The current research investigates a novel double perovskite halide absorber K2NiCl6 regarding its structural stability as well as electronic and optical properties using Density Functional Theory (DFT) calculations. According to the band structure, the direct band gap of K2NiCl6 is 1.179 eV. Using SCAPS-1D simulator, the emphasized configuration, ITO/IGZO/K2NiCl6/MoTe2/Pt, is also investigated. However, a simulated efficiency of up to 30.11

The performance of halide-based photovoltaic devices is dictated by complex interactions among band alignment, defect states, interfacial energetics, and charge transport. In this study, physics-based SCAPS-1D simulations are combined with...

Optical refractive sensors have a great potential for biomolecule identification because of their distinct spectral signatures, increased sensitivity, reduced interference, and label-free, nondestructive analysis capabilities compared to visible range sensors. Furthermore, the capacity of multi-mode metal-dielectric sensors to outperform their single-mode counterparts through long-wavelength tuning, improved information retrieval, and less false findings through multimode data cross-referencing makes them extremely beneficial. In this work, we utilized tilted silicon square block arrays in a metal-dielectric-dielectric (Al-SiO2-Si) structure to concurrently obtain a high sensitivity. The proposed structure allows for the detection of a wide range of biomolecules by supporting three modes that are produced by angle sensitivity and Fabry-Perot cavity-based BIC (FP-BIC) electric quadrupole (EQ) and toroidal dipole (TD) resonances. The proposed sensor has achieved the sensitivity for the peak of P1, P2, and P3 is Sp1=1050nm/RIU, Sp2= 2000 nm/RIU and Sp3=1237nm/RIU, respectively. The obtained figure of merit for peak P1 is 13.23 RIU−1, for peak P2, it is 30.25, and for peak P3 it is 17.67 RIU−1. The machine learning models of random forest, linear regression and Xgboost are used for estimating correlation value of R2 and determined values are 0.96 and 0.99 and 0.95 for these three models respectively. The proposed sensor effectively used for detecting various anemia and cancer cells, indicating significant promise for its bio-sensing application and real-time biomolecule dynamics monitoring. The Proposed sensor demonstrates the ability to detect wide range of biomolecules and has the potential to be used as an important tool for identification of different types of diseases.

ABSTRACT In this work, we present a machine learning–augmented simulation study of a MoTe 2 ‐based solar cell incorporating Sb 2 S 3 (antimony sulfide) as the hole transport layer (HTL). MoTe 2 was selected as the absorber due to its strong optical absorption, low toxicity, and compatibility with low‐cost fabrication methods. Using SCAPS‐1D, we optimised the heterojunction structure (Al/FTO/CdS/MoTe 2 /Sb 2 S 3 /Pt) by varying absorber and HTL thickness, doping concentration, defect density, and temperature. The best simulated device (MoTe 2 thickness 0.5 μm, N A ≈ 10 17 cm −3 ) achieves V oc ≈ 1.05 V, J sc ≈ 40.8 mA/cm 2 , FF ≈ 87.6%, and a power conversion efficiency (PCE) of 40.33%. We clarify that this extremely high efficiency represents a theoretical upper bound under idealised assumptions (e.g., negligible nonradiative losses), rather than an experimentally demonstrated result. For comparison, the baseline cell without Sb 2 S 3 yields V oc ≈ 0.95 V, J sc ≈ 38.15 mA/cm 2 , FF ≈ 81.09%, and η ≈ 29.35%. The Sb 2 S 3 layer significantly suppresses back‐surface recombination and improves carrier extraction, thereby enhancing V oc and FF. To streamline design, we generated a dataset of approximately 6735 SCAPS simulations spanning key input variables (thickness, doping density, defect density, temperature) and trained five regression models for performance prediction. Among these, Random Forest regression achieved the highest accuracy ( R 2 ≈ 0.98), effectively capturing nonlinear dependencies. Feature‐importance analysis confirmed that absorber thickness, defect density, and doping are the dominant performance drivers, consistent with the physics‐based trends. This hybrid SCAPS–ML framework provides a fast, data‐driven tool for optimising next‐generation solar cells. Our study advances previous work by explicitly identifying robust parameter ranges, introducing predictive modelling, and clarifying the theoretical bounds of simulated efficiency.

Rice cultivation faces increasing challenges from climate variability, threatening global food security. This study presents a climate-aware, IoT-enabled framework that integrates explainable artificial intelligence for real-time assessment of rice-growing suitability using a normalized favourability score. The proposed approach uses a primary dataset from eight Northern Bangladesh districts covering the Aman, Aus, and Boro varieties, with feature engineering informed by Bangladesh Rice Research Institute (BRRI) expertise to generate comprehensive favourability metrics. The core contribution is the development of optimized tree-based ensemble methods, Stacked Ensemble, Optimized Random Forest, and Optimized Extra Trees, designed to mitigate overfitting while achieving high predictive accuracy. Experimental results demonstrate robust performance, with the Stacked Ensemble achieving a root mean square error of 0.0159, an R2 of 0.9365, and a mean absolute error of 0.0075; the Optimized Random Forest attaining a root mean square error of 0.0160, an R2 of 0.9358, and a mean absolute error of 0.0072; and the Optimized Extra Trees yielding a root mean square error of 0.0162, an R2 of 0.9338, and a mean absolute error of 0.0072. The deployed IoT infrastructure enables real-time data acquisition, while explainable AI ensures transparency and user trust. This framework advances precision agriculture by providing an effective decision-support system for climate-resilient rice production and sustainable food security. Code publicly available at: https://github.com/abidhasanrafi/rice-yield-predictor .

Lead-free double perovskites solar cells are promising due to the tuneable band gaps, superior intrinsic environmental stability, and non-toxicity. In this study, we improve the performance of Rb2LiGaI6-based solar cell structures, where Au/MoO3/Rb2LiGaI6/WO3/FTO heterojunction device has been designed by investigating and comparing structures consisting of various combinations of hole transport layers (HTL) and electron transport layers (ETL). Using the SCAPS-1D simulation platform, fundamental device parameters, including absorber thickness, defect density, operating temperature, interface defect density, and electron affinity, are methodically changed to investigate their effects on device performance. Moreover, a simulated dataset comprising 3456 entities is further utilized to train and test four machine learning (ML) models Random Forest, Support Vector Regression, Neural Networks, and XGBoost. These models confirmed simulation trends, simplified the identification of high-efficiency configurations, and helped to find important performance-determining parameters with an outstanding R2 of 99.99 % and a very low MSE of 0.003 which is demonstrated by the XGBoost model. The optimized hetero-structure consisting of MoO3 HTL and WO3 ETL has achieved a power conversion efficiency (PCE), an open-circuit voltage (Voc), a short-circuit current density (JSC), and a fill factor (FF) of 32.64 %, 1.00 V, 38.02 mA/cm2, and 85.85 %, respectively. This study introduces research perspectives toward efficient, ecofriendly, and practical solar energy conversion using Rb2LiGaI6-based double perovskite solar cells (DPSCs).

Although several materials including silicon, III-IV, and perovskites are being tested under space environments, no reports of the emerging transition metal dichalcogenide (TMDC) solar cells (SCs) for space environments have been found as of now. Proton radiation is a common and primary occurrence in space. Hence, we have investigated the effect of 0.15 MeV and 3 MeV proton radiation on four different TMDC heterojunction thin-film SCs with frequently used hole and electron transport materials. We have used the SR-NIEL web application, Monte- Carlo simulation toolkit SRIM, solar cell capacitance simulation SCAPS-1D, and machine learning analysis for evaluating the energy loss, defect creation and performance analysis. MoS2 and MoSe2 had higher power conversion efficiencies (PCE) of 28.16 % and 28.11 %, respectively, while WSe2 and WS2 had high short-circuit current densities (Jsc) of 39.36 mA/cm2 and 35.15 mA/cm2, respectively, under standard operating conditions (AM 1.5G, 1000 W/m2 incident photon flux). However, when exposed to 0.15 MeV proton radiation, the performance of all devices significantly decreased. MoSe2 and WS2 had relatively higher PCEs of 19.47 % and 17.01 %, respectively, compared to WSe2 and MoS2, indicating some resistance to radiation-induced deterioration. Machine learning predictions revealed that WS2 and WSe2 are more sensitive to proton radiation than MoSe2 and MoS2 absorbers. MoSe2 and MoS2 absorber-based SCs, on the other hand, appear to be more resistant to the negative effects of proton radiation, making them better candidates for use in radiation-rich areas. These findings emphasize the relevance of material robustness to radiation when building SCs for space or radiation environments, as well as the promise of MoSe2 and MoS2 absorbers in such applications.

This study explored the use of cadmium telluride (CdTe) as a back surface field (BSF) in tin selenide (SnSe)-based heterojunction thin-film solar cells to enhance affordability and performance. Using the Solar Cell Capacitance Simulator (SCAPS-1D), we first modeled and analyzed a baseline structure of Cu/SnO2/CdS/SnSe/Au without a BSF layer. Subsequently, we investigated six alternative configurations incorporating different BSF materials: Cu/SnO2/CdS/SnSe/CFTS, MoS2, PEDOT:PSS, CuO, P3HT, CdTe/Au. Our detailed analysis revealed that the CdTe BSF provided superior performance compared to the other BSFs. Additionally, we optimized the thickness of the electron transport layer (ETL) and the absorber layer to maximize power conversion efficiency (PCE), short-circuit current (Jsc), fill factor (FF), and open-circuit voltage (Voc). We also varied the bulk defect density and interface defect density to assess the device's defect tolerance. Analyzing series resistance (Rs), shunt resistance (Rsh), carrier concentrations at different device depths, J-V curves, and temperature effects, we identified optimal photovoltaic (PV) parameters for the CdTe BSF, alongside a Voc of 0.96 V, Jsc of 38.42 mA/cm2, FF of 86.49%, and PCE of 32.34%. In contrast, the reference structure without a BSF achieved only a 26.81% PCE. This cost-effective novel structure demonstrates promising durability and high performance.

In the agricultural industry, precise mango type categorization is essential for quality evaluation, grading, and post-harvest management. Using an enhanced MobileNetV2 architecture, this work proposes a deep learning-based method for the automatic categorization of six mango varieties: Chaunsa (Black), Chaunsa (White), Dosehri, Fazli, Langra, and Sindhri. Using depthwise separable convolutions and a unique fine-tuning technique, the suggested model, called DC-MobileNetV2, enhances classification performance while preserving computational economy. With a balanced dataset, a thorough analysis was carried out, contrasting DC-MobileNetV2 with a number of cutting-edge CNN architectures, including ResNet101V2, ResNet152V2, Xception, InceptionResNetV2, InceptionV3, and VGG16. With weighted F1-scores of 95.02 % and macro and overall accuracy of 95.00 %, the suggested model outperformed all baseline models. The model's dependability was further validated using ROC analysis, which showed that AUC scores for all classes ranged from 0.99 to 1.00. The robustness and discriminative capacity of the model were demonstrated by the confusion matrix analysis, which showed few misclassifications, especially among closely related mango types. With potential uses in automated sorting and quality control systems in the agricultural sector, our results imply that Deep Convolutional MobileNetV2 is a very efficient and portable solution for real-time fruit categorization tasks.

The conventional cadmium sulfide (CdS) window/buffer layer in photovoltaic cells is environmentally hazardous because of the poisoning of cadmium (Cd). Alternatively, ZnS is more environmentally friendly than CdS and has a larger band gap, which makes it a potential candidate for window/buffer layers. In this study, ZnS thin films were deposited on glass substrates by a spin coating process and annealed at three (250 °C, 350 °C, and 450 °C) different temperatures. The XRD patterns confirmed that all the spin coated films had mixed wurtzite and cubic structures with a preferred orientation along the (111) plane of the predominant cubic phase. The highest crystallite size and lowest dislocation density were found at 350 °C annealing temperature due to the narrow, sharp and high intensity diffraction peak compared with those at 250 °C and 450 °C annealing temperatures. The SEM results indicate that the surface of the ZnS film annealed at 350 °C has a better surface coverage area with good uniformity, and is more homogeneous with a minimum amount of pinholes, voids and cracks than the other samples annealed at 250 °C, and 450 °C. The estimated optical band gap was determined to be between 3.957 and 3.991 eV. The calculated electrical resistivity values are on the order of 10^4 Ω cm. All the findings revealed that the film annealed at 350 °C presented good material properties for utilizing as buffer layer in thin film solar cells.

In this work, we explore a wheel-shaped hollow-core photonic crystal fiber (HC-PCF)-based optical alcohol sensor that operates in the terahertz (THz) region. We employ the finite element method (FEM) along with COMSOL Multiphysics software to simulate the structure and perform a numerical analysis of the model. In this configuration, alcohol analytes are integrated into the fiber's core. The results from the FEM simulation indicate that the proposed optical HC-PCF sensor achieves high sensitivity levels of 97.61% for ethanol, 98.80% for butanol, and 98.53% for propanol, all at a frequency of 2.1 THz. The measured low confinement losses at 2.1 THz are 8.0696 x 10-8 dB/m, 7.1966 x 10-11 dB/m and 3.6334 x 10-10 dB/m. Furthermore, the effective areas are 7.5911 x 10-8 m2, 7.8258 x 10-8 m2, and 8.0847 x 10-8 m2 for three types of alcohol. Additionally, we discuss the concepts of effective material loss, effective mode index, and total power fraction. Existing technologies can facilitate the fabrication of this proposed sensor. Moreover, we anticipate that the application of our fabricated sensor will extend to biomedicine, biosensing experiments, industrial applications, material research, healthcare, alcohol detection in drinks and liquids, and other THz communication technologies based on waveguides.

Cadmium telluride (CdTe) absorber layer in solar cells (SCs) is environmentally dangerous for the toxic behavior of cadmium (Cd). Alternatively, zinc telluride (ZnTe) is deliberated as a promising PV material for its adoptable absorption coefficient, better conversion efficiency and low production cost of materials requirements. The main objective of this study is to synthesis and characterization analysis of ZnTe thin films to enhance the performance of ZnS/ZnTe solar cell. The structural, optical, morphological and compositional properties of the ZnTe thin films were investigated by X-ray diffraction, UV-visible spectroscopy, scanning electron microscopy, and energy dispersive spectroscopy. The performance of the cell was analyzed by SCAPS-1D. The XRD results showed that all the spin coated ZnTe thin films are in cubic phase. The determining optical band gap values are in the range of 1.77-2.18 eV. The SEM images indicated that the surface of ZnTe thin film annealed at 400 °C has better surface coverage area with homogeneity, good uniformity, and minimum void compared to the other annealed samples. The EDS study exhibits that all the films are Te richness with p-type conductivity. The highest power conversion efficiency (PCE) is found 17.45% with V oc of 1.41 V, J sc of 14.01 mA cm-2 and FF of 88.53% for the 1184 nm optimum thickness of ZnTe and annealed at 400 °C. zinc sulfide (ZnS), indium tin oxide (ITO), platinum (Pt) and aluminum (Al) are indicated as buffer layer, transparent conductive oxide, back metal and front metal respectively of the device. All the findings confirmed that the deposited ZnTe thin films are suitable for usage as an absorber layer in thin film solar cells (TFSCs).

The tandem solar cell (TSC) with top and bottom sub-cells is able to absorb sunlight from visible to near infrared range and is promising for enhancing the photo-conversion efficiency. We have proposed a TSC with a bandgap of 1.8 and 1.29 eV for carbon nitride (C2N) as a top sub-cell and tungsten disulfide (WS2) as a bottom sub-cell absorber layer, respectively. In this research study, 1D solar capacitance simulation software (SCAPS-1D) is used to analyze the open circuit voltage (V-oc), short circuit current (J(sc)), fill factor (FF), and photo-conversion efficiency (eta) of the top and bottom sub-cell for the design of two-terminal (2-T) C2N-WS2 TSC. The current matching condition of J(sc) has been determined at the absorber layer thickness of 450 and 790 nm for the top and bottom sub-cells, respectively. The TSC device parameters such as defect density and doping density have been optimized at 10(15) (C2N), 10(14) (WS2) cm(-3), and 10(17)cm(-3) (C2N and WS2), respectively for achieving better performance of the proposed structure. The determined optimum values of V-oc, J(sc), eta, and FF are 2.38 V, 17.40 mA, 37.60%, and 90.78%, respectively. The present research paves the path for the realization of the high efficiency TSC.

The quest for efficient and sustainable energy solutions has propelled the exploration of novel materials and strategies for enhancing the performance of thin-film solar cells (TFSCs). This work presents a comprehensive investigation into the potential of CuBi2O4 based TFSCs as a viable candidate for high-efficiency photovoltaic devices. Through rigorous numerical simulation utilizing the SCAPS-1D software, this study delves into the intricate interplay of material properties, layer characteristics, and design strategies to unlock the untapped potential of CuBi2O4 based SCs. The study extensively investigates the influence of thickness, doping levels, and defect densities of each absorber on electrical properties like open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), and power conversion efficiency (PCE). The simulation results reveal a remarkable achievement, with a recorded efficiency of 36.04%, FF of 81.11%, JSC of 32.15 mA/cm², and VOC of 1.38 V. These findings point to the potential of thin-film SC based on CuBi2O4 as a greener and more efficient photovoltaic option. As an absorber material for next-generation SC, CuBi2O4 exhibits potential with an efficiency of 36.04%. This investigation advances CuBi2O4-based thin-film SC and provides light on sustainable energy solutions.

Vehicle sensing is key to implementing AI-based driving and monitoring systems. Vehicles on the road have increased dramatically. For that, managing the transportation system becomes difficult. To solve this problem, this article proposes a vision-based vehicle detection system. In this study, we developed real-time multi-object media detection based on “You only look once” algorithm (YOLOv5). We analyzed the accuracy of vehicle detection using YOLOv5s (small), YOLOv5n (nano), YOLOv5l (large), YOLOv5m (medium), and the largest of the five YOLOv5x. The test results confirm that the YOLOv5x model can provide higher detection accuracy than other algorithms. The main indicators of accuracy are Precision, Recall, and mAP (0.5).The determined accuracy of the YOLO5s, YOLOv5 m, YOLOv5n, YOLOv5l, and YOLOv5x algorithms on the dataset were 62.4, 64.2, 62.9, 68.7, and 69.7

In this study, SCAPS-1D simulator was used to investigate the performance of a solar cell structure based on Molybdenum Telluride (MoTe2) with Sb2S3 (Antimony Sulfide) Hole Transport Layer (HTL). The motivation behind choosing MoTe2 as an absorber layer for its higher optical absorption efficiency, cost-effectiveness, reliable and stable operation. The comparative study of this introduced (Al/FTO/CdS/MoTe2/Sb2S3/Pt) and baseline solar cell (Al/FTO/CdS/MoTe2/Pt) has been implemented. Various photovoltaic parameters like open-circuit voltage, short-circuit current, fill factor, and efficiency have been investigated varying absorber and HTL thickness, doping density, rare surface recombination velocity, defect density, series as well as shunt resistance and temperature. The proposed solar cell performance of η, Voc, Jsc, and FF was found to be 40.33%, 1.13 V, 40.78 mA/cm2, 87.63% optimizing absorber thickness value of 0.5 μm and doping concentration value of cm-3. The determined values of performance parameters Voc, Jsc, FF, and η are 0.95 V, 38.15 mA/cm2, 81.09% and 29.35%, respectively for baseline solar cell. The implantation of Sb2S3 layer contributes to improve the performances by diminishing carrier recombination losses. The present research results indicate the feasible way for obtaining a lower-cost, and higher-efficiency MoTe2-based SC with Sb2S3 HTL layer.

Perovskite-based tandem solar cells (SCs) show significant potential for improving efficiency. In this study, three configurations were designed and optimized: a top cell (ITO/ZnSe/CH3NH3GeI3/CuSCN/Ni), a bottom cell (ITO/ZnSe/CH3NH3SnI3/CuI/Ni), and a tandem cell combining both. Using SCAPS-1D, the study evaluated how the absorber layer thickness, doping concentration, and defect density affected photovoltaic (PV) performance. It also explored the influence of doping in the back surface field (BSF), interface defect densities, temperature, and back contact work function. The top cell achieved a power conversion efficiency (PCE) of 28.47%, with an open-circuit voltage (V-OC) of 1.21 V, a short-circuit current density (J(SC)) of 27.17 mA/cm(2), and a fill factor (FF) of 86.24%. The bottom cell reached a PCE of 18.46%, with a V-OC of 0.83 V, a JSC of 27.17 mA/cm(2), and an FF of 81.839%. The optimized tandem structure demonstrated a notably higher efficiency of 46.89%, with a J(SC) of 27.17 mA/cm(2), a V-OC of 2.04 V, and an FF of 84.31%. These results suggest the proposed CH3NH3GeI3/CH3NH3SnI3 tandem design could pave the way for efficient, eco-friendly, and cost-effective PV cells in a near future.

The remarkable performance of copper indium gallium selenide (CIGS)-based double heterojunction (DH) photovoltaic cells is presented in this work. To increase all photovoltaic performance parameters, in this investigation, a novel solar cell structure (FTO/SnS2/CIGS/Sb2S3/Ni) is explored by utilizing the SCAPS-1D simulation software. Thicknesses of the buffer, absorber and back surface field (BSF) layers, acceptor density, defect density, capacitance-voltage (C-V), interface defect density, rates of generation and recombination, operating temperature, current density, and quantum efficiency have been investigated for the proposed solar devices with and without BSF. The presence of the BSF layer significantly influences the device's performance parameters including short-circuit current (J(sc)), open-circuit voltage (V-oc), fill factor (FF), and power conversion efficiency (PCE). After optimization, the simulation results of a conventional CIGS cell (FTO/SnS2/CIGS/Ni) have shown a PCE of 22.14% with V-oc of 0.91 V, J(sc) of 28.21 mA cm(-2), and FF of 86.31. Conversely, the PCE is improved to 31.15% with V-oc of 1.08 V, J(sc) of 33.75 mA cm(-2), and FF of 88.50 by introducing the Sb2S3 BSF in the structure of FTO/SnS2/CIGS/Sb2S3/Ni. These findings of the proposed CIGS-based double heterojunction (DH) solar cells offer an innovative method for realization of high-efficiency solar cells that are more promising than the previously reported traditional designs.

This work proposes a hollow-core photonic crystal fiber-based edible oil sensor in the terahertz (THz) range (e.g., 1.0THz ≤ f ≤ 3.0THz) and different sensing characteristics are numerically analyzed. The suggested sensor’s performance was assessed by means of COMSOL Multiphysics, a commercial program that uses the finite element approach. The computational results indicate that the relative sensitivity is 85.591%, 84.648%, 82.625%, 82.683%, and 79.161%, respectively, at f = 2.2 THz, for several types of sunflower oil, mustard oil, coconut oil, olive oil and palm oil; and the corresponding effective areas are 7.22×10-8 um2, 7.09×10-8 um2, 6.83×10-8 um2,7.09×10-8 um2, 6.5231 ×10-8 um2. In addition, the effective material loss for sunflower oil, muster oil, coconut oil, olive oil, and palm oil has been found to be 0.02561 cm-1, 0.027054 cm-1, 0.030322 cm-1, 0.028854 cm-1 ,0.035427cm-1 respectively. Moreover, the proposed sensor also has low confinement loss are 1.55×10-8 dB/m, 1.63×10-8 dB/m, 1.31×10-8 dB/m, 1.99×10-8 dB/m, 4.0345×10-8 dB/m.This proposed sensor can be fabricated using extrusion and 3D-printing technologies, and due to its augmented detecting capabilities, it can be a vital part of oil sensing devices implemented in real life such as industry fields.