Despite its significant impact on performance and reliability, heat dissipation in solar cells, particularly silicon solar cells, has received relatively little attention. This paper presents a comprehensive two-dimensional (2D) simulation of heat transfer in silicon solar cells. Using COMSOL Multiphysics, we investigate the temperature distribution in conventional silicon solar cells by integrating optical, electrical and thermal modules. Subsequently, we focus on enhancing the performance of solar cells by investigating the effect of three key electrical and thermal parameters on their efficiency. These parameters include donor concentration (ND), acceptor concentration (NA) and temperature (T0). Using the Taguchi method and ANOVA analysis we designed an L9(33) orthogonal array to minimize experimental variance and identify the optimal control parameters. An artificial neural networks (ANN) based model has been developed.

This study presents the intelligent optimization and performance prediction of a D-shaped photonic crystal fiber (PCF) biosensor enhanced with localized surface plasmon resonance (LSPR) for ultra-sensitive analyte detection. The sensor incorporates a plasmonic gold nanoparticle (AuNP) coating to improve sensitivity by leveraging the interaction between the core-guided mode and the surface plasmon mode (SPM) at the metal–dielectric interface. Using the finite element method (FEM) for modal analysis, critical parameters, including the D-shaped channel radius (Rd), gold nanoparticle radius (Rg), and the angle between nanoparticles (δ), were optimized through response surface methodology (RSM) with a Box–Behnken design (BBD). To further refine the sensor’s predictive capabilities, a multilayer perceptron (MLP) artificial neural network was trained to model its performance, allowing precise estimation of sensitivity metrics within the specific refractive index (RI) range of 1.31–1.37. The optimized biosensor achieved a remarkable spectral sensitivity of 30,000 nm/RIU, an amplitude sensitivity of 200.292 RIU−1, and a resolution of 3.33 × 10–6 RIU. The integration of artificial intelligence in the design and optimization process highlights a paradigm shift in biosensor engineering, offering a powerful approach for real-time performance prediction and enhancing detection precision in biomedical diagnostics and environmental monitoring applications.

A combined computational workflow featuring response surface methodology, a hybrid teaching-learning-based optimization (TLBO)-ANN model, and Support Vector Regression (SVR) was used to design and optimize novel Schiff bases 1,2-bis(furan-2-ylmethylene)hydrazine (A1), 1,2-bis(furan-2-ylethylene)hydrazine (A2), 1,2-bis(thiophen-2-ylmethylene)hydrazine (A3), and 1,2-bis(thiophen-2-ylethylene)hydrazine (A4). The TLBO-ANN model achieved high predictive accuracy (R2 = 0.98) for synthesis yield. However, the ANN model produced the best yield prediction accuracy, as confirmed by experiments (yield: 91

The Coriolis force plays a crucial role in governing fluid motion in centrifugal microfluidics, particularly in Labon-a-CD platforms, where it significantly influences lateral flow essential for fluid mixing and biosensing. While previous studies have predominantly focused on radially aligned microchannels, the impact of alternative channel configurations remains underexplored. This study numerically investigates the effects of angular alignment (AA) and radial displacement (RD) on Coriolis-induced deviations in velocity profiles within rotating microchannels. A multi-parameter optimization was conducted to minimize the biosensor detection time (TR), considering angular velocity (omega), angular alignment (theta), radial displacement (RD), and the reaction surface position (XS). A Box-Behnken Design (BBD) based on Response Surface Methodology (RSM) was implemented, alongside a predictive model combining Teaching-Learning-Based Optimization and Artificial Neural Network (TLBO-ANN). The optimal detection time was found to be 1.23 min, corresponding to a rotational velocity of 87 rad/s, a reaction surface position of 150 & micro;m, an angular alignment of 34.55 degrees, and a radial displacement of 10 mm. This result was accurately predicted by BBD (0.818 min) with a 95 % confidence level, validating the model's reliability. TLBO-ANN outperformed BBD, achieving an R2 of 0.9991 and RMSE of 0.0518, compared to BBD's R2 of 0.9327 and RMSE of 0.4441. These findings underscore the critical role of channel geometry in modulating Coriolis effects and highlight the effectiveness of AI-based models in optimizing biosensor performance. This work lays a foundation for the refined design of centrifugal microfluidic systems, particularly for CRP-based diagnostic applications.

The structural organization of redox-active ligands plays a crucial role in organometallic chemistry because it influences the reactivity and stability of transition metal complexes. This research deals with the synthesis and characterization of a novel series of nickel(II) complexes, including an additional newly synthesized complex, with iminopyridine-based ligands (L1-L5). The ligands were synthesized by a condensation reaction between pyridine-2-carboxaldehyde and primary aromatic amines, and subsequently, the nickel(II) complexes were derived by the reaction of the ligands with NiCl2 & sdot;6H2O. The complexes were characterized by various techniques, including infrared spectroscopy, UV-visible spectroscopy, X-ray diffraction, and electrochemical studies. In addition, DFT calculations and ADMET predictions were used to investigate their molecular structure, reactivity, and potential drug-like properties. Box-Behnken design (BBD) was employed to optimize the synthesis conditions, with priority given to reaction time, temperature, and ligand equivalents. The results show that the complexes, including the new complex, exhibit significant redox activity, stable chelation with nickel(II), and favorable electronic properties, demonstrating their potential for further applications in catalysis and drug design.

ABSTRACT Machine learning regression models were employed to predict the electrical characteristics of GAA‐MOSFETs using a dataset of 459 simulation points. The models considered input features including gate voltage, drain voltage, channel length, silicon thickness, and metal work functions, with the logarithm of the drain current as the target. Various algorithms—including MLPRegressor, GradientBoostingRegressor, XGBRegressor, and ensemble tree‐based models—were trained with cross‐validation and evaluated on an independent test set. The MLPRegressor achieved the highest predictive performance ( R 2 = 0.9990, RMSE = 0.0946, MAE = 0.0612), closely reproducing simulation results for ON‐ and OFF‐state currents and threshold voltage. Feature importance analysis identified gate voltage and metal work function as the most influential parameters. The model accurately captured the effects of channel length and silicon thickness on device behavior, demonstrating its potential as a fast and reliable surrogate for computationally intensive numerical simulations, enabling rapid design and optimization of nanoscale transistors. In excess of DC behavior, the study is extended to small‐signal RF analysis and the linearity of the cylindrical GAA MOSFET: the transconductance ( g m ), output conductance ( g d ), transconductance generation factor (TGF), unity‐gain cutoff frequency ( f T ), and two‐port Y/S parameters. The simulated device exhibits a maximum transconductance ( g m ) of 107 μS, an intrinsic gain g m / g d of 54.5, and a cutoff frequency f T of 139 GHz. The same MLP model reproduces these RF performance metrics with a coefficient of determination R 2 > 0.97, enabling rapid design and optimization of nanoscale RF transistors.

A copper oxide-based polyaniline nanocomposite (CuO@PANI) was used to modify a glassy carbon electrode (GCE) in order to create an electrochemical sensor, synthesized via in situ oxidative polymerization. The methods of Fourier transform infrared spectroscopy (FT-IR), X-ray Diffraction (XRD), thermogravimetric analysis (TGA), Brunauer-Emmett-Teller (BET) area of measurement analysis and scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM-EDX) were used to thoroughly characterize the composite, including elemental mapping. These characterizations confirmed enhanced structural integrity, increased surface area and improved electrical conductivity arising from the presence of CuO nanoparticles and polyaniline. Electrochemical impedance spectroscopy (EIS), differential pulse voltammetry (DPV) and cyclic voltammetry (CV) were used to assess the improved electrode’s electrochemical performance, demonstrating significantly enhanced electron transfer kinetics and excellent electrochemical responsiveness. Box-Behnken methodology was used for the simultaneous optimization of the main experimental variables, therefore the optimal conditions were pH: 7.18, casting solution volume: 13 µL, drying time: 3.25 h, and accumulation time: 4.49 min. DPV measurements, after optimization, showed excellent sensitivity (17.823 A/M) and a broad linear range (0.001-10 µM) with a low limit of detection of about 1 nM. Interference and recoveries (98

This study focuses on the synthesis of novel nickel (II) complexes with chelated (3-keto-enamine ligands and evaluates their catalytic performance in imine preparation. The main objectives are to optimise the synthesis of (N boolean AND O) NiCl2 complexes, characterise them by UV-vis and FT-IR spectroscopy and investigate their electrochemical properties. A Box-Behnken design was used to refine the synthesis process. Spectroscopic analyses elucidated the complexation behaviour, while cyclic voltammetry revealed reversible monoelectronic processes. The catalytic activity of the complexes was rigorously tested, with statistical analysis tools, including ANOVA and PSO-based artificial neural networks, aiding data interpretation and yield prediction. Optimal conditions were identified, revealing promising catalytic properties. The integration of predictive modelling has consequently improved yields and provided valuable insights into the catalytic applications of these nickel (II) complexes.

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.

This study focuses on the optimization and performance assessment of a dual-core photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor for advanced biosensing applications. The Plackett-Burman design (PBD) was used to optimize key structural parameters, including air-hole diameters ( d(1) , d(2) , d(3) , d(c) ) and gold layer thickness ( t(Gold) ). Finite element method (FEM) simulations were used to analyze the sensor's optical properties, while PBD helped identify the most influential parameters affecting confinement loss ( C-Loss ). Regression analysis was used to model the relationship between the sensor's geometric parameters and confinement loss, revealing that d(3) and tGold have the most statistically significant effects. The optimized PCF-SPR sensor demonstrated outstanding performance, achieving a maximum wavelength sensitivity of 8000 nm/RIU, a resolution of 1.25x10(-5) RIU, and an amplitude sensitivity of 573.436 RIU-1, underscoring its high potential for refractive index (RI)-based detection. To enhance predictive modeling, we applied an extensive range of machine learning regression models to estimate confinement loss. A comparative evaluation of these models identified the extra tree regressor (ETR) as the most effective in accurately predicting sensor performance. These findings highlight the synergy between FEM simulations, design of experiments (DOEs), and machine learning for optimizing PCF-SPR sensors, paving the way for highly sensitive, real-time biosensing applications.

The synthesis of novel dicationic nickel (II) complexes, designated [Ni(HMPA) 4 ][Y] 2 , was achieved by reaction of the organometallic compound [Ni(cod) 2 ] with the allylic salt (C 4 H 7 (OP(NMe 2 ) 3 /NaY), (Y = BF 4 , PF 6 , BArF 24 ). Crystal structure analysis, NMR, and infrared studies provided conclusive evidence for the identification of a homoleptic nickel (II) Oxo-phosphoryl complex. The synthetic procedure involves the oxidative addition of methallyloxyphosphonium salts with non-coordinating anions, resulting in a tetrakis(hexamethylphosphotriamide) nickel (II) complex. Using an orthogonal Taguchi design, key factors such as reaction time, temperature, and stirring rate were considered. The optimized conditions were determined.

Lead (Pb2+) contamination poses serious risks to human health and environmental safety, highlighting the need for sensitive and selective detection methods. In this study, we developed a novel nanocomposite, Fe2O3 nano-particles functionalized with 4-(3,5-dimethyl-1H-pyrazol-1-yl)carboxylate (DCTA) and decorated with silver nanoparticles (Fe2O3@DCTA-Ag), using simple and efficient synthesis techniques. This material was employed to fabricate an electrochemical Pb2+ sensor based on differential pulse voltammetry (DPV). The sensor performance was optimized using a response surface methodology (RSM) combined with a Box-Behnken design (BBD), evaluating the effects of pH, contact time, drop volume, and drying time through a 34 factorial design. A multivariate regression model correlated the peak current with these factors, identifying the optimal conditions. Under these conditions, the sensor exhibited a linear detection range of 0.2 nM to 10 mu M, with a detection limit of 0.2 nM. It showed excellent selectivity against co-existing ions and consistent performance in various food samples (rice, corn, milk, honey, tea) and environmental water samples, demonstrating its practical applicability.

In this paper, microporous Zn‐based zeolitic imidazolate framework with the sodalite cage structure (SOD‐ZIF‐8) was synthesized by the solvothermal method. Powder X‐ray diffraction (PXRD), scanning electron microscopy (SEM) and N2 adsorption were employed to characterize the synthesized material. An ultra‐sensitive electrochemical sensor based on highly dispersed bimetallic Ni‐Pt nanoparticles immobilized on zeolitic metal–organic framework ZIF‐8 for dopamine quantification is introduced for the first time. The as‐prepared Ni‐Pt@ZIF‐8 composite was deposited onto a glassy carbon electrode (GCE), serving as a sensor that exhibits superior properties for the detection of dopamine (DA). To enhance the electroanalytical performance of the developed dopamine sensor, different experimental parameters (pH, drying time (h), deposit drop volume (µL) and accumulation time (min)) were optimized using a Box‐Behnken experimental design. Benefiting from the synergy of ZIF‐8 and Ni‐Pt bimetallic nanoparticles, the Ni‐Pt@ZIF‐8 composite exhibited high sensitivity towards dopamine, achieving a low detection limit of 1.0 nM. The sensor's linear response to dopamine (1 nM to 10 µM), resistance to interference, and high recovery in human serum, coupled with its simple fabrication, make it a promising tool for real‐world dopamine detection.

Microfluidic biosensors offer a promising solution for real-time analysis of coronaviruses with minimal sample volumes. This study optimizes a biochip for the rapid detection of SARS-CoV-2 using the Taguchi orthogonal table L9(34), which comprises nine groups of experiments varying four key parameters: Reynolds number (Re), Damköhler number (Da), Schmidt number (Sc), and the dimensionless position of the reaction surface (X). Signal-to-noise (S/N) ratios and analysis of variance (ANOVA) are employed to determine optimal parameters and assess their impact on binding kinetics and response time of the detection device. These obtained optimal parameters correspond to Re = 4.10-2, Da = 1000, Sc = 105, and X = 1. Additionally, results highlight Da as the most influential factor, accounting for 91%, while X has a minimal effect of 0.3%. Furthermore, an artificial neural network optimization technique, specifically particle swarm optimization (PSO), was utilized to predict biosensor performance. Derived from the Full L81(34) design experiment, the PSO model demonstrates its effectiveness compared to the conventional multi-layer perception (MLP) model, thus underlining its potential in this innovative optimization context.

This study aims to optimize the synthesis process of a nickel (II) dicationic precatalyst supported by alpha-iminopyridine ligands (ImPy) to improve yield and efficiency. A Taguchi optimization approach was used to systematically evaluate and optimize key factors-reaction time, temperature, and stirring speed-in the synthesis of bis(alpha-iminopyridine)diaquanickel (II) complex. The synthetic procedure involves the oxidative addition of methoxyphosphonium salts with non-coordinating anions, and an orthogonal Taguchi design was employed to determine the optimal conditions. The optimal synthesis conditions were identified as a reaction time of 3 h, a temperature of 0 degrees C, and a stirring speed of 350 rpm, resulting in a 90% yield. The synthesized dicationic nickel (II) complexes with aryl-ortho substituted bis(alpha-iminopyridine) ligands were thoroughly characterized by FTIR, UV-Vis, and NMR spectroscopy. Single crystals of [Ni(H2O)2(Me)2ImPy(Me)][(PF6)2] were successfully grown by antisolvent crystallization and analyzed by single-crystal X-ray diffraction, revealing a monoclinic structure with space group P21/n and specific lattice parameters. The Taguchi optimization method effectively improved the yield of the nickel (II) dicationic precatalyst synthesis. The characterization confirmed the paramagnetic octahedral nickel (II) nature and provided insights into the crystal structure and supramolecular interactions. DFT calculations were performed to validate the complex geometry, and the results supported the crystallographic data by confirming the optimized coordination environment and electronic properties of the complexes.

Designing Photonic Crystal Fibers incorporating the Surface Plasmon Resonance Phenomenon (PCF-SPR) has led to numerous interesting applications. This investigation presents an exceptionally responsive surface plasmon resonance sensor, seamlessly integrated into a dual-core photonic crystal fiber, specifically designed for low refractive index (RI) detection. The integration of a plasmonic material, namely silver (Ag), externally deposited on the fiber structure, facilitates real-time monitoring of variations in the refractive index of the surrounding medium. To ensure long-term functionality and prevent oxidation, a thin layer of titanium dioxide (TiO2) covers the silver coating. To optimize the sensor, five key design parameters, including pitch, air hole diameter, and silver thickness, are fine-tuned using the Taguchi L8(25) orthogonal array. The optimal results obtained present spectral and amplitude sensitivities that reach remarkable values of 10,000 nm/RIU and 235,882 RIU-1, respectively. In addition, Artificial Neural Network (ANN) optimization techniques, specifically Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO), are used to predict a critical optical property of the sensor confinement loss (αloss). These predictions are derived from the same input structure parameters that are present in the full L32(25) design experiment. A genetic algorithm (GA) is then applied for optimization with the goal of maximizing the confinement loss. Our results highlight the effectiveness of training PSO artificial neural networks and demonstrate their ability to quickly and accurately predict results for unknown geometric dimensions, demonstrating their significant potential in this innovative context. The proposed sensor design can be used for various applications including pharmaceutical inspection and detection of low refractive index analytes.

The combination of metal-organic frameworks (MOFs) and metal nanoparticles offers great prospects for improving the electrochemical properties of sensors. In this paper, silver (Ag) nanoparticle-doped metal-organic framework (MIL-101) composites (Ag-MIL-101) were prepared by ultrasonic treatment of MIL-101 and the reduction of the metal precursor (AgNO3) within the MIL-101 material. X-ray diffraction patterns confirmed the formation of Ag-MIL-101(Cr). The resulting material was used to construct a new electrochemical sensor for the reliable detection of dopamine. The electrochemical response of the developed sensor toward dopamine was evaluated using differential pulse voltammetry. A Box-Behnken design was performed, and response surface methodology was used to study the influence of different parameters on dopamine detection. The response of the modified electrode for dopamine detection was linear in the range from 0.02 mu M to 10 mu M, and the detection limit was 0.02 mu M (S/N = 3). Additionally, it showed high selectivity in the presence of urea, uric acid, ascorbic acid, and L-arginine. Due to its low cost, easy process, and great performance, this Ag-MIL-101/GCE electrode can be a good candidate for the fabrication of a non-enzymatic dopamine sensor.

This study focuses on the development and optimization of a modified electrode for urea detection using a molecularly imprinted polymer (MIP)@ZIF-7 composite. The electrode surface was prepared by mechanical polishing followed by electropolishing in H 2 SO 4 , and then modified by electropolymerization of pyrrole in LiClO 4 solution. Factors influencing the performance of the electrode were systematically investigated using Plackett-Burman Design (PBD) for screening and Central Composite Design (CCD) for optimization. An artificial neural network (ANN) model was trained to predict the electrochemical response based on various parameters. The ANN showed high prediction accuracy with $\mathbf{R}^{\mathbf{2}}$ values of 0.96815 (training), 0.9917 (validation) and 0.9937 (test). The optimized electrode showed excellent performance in urea detection as evidenced by differential pulse voltammetry with a linear response range from $\mathbf{10}^{-\mathbf{11}}$ to $\mathbf{10}^{-\mathbf{5}}\mathbf{M}$ and a low detection limit of $\mathbf{5.10}^{-\mathbf{12}}\ \mathbf{M}$ . Overall, this study highlights the effectiveness of the MIP@ZIF-7 modified electrode for sensitive and selective urea detection.

This study deals with the optimization of Fe(III) ion removal using activated carbon from olive stone waste using advanced machine learning models. The main objective is to evaluate and compare the performance of machine learning models, specifically multilayer perceptron artificial neural network (MLP‐ANN), general regression artificial neural network (GR‐ANN), radial basis function artificial neural network (RBF‐ANN), and particle swarm optimization artificial neural network (PSO‐ANN) in predicting Fe(III) removal efficiency. Experimental data on adsorption parameters were used to train and test the models. Techniques such as tuning hidden layer neurons, optimizing propagation values, and using a Taguchi approach PSO algorithm were applied to improve the models. For the MLP‐ANN model, the optimal configuration contains 13 neurons in the hidden layer. Concerning the parameters involved in the PSO‐ANN model, the coefficient C2 and the particle have the main effect on the reduction of the error. Their contributions are respectively 49% and 19%. The PSO‐ANN model showed superior performance with the highest regression coefficient (0.9997) and remarkable prediction accuracy, surpassing other models such as MLP‐ANN and GR‐ANN. This research suggests that innovative optimization techniques, particularly using PSO algorithms, significantly enhance the predictive capabilities of machine learning models in complex adsorption processes, contributing to more accurate Fe(III) removal models.

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 ( omega ), biosensor position ( XS ), angular alignment ( theta ), and radial displacement ( RD ). Our research also involves a comprehensive performance evaluation of the PSO-ANN model, comparing it to an alternative multilayer perceptron (MLP) 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.