Advanced Theory and Simulations

The insulating oil in transformers plays a critical role both as a coolant supporting the paper insulation and as an element protecting the dielectric properties of the system. Serious failures may result from changes in the oil's dielectric characteristics brought on by the presence of water and other contaminants. This study assesses the insulation condition of transformer oils by designing and simulating three different dielectric resonator antennas (DRA) in the CST Design Environment. An attempt is made to find the best design by analyzing the effect of variations in dielectric constant on various resonator antenna designs. The resonant frequency range of 1.5−3GHz$1.5 {-}3\;GHz$ and the RT5880 dielectric substrate are chosen. Simulations are carried out to illustrate situations where the εr${\varepsilon _r}$ value of the oil varies between 1$\hskip.001pt 1$ and 5$\hskip.001pt 5$. In general, it is found that as the dielectric constant of the oil (εr${\varepsilon _r}$) increases, the resonant frequency (fr${f_r}$) and the S11${S_{11}}$ parameter decrease. Single DRA–A single SMA port is advised for low‐cost and straightforward applications, but Dual DRA can be chosen for investigations needing high sensitivity and double‐check. The analysis allows the best approach to be selected based on the requirements of the application by highlighting the advantages and disadvantages of different designs.

In this study, a multi‐scale heterogeneous electrochemical‐thermo‐mechanical coupling model (MHETM) is proposed. A two‐dimensional heterogeneous gradient porosity electrode model (U1, G2, and G3) and a 3D macroscopic cell model are combined to realize a multi‐scale coupled multi‐physics field simulation of lithium iron phosphate (LFP) battery from microscopic particles to macroscopic cells. The MHETM model has higher accuracy and can more accurately describe the lithium ion transport process inside the active particles. The results show that the gradient porosity design optimizes the lithium ion diffusion path and improves the diffusion rate and end‐of‐discharge concentration. Meanwhile, the maximum stress and displacement of the G3 model are significantly lower than those of the U1 model, respectively. In addition, the thermal‐mechanical coupling analysis revealed the negative correlation between thermal stress and thermal expansion. The introduction of the macro‐thermal model further facilitates the lithium ion transport, resulting in an increase in the concentration maxima of both the U1 and G3 models, with a more significant increase in the G3 model. The MHETM model provides an effective tool for an in‐depth understanding of the complex multi‐physical field coupling mechanism inside the lithium‐ion batteries.

Natural light‐harvesting antenna complexes efficiently capture solar energy mostly using chlorophyll molecules, i.e., magnesium porphyrin pigments, embedded in a protein matrix. Inspired by this natural configuration, artificial clay‐porphyrin antenna structures have experimentally been synthesized and demonstrated to exhibit remarkable excitation energy transfer properties. The current study presents a computational design and simulations of a synthetic light‐harvesting system that emulates natural mechanisms by arranging cationic free‐base porphyrin molecules on an anionic clay surface. The transfer of excitation energy among the porphyrin dyes is investigated using a multiscale quantum mechanics/molecular mechanics (QM/MM) approach based on the semi‐empirical density functional‐based tight‐binding theory for the ground state dynamics. To improve the accuracy of the results, an innovative multifidelity machine learning approach is incorporated, which allows the prediction of excitation energies at the numerically demanding time‐dependent density functional theory level together with the def2‐SVP basis set. This approach is applied to an extensive dataset of 640 k geometries for the 90‐atom porphyrin structures, facilitating a thorough analysis of the excitation energy diffusion among the porphyrin molecules adsorbed to the clay surface. The insights gained from this study, inspired by natural light‐harvesting complexes, demonstrate the potential of porphyrin‐clay systems as effective energy transfer systems.

In this work, the potential of lead‐free double perovskite Cs2InCuCl6 (CICC) is investigated as a solar cell absorber. CICC exhibits a direct bandgap of 1.1 eV and exceptional thermodynamic stability with high decomposition enthalpies (0.4–67.4 meV atom⁻¹). Utilizing Solar Cell Capacitance Simulator software (SCAPS)‐1D simulations, device architecture, including material selection, layer thicknesses, and doping concentrations, are systematically developed and optimized achieving a high open‐circuit voltage (Voc) of 0.8 V, approaching the Shockley–Queisser limit, an excellent short‐circuit current density (Jsc) of 26.20 mA cm⁻², and a fill factor (FF) of 87.57%. This optimization leads to a record power conversion efficiency of 19.77% with grounds for further enhancement. The key highlight of this study is the incorporation of Mott–Schottky (MS) analysis within the simulation framework, providing unprecedented insights into interfacial charge transport and its impact on device performance. This work paves the way for advanced interface engineering in lead‐free perovskite solar cells, offering a roadmap for realizing highly efficient and stable devices.

Fully inorganic Pb‐Sn perovskite solar cells exhibit excellent photovoltaic conversion efficiency and stability, positioning them as strong competitors to conventional organic–inorganic hybrid perovskite solar cells. By introducing a gradient distribution of Pb and Sn in the perovskite absorber layer, the energy band structure can be optimized and a built‐in electric field can be created within the absorber layer, affecting carrier transport and separation. In this paper, a new structural perovskite solar cell model of F‐doped Tin Oxide/hole transport layer/all inorganic Pb‐Sn gradient perovskite/electron transport layer/MoS2/Ag is proposed, and the structure is optimized and simulated by Solar Cell Capacitance Simulator. First the effects of gradient distribution, doping density, and defect density of the absorber layer are analyzed on the device, and then introduced the 2D material MoS2 as the interface layer. The device performance can be improved by tuning the energy band structure when inserting a 10 nm MoS2 layer, and an energy conversion efficiency of 21.06% is obtained. Finally, the effects of interface defects and different transmission layer materials on the device are considered, and the final optimized device performance parameters are VOC = 0.75V, JSC = 32.1 mA cm⁻², FF = 75.05%, PCE = 17.94%.

Foam core sandwich composite structures have wide structural applications in aerospace; they are subjected to thermo‐mechanical loading environments during their service life. Therefore, it is necessary to predict the fracture behavior of these composite structures accurately. In this work, a computational framework based on the well‐proven, computationally efficient hybrid PFM associated with an orthogonal decomposition scheme is presented and implemented to predict the thermo‐mechanical crack growth phenomena in an orthotropic multi‐material layered system (foam core graphite epoxy laminated composite sandwich structure) under the combined effect of mechanical loading and thermal environment (heating or cooling). The thermo‐mechanical fracture response of the laminated composite sandwich structure (LCSS) is analyzed for crack initiation, crack growth, and load‐bearing capacity. Both the crack intersection and crack merging phenomena are captured during the failure of LCSS under thermal cooling and thermal heating, accompanied by mechanical load. The performance of the LCSS is analyzed by comparing the structural load capacity, crack nucleation threshold, and fracture energy of the structure in multiple numerical cases. The presented methodology, based on a hybrid phase field method and orthogonal strain decomposition scheme, is validated for structural problems from existing literature under the thermo‐mechanical loading and further extended to LCSS structure cases.

Inverted perovskite solar cells (I‐PvSCs) utilizing inexpensive and stable inorganic metal oxide‐based hole transporting layers can reach higher power conversion efficiencies with low hysteresis. In this study, an oxide‐metal‐oxide (OMO) stack is proposed as a transparent conductor (TC) for I‐PvSCs with the overcoat oxide material chosen in such a way that it also acts as a hole transport material (HTL) for the device. The proposed OMO acts as both TC and HTL for the I‐PvSCs device. Using optical simulations based on the transfer matrix method, the OMO stack for maximum average visible transmittance (AVT) and short‐circuit current density (JSC) is optimized. Four different OMO combinations are investigated, with NiO as a fixed overcoat oxide layer due to its hole‐transporting properties. When simulated with a simultaneous variation of up to four different layers, the ZnO/Ag/NiO stack produces the highest AVT (90.24%), while TiO2/Ag/NiO incorporated device attained a best JSC of 23 mAcm⁻². A detailed optical study has been conducted to understand the results, including wavelength‐dependent field distribution within the stack. This study presents optimized OMO designs that can effectively substitute ITO in inverted perovskite solar cells.

In the search for novel magnetically and ferroelectrically active compounds, layered materials have served as an ideal playground for understanding and engineering such properties. Within this type of compounds, the fluoride‐based Dion‐Jacobson (DJ) family (nn = 1) are poorly explored and several issues related to the group‐to‐subgroup phase transitions remain unclear. Here, the symmetry‐allowed structural instabilities of CsFeF4${\rm CsFeF}_{4}$, as a prototype among this magnetically active family of compounds, are examined and highlighted. Based on soft modes present at the high‐symmetry qq‐points of the Brillouin zone, the sequence of allowed structural phase transition is re‐examine using group theory analysis and first‐principles calculations within the density‐functional theory framework. The physical reasons behind the appearance of in‐phase FeF6${\rm FeF}_6$ octahedral rotations over the out‐of‐phase ones are also described. Although this member of the DJ family has not shown any polar structure in its subsequent transitions, which are generally present when nn>$>$1, the [001] (/( superlattice as a potential route is investigated to engineer polarization. The results show that the competing low‐energy phases may disrupt the polarization; nevertheless, it is showed that the appearance of weak polar displacements is symmetry‐allowed.

MXenes, particularly Hf₂C and Zr₂C monolayers, exhibit exceptional electronic and magnetic properties, making them promising candidates for advanced applications. In this study, the effects of Co and Mn doping on Hf₂C and Zr₂C are investigated using first‐principles calculations. The revPBE exchange‐correlation functional is identified as yielding the lowest energy configurations. Molecular dynamics simulations confirm the structural stability of the doped systems, with no signs of phase transitions or instabilities. Doping significantly alters the electronic band structures and magnetic properties. Co doped Hf₂C displays a bandgap, making it suitable for infrared detectors and low‐temperature sensor applications, while Mn doping lead to a significant enhancement of the net magnetic moments relative to the pure monolayers. Applying an external electric field results in significant changes in the magnetic moment, particularly in Co doped Hf₂C and Zr₂C monolayers, highlighting their strong sensitivity to electric‐field‐induced perturbations and suggesting potential utility in orbitronic applications. These findings highlight the versatility of doped MXene monolayers, paving the way for their use in spintronic devices, detectors, and sensors.

This paper introduces the design of a high‐performance Surface Plasmon Resonance (SPR) sensor using a SnSe2/Si/PbTiO3 heterostructure for detecting malaria, targeting the different stages of the plasmodium parasite lifecycle. The Tin di‐selenide (SnSe2) with high refractive index (RI) and excellent absorption property in visible and infrared regions allows efficient interaction with the evanescent field, thereby increasing sensitivity for small RI changes near the surface. The strategic integration of lead titanate (PbTiO3), known for its high RI and tunable bandgap, with SnSe2 and Silicon (Si) layers, the proposed sensor design (FK51A‐prism/Ag/SnSe2/Si/PbTiO3/Sensing‐Medium) significantly improves sensitivity to 390.41°/RIU for the ring stage of malaria. The Kretschmann configuration, in conjunction with the Transfer Matrix Method (TMM) and angular interrogation, has been utilized to optimize the performance of the proposed SPR sensor. The proposed design achieves an optimal Quality Factor (QF) of 130.92 RIU⁻¹, enabling the detection of small changes in RI. With a Detection Accuracy (DA) of 0.33 deg⁻¹ for the ring stage, the proposed SPR sensor demonstrates its potential for early and accurate malaria diagnosis. Also, the enhanced DA and QF in later stages (trophozoite and schizont stages) offers broad detection range of the proposed SPR design. The design offers a promising application across different biomedical applications.

In the field of electronics, molecular technologies provide promising opportunities for innovators and scientists to advance technological progress. At the molecular scale, the simulation of ensembles becomes fundamental to advancing the fabrication, design, and prototyping of new technologies. This work proposes a framework leveraging the SCERPA tool and DFT calculation to efficiently evaluate the electronic properties of molecular ensembles. The Molecular Field‐Coupled Nanocomputing (MolFCN) is considered as a case study to validate ab initio‐comparable precision resulting from the SCERPA calculation on charge‐constrained multi‐molecule systems. In addition, it is demonstrated that the SCERPA results can be used as a nonrelativistic initial guess of DFT calculation, eventually reducing the ab initio computation time by 86 %. Finally, a periodic molecular FCN system is proposed, named SelfPolarizer. The proposed framework is employed to demonstrate that the ensemble naturally encodes QCA‐like digital information, providing the first simulated proof of concept for MolFCN technology obtained with DFT precision.

Sb2(S,Se)3 is a promising thin‐film solar absorber with a tunable bandgap (1.3–1.7 eV) and earth‐abundant composition, yet its maximum reported efficiency (10.75%) in FTO/CdS/Sb2(S,Se)3/Spiro‐OMeTAD/Au remains below the Shockley‐Queisser limit. Moreover, the high cost of Spiro‐OMeTAD as an HTL limits commercialization. Herein cost‐effective triazatruxene‐based HTLs (CI‐B2, CI‐B3, TAT‐H, TAT‐TY1, TAT‐TY2) are introduced for the first time in Sb2(S,Se)3 solar cells and optimize device performance using SCAPS‐1D. After replicating the experimental efficiency, optimization of HTL, ETL, and absorber parameters results in VOC (≈1 V), JSC >30 mA cm⁻²), and FF (72–74%). Overall, efficiencies of 22.97%, 23.09%, 22.47%, 21.08%, 23.24%, and 23.11% are achieved for Spiro‐OMeTAD, CI‐B2, CI‐B3, TAT‐H, TAT‐TY1, and TAT‐TY2, respectively, owing to the reduced VOC loss (≈0.4 V), enhanced QE (>70%), reduced recombination (by a factor of 3 × 10¹⁸ cm⁻³s⁻¹), and stronger electric fields, positioning triazatruxene‐based HTLs as a cost‐effective alternative to Spiro‐OMeTAD, significantly boosting Sb2(S,Se)3 solar cell performance.

Alzheimer's Disease (AD), a prevalent neurodegenerative disorder, is characterized by cognitive decline and neuronal death. Acetylcholinesterase (AChE) remains a primary therapeutic target, with donepezil as a widely used drug. However, its limited efficacy prompts the search for improved derivatives. This raises a key question: Can structural modifications to donepezil lead to analogues with improved therapeutic potential against AD? In this study, novel donepezil analogues are designed by replacing its indanone moiety with bi‐heterocyclic scaffolds to enhance binding affinity, pharmacokinetic properties, and anti‐AD activity. Molecular docking is used to identify compounds with favorable AChE interactions, followed by pharmacokinetic profiling for drug‐likeness and blood–brain barrier permeability. Molecular dynamics simulations further evaluate binding stability and free energy. Among the designed compounds, AS3 (indole‐based), AS4 (benzofuran‐based), and AS8 (coumarin‐based) showed enhanced AChE affinity and stable interactions compared to donepezil. AS4 exhibited the highest binding affinity, while AS8 demonstrated superior reactivity and chemical stability. Additionally, a new compound is designed by modifying both the indanone and piperidine moieties and introducing fluorine functionalization on the benzyl group. This compound demonstrated significantly improved binding affinity toward AChE, highlighting a promising new scaffold for further development.

Echinophthirius horridus, an ectoparasitic seal louse adapted for living on diving wildlife in the marine environment, exhibits unique cuticular morphology with dense body coverage of characteristically‐shaped setae. This study investigates their potential role in reducing drag during the host's diving activities. Scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) examines E. horridus setae morphology, revealing stair‐like elevations and gradual sclerotization increase from base to tip. Numerical simulations using movable cellular automata (MCA) demonstrate that optimal inclination of surface protrusions leads to vortex formation, potentially reducing friction and energy losses. Vertical protrusions cause stronger flow perturbations and higher energy dissipation compared to natural inclination. Over time, as flow self‐organizes, total power losses decrease, suggesting natural selection optimized surface structure inclination and spacing to minimize friction and energy losses. Comparisons with shark scales reveal morphological similarities but different drag reduction mechanisms, with seal louse setae utilizing a “ball‐bearing” effect and shark scales relying on a “riblet effect.” This study provides insights into surface topography's influence on fluid dynamics at small scales, with potential applications in understanding biological surfaces and designing reduced surface drag artificial surfaces.

Stress Corrosion Cracking (SCC) poses a significant threat to production systems, arising from the interaction of tensile stresses and corrosive environments. Sulfide Stress Cracking (SSC), particularly associated with hydrogen sulfide (H2S) gas, is highly relevant in oil and gas production. Corrosion‐resistant alloys, such as Duplex Stainless Steel (DSS), help mitigate this issue. However, understanding the impact of environmental conditions and loads on SSC in DSS remains challenging. Existing standards lack insights into specific environmental factors. Modeling SSC using physics‐based approaches is computationally intensive. To address this, a novel machine learning (ML) framework utilizing decision tree‐based models and probabilistic graphical models (Bayesian network, BN) is developed. The dataset for DSS is curated from published literature, and data imbalance is addressed using advanced data curation methods. The framework aims to unravel the intricate factors driving SSC in DSS, providing an accurate predictive tool for the oil and gas industry.

Radiative lifetime (RT) on the picosecond to femtosecond scale plays a key role in enabling ultrafast optoelectronic technologies. This study investigates the RT in InGaN/GaN quantum wells (QWs), focusing on the effects of well thickness, temperature, and indium (In) composition using the finite element method. The results show a significant dependence of RT on these parameters for both subband (ISB) and band‐to‐band (BTB) recombination. Specifically, radiative lifetime for ISB transitions extend to nanosecond timescales, with values reaching up to 6 ns, reflecting reduced wave function overlap and lower recombination probabilities at higher temperatures. In contrast, BTB recombination exhibits much faster dynamics in the fs range, with lifetimes as short as 10 fs, which is critical for high‐speed applications. This research highlights the importance of precisely controlling QWs parameters, as well as internal and external factors, to optimize device performance in emerging InGaN‐based ultrafast technologies.

The dynamics of relativistic fermions are studied in the presence of an out‐of‐plane magnetic field and a spinning point‐like defect, deriving exact solutions. These results show that the defect's spin (ϖ$\varpi$) breaks the symmetry between fermion and antifermion energy levels around zero energy. This symmetry breaking is influenced by the magnetic field strength (B∘$\mathcal {B}_{\circ }$) and the conical or anti‐conical geometry. Energy levels are further modified by the fractionalized spin s∼=s/α$\tilde{s} = s / \alpha$, where α$\alpha$ denotes the angular deficit or surplus, affecting conical or anti‐conical backgrounds. While fractionalized spin has no effect when s∼=−|s∼|$\tilde{s} = -|\tilde{s}|$, it significantly alters energy levels when s∼=+|s∼|$\tilde{s} = +|\tilde{s}|$. The defect's spin impacts fermion energy levels, leaving antifermion levels unchanged. For large ϖ∼=ϖ/α$\tilde{\varpi } = \varpi / \alpha$, the defect's spin dominates, minimizing internal quantum effects. In the case of ϖ=0$\varpi = 0$ and α=1$\alpha = 1$, Landau levels are recovered. These findings suggest the potential to fine‐tune charge carrier dynamics in magnetized monolayer materials with spinning defects or vortices.

Random networks of metal oxide nanowires are of great technological interest in a variety of applications. Unveiling how the properties of the elementary components of the network, namely, nanowires and nanowire‐nanowire junctions, may emerge as macroscopic functionalities is a fundamental issue. However, the disordered nature of the network and difficulties in estimating important properties, such as the junction contact radius, hinder the understanding and exploitation of these materials. This paper develops a model suitable for discussing the electrical resistance of the network and its transition from the junction‐dominated regime to the nanowire‐dominated regime, based on the nanowire characteristic lengths and energies. The result is achieved by integrating recent network theories with the equations of semiconducting metal oxides. Constraints imposed by the thermodynamics of contact mechanics are introduced to provide a lower limit for the unknown junction contact radius. Focusing on the literature of gas sensing, the model provides a theoretical background to frame the relationship between gas‐sensing properties and the surface termination of nanowires in the context of network theories.

Strontium aluminate doped with the europium(II) ion (SrAl2O4:Eu²⁺) is a versatile material with phosphor mechanoluminescent applications and static electricity‐induced luminescence properties. Oxygen vacancies play a crucial role in the mechanism governing the luminescence of SrAl2O4:Eu²⁺. The present study uses first‐principles calculations to estimate the energy level distribution of oxygen vacancies in europium‐doped strontium aluminate while considering the valence states of the luminescent Eu²⁺ and Eu³⁺ centers in the emission process. The results indicate that the energy levels of the oxygen vacancies are distributed ≈ 2.5 eV for Eu²⁺ and above 2.5 eV for Eu³⁺. Regardless of the charge states of the oxygen vacancies, their energy levels exhibit a relatively broad distribution owing to structural relaxation. The energy levels ε(0/1+) and ε(0/2+) of the oxygen vacancies shift toward the conduction band for Eu³⁺ relative to Eu²⁺. This finding is valuable for understanding the re‐excitation of electrons trapped in oxygen vacancies through external stimuli, such as thermal, mechanical, or electrical effects.

This study examines significant applications across various domains, including microfluidics, biomedical engineering, and energy systems, focusing on the advancement of lab‐on‐a‐chip technologies, electrokinetic pumps, and micro‐scale filtration systems. A detailed investigation is conducted to explore the combined influence of peristaltic transport, double‐diffusive convection, electroosmosis, magnetohydrodynamics (MHD), Hall current, viscous dissipation, thermal radiation, and porous medium flow in the presence of a heat source. The Poisson–Boltzmann ionic distribution is approximated using the Debye–Hückel method. The lubrication approach is adopted to make the system simpler. To solve nonlinear partial differential equations in a more sophisticated manner and to improve the reliability of models in various scientific and technical domains, this work uses the homotopy perturbation technique (HPM). A thorough investigation is conducted into the effects of key parameters on the flow characteristics using graphical representations. The results of this investigation show that, in the presence of double‐diffusive convection, the Helmholtz–Smoluchowski velocity parameter can improve the velocity distribution of the Ree‐Eyring nanofluid. Notable variations in trapped boluses are noticed due to the influence of some influential parameters. Furthermore, the present outcomes are validated with previous discoveries under specific conditions.

Hybrid nanofluids exhibit enhanced thermal properties compared to conventional nanofluids. Viscosity, critical for assessing heat transfer efficiency, influences pressure drop and pumping power. This study models hybrid nanofluid viscosity using Radial Basis Function (RBF), Multilayer Perceptron (MLP), and a Committee Machine Intelligent System (CMIS). A dataset of 584 viscosity data points is utilized. Particle Swarm Optimization (PSO) and Farmland Fertility Algorithm (FFA) are employed to train the RBF, while the MLP utilized Scaled Conjugate Gradient (SCG), Bayesian Regularization (BR), and Levenberg‐Marquardt (LM) algorithms. The CMIS is created by integrating MLP‐BR, RBF‐FFA, and RBF‐PSO networks. The AAPRE values for RBF‐PSO, RBF‐FFA, MLP‐LM, MLP‐SCG, MLP‐BR, and CMIS models are 1.7464, 1.6647, 2.6851, 2.1889, 2.1792, and 1.519, respectively. The R² values are 0.9689, 0.9394, 0.4794, 0.9727, 0.9404, and 0.9688, respectively, which indicates that the CMIS model with the lowest Average Absolute Percent Relative Error (AAPRE) and the highest Determination Coefficient (R²) value is the most accurate model and outperforms other models in estimating viscosity, demonstrating greater accuracy than empirical and theoretical models. Sensitivity analysis showed that temperature has a significant positive impact on viscosity, while nanoparticle size has a negative effect. The CMIS model is reliable for predicting nanofluid viscosity, exhibiting a broad application range and minimal outlier data.

The aim of this current analysis is to explore the properties of the hybrid nanoparticles subjected to non‐Newtonian fluid flow over a linearly stretched surface. For the improvement of thermal transport, Tewari and Das model is altered with modify Buongiorno's model. By imposing appropriate similarity transformations on (PDEs), nonlinear ordinary differential equations are achieved. Applying the current similarity synthesis, the PDE model is translated into ODEs and the modified equations are overcome by a well‐known shooting technique. The resulting set of nonlinear ordinary differential equations is eliminated mathematically by utilizing the Runge‐Kutta 4th order method in MATLAB software. The velocity profile goes down with the uplifting values of Hartmann number but it is clearly observed that the results of hybrid nanoparticle's is more effective than mono nanoparticles. To valid the given model, a comparison table is made with the data present in already published papers. Across a comprehensive range of magnetic field intensities, inverse Darcy numbers, and viscoelastic characteristics, the hybrid nanofluid exhibits a moderately enhanced skin friction factor, a slightly diminished heat transfer performance by the Nusselt number, and a marginally improved mass transfer efficiency by the Sherwood number. This work can find applications in the field of metal cooling, paper production etc.

In recent years, the incidence of plant diseases caused by bacterial, fungal, and environmental factors has steadily increased, affecting plants at various stages of agricultural production. Plant diseases not only reduce food production and quality but also pose significant social, health, and economic challenges. Mathematical modeling provides a method to analyze and predict the spread and impact of plant diseases. In this study, a mathematical model is suggested to describe the transmission dynamics of coffee berry disease (CBD). The model is extended in the sense of the Caputo derivative to improve accuracy and provide more realistic scenarios for coffee berry disease. A detailed analysis of the key mathematical properties of the model is presented. The conditions of local and global stability of the equilibrium points are analyzed. The existence and uniqueness of the model solution are presented, considering the fixed‐point theory. Parameter sensitivity is also examined. The numerical simulations are carried out to illustrate the practical application of fractional derivatives in the study of plant epidemiology.

This research utilizes Monte Carlo simulations with the Blume–Capel model to investigate the hysteresis behavior of graphene, graphyne, and graphdiyne nanostructures. By analyzing the effects of exchange interactions, crystal field, and temperature, the research explores the stability and reversibility of magnetization in these materials. The study finds that graphene exhibits the highest stability, with core–shell and core–core interactions enhancing stability, while stronger crystal fields and higher temperatures reduce coercivity and accelerate the paramagnetic transition, optimizing properties for nanotechnology.