neurological diseases, yet conventional methods are often costly, time-consuming, and dependent on specialized expertise. Electrochemical sensors based on nanomaterials provide a promising alternative, particularly when enhanced by ML to enable accurate, real-time prediction of analyte concentrations. In this work, a screen-printed electrode (SPE) functionalized with green-synthesized silver nanoparticles decorated reduced graphene oxide (AgNPs-rGO) was employed for the simultaneous neurotransmitter detection. Experimental electrochemical signals were systematically collected and used as input features for ML models, with 80% of the dataset applied for training and 20% for validation. Among the tested algorithms, Random Forest Regression (RFR) achieved the highest predictive accuracy, with R ² values of 0.984 for 5-HT and 0.897 for DA. By integrating eco-friendly nanomaterials with ML-based predictive modeling on a miniaturized platform, this study provides a rapid, efficient, and sustainable approach with strong potential for improving early diagnosis of neurological disorders.

This work presents the simulation and optimization of a novel surface acoustic wave (SAW) hydrogen sensor based on a graphene oxide (GO) layer decorated with palladium nanoparticles (Pd NPs) on a lithium niobate (LiNbO ₃ ) substrate. A finite element method (FEM) was used to model the sensor, and a response surface methodology (RSM) was applied to optimize key parameters, including substrate thickness, interdigital transducer spacing, and operating frequency. The design space was statistically explored using the least squares method (LSM). The optimized configuration yielded an insertion loss of −1.06 dB at 80 MHz with a 74.74 µm substrate thickness and 6.90 mm transducer spacing. The sensor demonstrated excellent performance, with time delay and amplitude sensitivities of 26 ns and 0.72 dB per atomic fraction (af) of hydrogen in Pd, respectively. These results highlight the potential of the proposed SAW device for reliable hydrogen leak detection in safety-critical environments.

The green synthesis of zinc oxide nanoparticles (ZnO NPs) using Retama raetam leaf extract via microwave irradiation was investigated. The biosynthesized NPs were characterized using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and UV-Vis spectrophotometry. An XRD pattern confirmed the formation of a hexagonal wurtzite structure. An FTIR analysis indicated the interactions of the NPs with bioactive molecules involved in their synthesis. SEM and STEM imaging determined the morphology of the NPs with an average size of 14 nm. Furthermore, the biosynthesized ZnO NPs were used as a sensitive layer for detecting volatile organic compounds (VOCs) at low concentrations ranging from 0.5 to 5 ppm. The response sensor measured at an optimum operating temperature of 250 °C and 50% relative humidity (RH). The sensor exhibited a strong response to 5 ppm ethanol (325%), a detection limit as low as 4 ppb and an excellent stability across varying humidity levels.

Sustainable nanoplatform for the sensitive and simultaneous detection of neurotransmitters for early-stage diagnosis of neurodegenerative diseases.

A novel electrochemical sensor, based on a glassy carbon electrode modified with a newly synthesized manganese( iii ) porphyrin complex, was developed for serotonin detection.

Recent developments in surface plasmon resonance (SPR) sensors based on photonic crystal fiber (PCF) technology have greatly enhanced sensitivity. Nevertheless, existing configurations often face challenges related to design complexity and real-time application optimization. In this study, we present a straightforward and highly sensitive circular PCF-SPR biosensor tailored for the detection of early-stage cancer cells, with efficient performance across both visible and near-infrared spectrums. Using the Taguchi L₂₇(3⁵) orthogonal array approach, we optimized five critical structural parameters: air hole diameters, pitch, and the thickness of the gold (Au) and titanium dioxide (TiO₂) layers. The resulting sensor achieved remarkable spectral sensitivity of 18,000 nm/RIU and an amplitude sensitivity of 681.655 RIU^-1. Designed to function across a wide refractive index range (1.29-1.40), the biosensor is particularly effective for identifying cervical, blood, and skin cancer cells, reaching peak sensitivity of 7,500 nm/RIU for cervical cancer detection. Additionally, performance prediction utilized machine learning techniques, specifically Multiple Linear Regression (MLR) and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) models. The MLP-ANN model demonstrated superior predictive accuracy, highlighting the potential of artificial intelligence in optimizing biosensor configurations for advanced cancer diagnostics.