Perovskite types of photovoltaic sources have become the primary candidates for next-generation photovoltaic sources thanks to their high-quality optoelectronic characteristics and their fast-enhancing power-conversion parameters. Nonetheless, the computational space of perovskites is quite large and first-principles is expensive, and this makes systematic optimization of materials a challenging problem. This paper presents a unified multiscale system that entails the implementation of density functional theory (DFT), machine learning (ML), and device-level simulation to hasten the search and development of high-performance perovskite solar cell materials. Physically significant descriptors are generated through DFT calculations such as structural parameters, electronic bandgaps, formation energies, and properties of defect-related characteristics. The descriptors are utilized to train the supervised ML models that can predict the important material and photovoltaic performance indicators with high accuracy at a rapid rate. Predictions of the ML are then combined with practicing simulations of the device to assess open-circuit voltage, short-circuit current density, fill factor, and power conversion efficiency. The findings show that there are good correlations among defect tolerance, bandgap optimization, and photovoltaic performance with defect suppression being a leading channel of increasing the efficiency. The current DFT-ML scheme consumes much less computational power; however, still has physical interpretability, and can offer efficient and predictive solution to perovskite material screening as well as solar cell design scaling.
Multi-spherical shaped of silver nanoparticles AgNPs were successfully synthesized using laser ablation method with three different laser energy (150 mJ, 300 mJ and 450 mJ). The XRD diffraction analysis are confirm of prepared of Ag and AgO particles with a face-centered cubic structure, the Average crystalline size are equal to 12.946 nm, 11.338 nm and 10.060 nm for AgNPs samples exposed with laser energy 150 mJ, 300 mJ and 450 mJ respectively. The EDX analysis are demonstrating to the crystalline Silver and Silver oxides nano particle. The SEM analysis showed that silver nanoparticle exhibits various clusters of spherical nanoparticles shapes with different nano-diameter .The Pseudomonos auruginosa and Klebsiella pneumoniae are used to investigated the AgNPs antibacterial activity. The results show that zone of inhibition (ZOI) diameter to both bacteria’s are varying when the bacteria’s are exposed with sample AgNPs prepared by laser energy 150 mJ, 300 mJ and 450 mJ. The varying range are between (18 to 15 nm) and between (23 to 15 nm) for Pseudomonos auruginosa, and Klebsiella pneumoniae bacteria respectively.
This study presents a data-driven predictive model for estimating Electromagnetic Interference (EMI) Shielding Effectiveness (SE) over a frequency range of 100 MHz to 12 GHz. A hybrid modelling method was developed that uses both material-specific parameterization and frequency–response correlations. The Ordinary Least Squares (OLS) regression technique was employed for model calibration, resulting in a linear model as a function of frequency. The framework was implemented in MATLAB and tested using six independent experimental datasets like conventional metals (copper, aluminium, steel, silver) and advanced materials (graphene, carbon composites). The dataset was randomly split into 70
Polypyrrole (PPy) and aluminum(8-hydroxyquinolineate) (Alq3) are both promising materials in the field of organic electronics. PPy is characterized by high electrical conductivity and good environmental stability, while Alq3 is known for its excellent optical properties and is one of the most common materials used in the fabrication of organic light-emitting diodes (OLEDs). This research focuses on the preparation of PPy/Alq3 composite thin films using spin-coating technology and investigating the effect of mixing ratios and heat treatment (annealing) on the morphological and structural properties of these films. Scanning field electron microscopy (FESEM) analysis revealed that the prepared films exhibit homogeneity and a smooth surface, with minimal effect of annealing on the overall morphology. X-ray diffraction (XRD) analysis showed that Alq3 retains its amorphous nature in all samples, while PPy exhibits characteristic diffraction peaks indicating its semi-crystalline nature. It was also observed that annealing leads to an increase in the intensity of the PPy peaks, reflecting an improvement in the polymer’s microcrystalline structure. These results suggest the potential use of these composites in various optoelectronic applications, particularly in organic solar cells and sensors.
Zinc sulfide (ZnS) is a widely used semiconductor with applications across various fields, prompting the need for environmentally friendly synthesis routes. Here, we present a hybrid hydrothermal–biosynthetic approach for the fabrication of nanostructured ZnS using garlic (Allium sativum) as a natural sulfur source. Reactions were carried out at 160 °C for 1, 7, and 24 h. Structural, compositional, morphological and thermal analyses confirmed the formation of blende-phase ZnS nanostructures with distinct morphologies: quasi-spherical particles (Eg 3.95 eV) at 1 h, core–shell structures (Eg 3.6 eV) at 7 h, and a mixture of quasi-spherical ZnS (Eg 3.7 eV) and biomass-derived particles at 24 h. A mechanism for particle formation and growth is proposed based on experimental evidence. Photocatalytic tests showed an enhanced photocatalytic response in the sample at 7 h, achieving 56
Nanotechnology offers a powerful platform for engineering advanced materials with multifunctionality. This work details the fabrication and characterization of nano-selenium hybrid systems (SeNPs-dye) based on phenazone bis-azo dyes for application in polyester textiles. A series of bis-azo dyes based on phenazone and phloroglucinol scaffolds was synthesized and evaluated for antimicrobial activity and suitability in functional polyester printing. Biological screening showed that only the phenazone derivatives displayed significant activity, whereas the phloroglucinol analogues were essentially inactive. Selenium nanoparticles were therefore selectively integrated with the active phenazone dyes to afford nano-selenium hybrids, from which SeNPs-9d emerged as the lead system. SeNPs-9d exhibited broad-spectrum antibacterial activity against Staphylococcus aureus, Bacillus cereus, Escherichia coli, and Salmonella typhi, together with potent inhibition of α-amylase, α-glucosidase, and lipase. When applied to polyester fabric by screen printing, the SeNP-modified dyes produced level prints with high color strength, stable shade build-up, and excellent fastness to washing, rubbing, perspiration, sublimation, and light while retaining both color and antimicrobial performance after repeated laundering. Computational and ADMET studies supported these findings by indicating favorable binding energetics, electronic reactivity, and acceptable predicted safety for SeNPs-9d. Overall, nano-selenium modified phenazone bis-azo dyes, particularly SeNPs-9d, appear as promising antimicrobial colorants for advanced medical and protective textile applications.
The present research focuses on the design, fabrication and optimization of NLC gel for topical delivery of Gabapentin for management of peripheral neuropathic pain. Particle size, zeta-potential, drug entrapment efficiency and in vitro drug release were the NLC characteristics initially established and optimized by means of quality by design. The optimized NLC formulation was a moderately polydisperse system (PDI = 0.331) with an average size of 144.4 nm and with zeta potential − 27.2 mV. After that, the 1
Lead-free perovskite solar cells are promising candidates for sustainable photovoltaic technologies, but their performance and practical use are still limited by the choice of stable, efficient, and affordable back electrodes. Silver (Ag) is highly conductive and low-cost, but it can suffer from corrosion and ion diffusion, while gold (Au) provides better stability but is expensive. In this study, Ag–Au thin-film back electrodes with different compositions, Ag95Au5, Ag50Au50, and Ag5Au95, were prepared by co-thermal evaporation and integrated into glass/FTO/ZnO/CsEuCl₃/NiO/Ag–Au lead-free perovskite solar cells. Structural and morphological analyses confirmed that the electrode composition strongly influenced film crystallinity, surface uniformity, and interfacial quality. Among the tested electrodes, the Ag-rich Ag95Au5 contact showed the best photovoltaic performance, achieving a power conversion efficiency of 9.78
The application of magnetic nanoprobes based on nuclear magnetic resonance (NMR) technology offers a rapid and highly sensitive approach for detecting foodborne pathogenic microorganisms. To enhance the detection efficiency of nuclear magnetic resonance technology for Salmonella and similar pathogenic microorganisms in food, a new detection method was proposed. Firstly, Bio-PEG-MNPs with high water solubility and biocompatibility were obtained by modifying magnetic nanoparticles with chain shaped polyethylene glycol and biotin. Secondly, by analyzing its particle size distribution through dynamic light scattering, a dual antibody sandwich NMR detection system for salmonella was optimized and constructed. Finally, the lateral relaxation time of salmonella was rapidly measured using low field NMR, with a focus on optimizing the key parameters of the detection system. The experiments revealed that Bio-PEG-MNPs had monodisperse and stable properties at MNPs/PEG of 1:15 and MNPs/Bio of 1:21. The experimental parameters for optimal detection of signals were a biotinylated antibody concentration of 1 µg/mL, incubation time of 60 min for both salmonella and probe, and SA dosage of 1 µg/mL. The NMR sensor was able to specifically detect salmonella in PBS with a lower limit of detection of 100 CFU/mL, including a wide range of detections from 102 to 107 CFU/mL. The sensor was effective in detecting salmonella in milk and pork samples in the range of 102 107 CFU/mL and CFU/g. This suggests that the NMR biosensor is a practical and accurate method for identifying salmonella in food. The technique can be used to identify salmonella in actual samples, which will encourage the use of low-field magnetic resonance imaging.
In today’s technology-driven, fast-paced lifestyle, stress has increasingly become an inevitable part of daily life. This has led to numerous physiological and psychological challenges for the medical research fraternity. Therefore, there is a pressing need for accurate and precise biochemical profiling of stress-signaling hormonal fluctuations. Among these biomarkers, cortisol, a steroid hormone closely linked to stress, and nanosensors for cortisol have been widely reported. In this work, A graphene-based electrode sensor was developed for the selective detection of cortisol, utilizing two distinct cross-linking chemistries: the conventional zero-length EDC–NHS linker and the bifunctional N-k-maleimidoundecanoyl-oxysulfosuccinimide ester (SKMUS), which features a 16.3 Å hydrocarbon spacer arm. The study aimed to investigate the effect of linker length and architecture on the surface accessibility and functional activity of a cortisol-specific aptamer. The surface coverage of SKMUS on the graphene oxide-modified screen-printed electrode (GO-SPE) and the aptamer density immobilized via SKMUS chemistry, when compared with that of EDC-NHS chemistry, provided insights into the steric effects and the role of linker length on the sensitivity of cortisol sensing. Furthermore, Atomic Force Microscopy (AFM) provided a detailed, stepwise surface analysis throughout the sensor fabrication process. The results indicated that SKMUS facilitated a more uniform and flexible aptamer orientation due to its optimal spacer length, enhancing target accessibility and recognition efficiency. High specificity was ensured by a 14-mer, high-affinity, rationally truncated aptamer (derived from the 61-mer parent sequence) that minimized cross-reactivity with structural analogues of cortisol. These enhancements in sensitivity, achieved through cross-linking chemistry, can enable early diagnosis, improved monitoring, and preventive therapeutics, paving the way for more effective stress management strategies.
The integration of nanomaterials with green chemistry has emerged as a rapidly growing and safe approach, replacing conventional, toxic synthesis methods and establishing new standards across scientific research. In the present work, novel bismuth nanoparticles (BiNPs) were synthesized using a green route, employing the leaf extract of Eucalyptus camaldulensis as both a reducing and stabilizing agent, followed by the formation of bismuth nanoparticle–chitosan (BiNPs–CS) composites. UV-Vis, FTIR, and XRD spectroscopic techniques were employed to confirm the biosynthesis of BiNPs and BiNPs-CS, as well as the interaction between BiNPs and chitosan. Both XRD and direct size measurement using dynamic light scattering (DLS) confirmed that the average size of the nanoparticles in the composites remained approximately 15 nm. Based on the recovered dried product from synthesis repeated in triplicate, the average yield of synthesized BiNPs-CS was found to be 79
The production of biodiesel generates high concentration, refractory wastewater, posing serious environmental challenges. Traditional treatment methods suffer from low efficiency and a tendency to cause secondary pollution, highlighting the urgent need for the development of efficient and environmentally friendly wastewater treatment technologies. In the current study, a zero-dimensional composite nanomaterial of magnetic iron oxide supported on nano-zero-valent iron (Fe3O4–nZVI) was successfully prepared via co-precipitation and liquid-phase reduction to overcome the problems of the conventional pathways. Different characterizations handled to confirm the morphology, crystal structure, and magnetic properties of the synthesized material. Its removal performance for both dissolved and emulsified oils in biodiesel wastewater was investigated. The obtained Fe3O4–nZVI composite exhibited a uniform spherical structure, good crystallinity, and a saturation magnetization of 105.5 emu/g, demonstrating excellent magnetic separation performance. The high removal efficiency of oily pollutants was attributed to multiple synergistic mechanisms: high-surface-area adsorption by nZVI and magnetic support, reductive degradation by nZVI, rapid solid-liquid separation enabled by magnetic recovery, and particularly the synergistic effect between Fe3O4 and nZVI, which significantly enhanced the material’s stability and reactivity. Under optimal conditions (dosage: 0.5 g/L, pH: 9, temperature: 25 °C, reaction time: 90 min), removal rates for dissolved oil and emulsified oil reached 90.6
Although individual nanomaterials have been extensively investigated to enhance the performance of Portland pozzolana cement (PPC) systems, limited attention has been given to nanostructured composites. In this study, a zeolite/CaO/SiO2 nanocomposite (ZCS) was synthesized to improve the performance of PPC pastes. Nano-CaO and nano-SiO2 were derived from eggshells and corncobs, respectively, via the sol-gel method, while zeolite was obtained from clinoptilolite. The ZCS composite was prepared using an ex-situ sol-gel method, exhibiting a CaO/SiO2 ratio of 2, mesoporous structure, and a BET surface area of 358.37 m2/g. Optimal workability and compressive strength of the cement paste were achieved at 3
Continuous and non-invasive monitoring of arterial pressure is critically important for early detection and management of cardiovascular disorders, as it provides vital insights into blood pressure variations, arterial stiffness, and vascular aging. However, achieving accurate and sensitive measurement of subtle arterial pulse signals under low-pressure physiological conditions remains a significant challenge. This study aims to develop a flexible, wearable capacitive pressure sensor capable of reliably capturing arterial pulse signals at the radial artery. To address this, a capacitive MEMS sensor incorporating a novel graphite–polydimethylsiloxane porous dielectric layer (GPDL-PDMS) was designed, fabricated, and characterized. A sacrificial sucrose porogen was employed to create an interconnected porous microstructure within the PDMS matrix, enhancing compressibility and effective dielectric permittivity. Furthermore, the inclusion of graphite fillers improves dielectric response and mechanical sensitivity, enabling precise detection within the low-pressure range of 0–10 kPa. The developed sensor demonstrates high sensitivity, particularly below 5 kPa, making it highly suitable for physiological monitoring. When deployed over the radial artery, it successfully captures arterial pulse waveforms with a clear distinction between systolic and diastolic peaks, facilitating the extraction of key cardiovascular parameters such as pulse wave velocity and augmentation index. Overall, the proposed sensor, combining low-cost materials, simple fabrication, and enhanced sensitivity, presents a promising platform for next-generation wearable systems for continuous, real-time cardiovascular monitoring and early-stage diagnosis.
This study presents a numerical analysis of steady Darcy–Forchheimer hybrid nanofluid flow over a stretching surface, considering the influences of thermal radiation and motile microorganisms. A hybrid nanofluid created from GO and MoS₂ nanoparticles in water (H2O) as the base fluid is employed in the analysis. Hybrid nanofluids are widely utilized in various fields such as heat exchangers, cooling systems, biomedical instruments, and agricultural applications. The governing model consists of nonlinear PDEs that apply appropriate similarity transformations to become a dimensionless system of ODEs. A three-stage Lobatto approach in MATLAB is employed to numerically solve the resulting solution. The model further investigates the dynamic roles of thermophoresis and Brownian motion. Both graphical and numerical illustrations of the deviations in velocity, temperature, concentration, and motile microbe distributions instigated by involving physical parameters. In the primary component of velocity of fluid deduction and enhance in the secondary component of velocity of fluid. The growing in thermal profile when growth in suction and radiation parameters. When growth in Brownian motion parameter then declined the concentration profile but boost against thermophoresis. Also, the profile of the motile microbe is demonstrated to be reduced by both Lewis number and Peclet number. These results contribute significantly to the advancement of heat transfer efficiency in engineered nanofluids.
The performance of photovoltaic/thermal (PV/T) systems declines significantly under hot climatic conditions because rising cell temperatures reduce electrical conversion efficiency and limit the recovery of useful heat. This study aims to evaluate the effectiveness of CuO/water nanofluid cooling in improving PV/T performance under Iraqi climatic conditions and to assess the capability of machine-learning models to predict system efficiency. A three-dimensional CFD model was developed in ANSYS Fluent for a PV/T system operating under steady laminar flow, with CuO/water nanofluid concentrations ranging from 0.25 to 1 vol
