Advanced Theory and Simulations最新文献

Polydopamine (PDA) is a mussel-inspired material with remarkable adhesive properties and a wide range of applications. There has been significant interest in understanding the structure of the PDA coating itself, as well as the PDA/substrate interaction. In this study, the adsorption of model monomers and oligomers on defect-free graphene is examined computationally. The adsorption of 5,6-dihydroxyindole (DHI) and its oxidized form, dopaminechrome (DAC), on perfect graphene is studied using the DFT formalism under periodic conditions. Non-covalent and covalent dimers adsorption is considered, taking into account different possible arrangements to establish the role of stacking configurations and aryl-aryl, pyrrole-pyrrole and aryl-pyrrole valence bonds. A theoretical extension is developed to compute the adsorption energy for covalent trimers. The results show that adsorbates predominantly composed of DAC monomers are preferred candidates for the growth of an oligomeric structure. The reaction energy required to produce covalent dimers is thermodynamically more feasible when a free monomer reacts with a monomer that has already been adsorbed than when two monomers react as free species. A particular analysis of the interaction forces between monomers or dimers and perfect graphene is performed to predict the rupture of the adsorbate/substrate system.

This article focuses on the study of solid tumours, particularly malignant ones, through the use of sophisticated mathematical model aimed at explaining their growth, which is primarily driven by cellular proliferation. Among these, gliomas, one of the most severe forms of brain cancer, are notable for their distinct behaviour, marked by deep infiltration into surrounding tissues. We primarily focus on studying a specific type of glioma known as glioblastoma multiforme using the brain tumour growth model governed by the Burgess equation. To achieve this, we implement a hybrid numerical scheme which is combination of polynomial differential quadrature method and best four-stage, third-order strong stability-preserving time-stepping Runge–Kutta (SSP-RK43) algorithm. The stability of the proposed method is also studied using matrix method. Moreover, the order of convergence of the proposed scheme is presented. The proposed scheme is simulated and tested on some test examples. We compare the results with the existing methods in terms of error. The computational time for each example is presented. The low computational cost demonstrates the efficiency of the proposed scheme.

In this work, we present an analytical solution of Schrödinger equation of confined pseudoharmonic potential in the presence of a moving boundary condition for an arbitrary angular momentum state. It turns out that an important quantity to probe the problem is the internuclear distance ratio, which depends on the solution of Ermakov equation. The minimum value of the time-dependent (TD) Heisenberg uncertainty product always remains greater than that of the minimum uncertainty product $��\hslash �/�2�$. The TD average energy is derived analytically in a closed form, and the corresponding average force and average pressure are defined. Moreover, the time correlation function of two states for the case of six selected diatomic molecules (CO, NO, ScH, CH, $�H_2$, $�N_2$) is obtained. It is found to depend on the internuclear distance ratio at two different time domains. The TD survival probability and average life-time of a molecule in a confined quantum system are defined. Expressions are offered for quantum similarity measure, dissimilarity, and quantum similarity index. The latter is given for a pair of molecules. The obtained results are compared with available literature, wherever possible. To our knowledge this is the first detailed report of a non-harmonic central potential in a TD moving boundary condition.

This work is done to understand the mechanistic details of the excited state non-adiabatic process of diazirine and three other derivatives that are substituted with electron-donating (methoxy-diazirine) and withdrawing (fluoro-diazirine) groups, as well as combining both (fluoro-methoxy-diazirine), to study their effect on generating carbene and molecular nitrogen via diazomethane. All electronic structure calculations are performed at SA2-CASSCF followed by MS-CASPT2 levels of theory, and non-adiabatic molecular dynamics simulations at SA2-CASSCF level by choosing appropriate active spaces. Two conical intersections (CIs) are found for each of the molecules in the non-adiabatic process, which are associated with asymmetric vibrations of the C─N bonds in the three-membered ring of the diazirines, except for pure diazirine, where the second CI is associated with symmetric vibration. The dynamics starts from n-π* excited state (S₁). In the dynamics, trajectories are seen to go through both the CIs whichever is energetically and dynamically permissible. The first-order kinetics of N₂ formation revealed that the reaction is fastest for pure diazirine and slowest for fluoro-methoxy-diazirine. The mechanism, as revealed from the present calculation, looks so reliable that an MS-CASPT2 dynamics, if performed, would perhaps change the population in each pathway, leading to different rate constants of N₂ formation, but the number of pathways and the trend of rate constants may remain the same.

This study explores the effects of a spatially varying magnetic field on the flow and heat transfer of a power-law non-Newtonian fluid past a stretchable porous wedge within a Darcy–Forchheimer porous medium. A mathematical framework is developed by extending the Navier–Stokes and energy equations, incorporating the Cauchy stress tensor for power-law fluids, Darcy's law, Forchheimer's inertial correction, and Lorentz force effects. The governing partial differential equations are transformed into a system of nonlinear ordinary differential equations via a local non-similar transformation, treating dimensionless parameters as streamwise independent. The resulting system is solved using a finite difference method (FDM) in MATLAB, generating 200 data points for velocity, temperature, skin friction, and Nusselt number. These results are further employed to train a supervised artificial intelligence model based on the Levenberg-Marquardt Scheme–Artificial Neural Network (LMS-ANN), with 70% of data used for training and 15% each for validation and testing. Comparative analysis demonstrates excellent agreement between FDM and LMS-ANN, achieving mean squared errors as low as 2.6 × 10⁻⁷ for skin friction and 6.6 × 10⁻⁸ for Nusselt number. The study confirms that Darcy–Forchheimer effects significantly enhance velocity and heat transfer, establishing the LMS-ANN framework as a reliable and efficient predictive tool.

Pseudomonas aeruginosa, a notorious gram-negative pathogen, exhibits extensive antibiotic resistance and is a prominent cause of nosocomial infections. Given the urgency of addressing this challenge, exploring therapeutic alternatives to mitigate virulence factors is imperative. This study utilizes in silico analysis to probe the interactions between synthetic autoinducers and pivotal proteins (LasR, QscR, RhlR, and PqsR) within P. aeruginosa Quorum Sensing network. Unveiling distinct binding modes and active site selectivity, it sheds light on protein-specific regulation design for tailored therapies. LasR and RhlR demonstrate stabilization through flexible complex structures, with LasR favoring π-ion and π–π stacking interactions, potentially linked to hydrophobic pocket modifications, while RhlR relies on hydrogen bonds for stability. In contrast, QscR and PqsR prefer long side chains and π-type interactions, altering the hydrophobic pocket without inducing flexibility, with minimal hydrogen bond formation. These insights underscore the potential for personalized therapeutic interventions targeting bacterial virulence factor.

The bandgap is a fundamental parameter that governs the electronic and optoelectronic properties of materials. However, accurately predicting this parameter remains a significant challenge due to the inherent trade-off between computational efficiency and model interpretability. This study introduces a bandgap correction framework based on genetic-programming symbolic regression. The approach integrates analytical formulations with physical priors and is trained on PBE and HSE06 bandgap data of quaternary chalcogenides computed within the framework of density functional theory. It provides an accurate linear correction to PBE bandgaps, aligning them closely with HSE06 predictions, while maintaining high computational efficiency and clear physical interpretability. Furthermore, the model also demonstrates strong generalization capabilities across multiple isostructural space groups. Beyond its predictive accuracy, the model uncovers an interpretable relationship between the bandgap and underlying electronic structure, thereby offering a transparent and generalizable strategy for band structure engineering in multicomponent semiconductors.