ABSTRACT In this work, the unsteady heat and mass transport properties of a Casson fluid passing over an oscillating vertical cylinder embedded in a Darcy–Forchheimer porous medium are examined. The growing industrial application of oscillatory cylindrical systems in drilling operations, increased oil recovery, biochemical reactors, and polymer processing, where non‐Newtonian fluids interact with porous materials under periodic motion is the motivation behind this work. In order to effectively represent transport phenomena found in petroleum reservoirs, chemical mixing towers, food processing facilities, and heat exchange devices, this model integrates Soret and Dufour effects, viscous dissipation, chemical reaction, and heat generation/absorption. A dimensional partial differential system of equations is created by formulating the governing equations of momentum, energy, and concentration. The suitable transformations are then applied in the governing model to obtain the dimensionless form in terms of partial differential equations. To solve the equations numerically, a reliable and effective Crank‐Nicolson finite difference technique is implemented. The understanding of how to regulate heat and mass flow in porous geometries is made easier by this work. The effects of significant parameters on velocity, temperature, and concentration are investigated numerically and graphically.

ABSTRACT The accurate estimation of the State of Charge (SOC) for Lithium‐Ion Batteries (LIBs) remains a critical challenge for Battery Management Systems (BMS). Due to complex and dynamic charging/discharging conditions, the inherent fixed memory structure of conventional Long Short‐Term Memory (LSTM) networks fails to adequately bridge the discrepancies between historical data and real‐time measurements, constraining their adaptability to rapid operating scenarios. To address this, this paper proposes a novel adaptive LSTM network based on the Atangana–Goufo (AG) fractional‐order difference operator, named LSTM‐AG network. The key innovation lies in the integration of the AG operator, which develops non‐local and fractional‐order properties well‐suited for modeling complex dynamic systems with the LSTM framework. This integration establishes a fractional‐order memory gating mechanism that dynamically and self‐adaptively balances the contribution of long‐term historical information against current inputs, overcoming the memory rigidity of conventional LSTM. This adaptive capability effectively enhances the modeling flexibility and responsiveness to fluctuating operating conditions. Comprehensive experimental validations under various operating conditions demonstrate that the proposed LSTM‐AG network outperforms the standard LSTM network with significantly higher SOC estimation accuracy, stronger robustness, and better generalization ability.

ABSTRACT Biomineralization is a promising approach for producing composite materials that contain living organisms, biopolymers, and minerals. Brittle biomineralized materials can be potentially toughened by more deformable microorganisms and biopolymers. Microbially‐induced carbonate precipitation (MICP) is a common type of biomineralization that occurs when urease‐producing bacteria hydrolyze urea to form calcium carbonate (). MICP is often used in porous materials to improve their mechanical properties. The morphology of the precipitation is strongly influenced by the bacteria and the morphology and porosity of the host medium. To explore their effects on MICP, we constructed an agent‐based discrete element model to investigate clustering and morphology in tandem with the growth of bacterial cells and the secretion of extracellular polymers. The model parameters are validated and optimized via our experimental measurements. We further addressed parametric uncertainties and quantitatively evaluated the relative importance of enzyme secretion, catalytic kinetics, and diffusion dynamics parameters by employing Monte Carlo simulations combined with data‐driven variogram analysis. Based on this model, we also examine the influence of bacterial concentration, bacterial distribution, urea concentration, and the geometry of porous media on the morphology. Unlike prior field‐scale or particle‐based methods that primarily focus on structures, our model will pave the way toward the study of the kinetics of urease‐producing bacteria and the mechanically relevant microstructural features of MICP‐treated composite materials.

ABSTRACT Covalent organic frameworks (COFs) are promising materials for gas separation and carbon capture, however, the vast chemical and structural space of possible COFs makes conventional molecular simulation‐based screening of adsorption properties such as Henry coefficients computationally infeasible. Here, we systematically investigate the performance of several physically motivated descriptors, the sine matrix, Ewald sum matrix, smooth overlap of atomic positions (SOAP), many‐body tensor representation (MBTR), and a custom Lennard Jones‐based descriptor for predicting Henry coefficients of small gas molecules (both polar and nonpolar) in COFs using machine learning. To account for realistic variability, we construct datasets including both chemically functionalized frameworks and interlayer displaced stacking configurations, and train relatively simple models (random forests, neural networks, and gaussian process regression) on these data. By comparing predictive performance across descriptor–model combinations, we demonstrate how various physical representations capture the key factors governing gas adsorption, including electrostatics, local atomic environments, and van der Waals interactions. Our results highlight the critical role of descriptor choice and provide physically interpretable guidance for designing efficient machine learning models, providing a foundation for scalable high throughput computational screening of COFs for gas separation applications.

ABSTRACT Bromodomain‐containing protein 4 (BRD4) regulates transcription via its two homologous bromodomains, BRD4‐BD2 and BRD4‐BD1. The selective interaction mechanisms of three pyridinone inhibitors, Spd17, Spd21, and Spd33, with BRD4‐BD2 and BRD4‐BD1, were explored using multiple MD simulations, MM‐GBSA, and SIE. The energy results indicate that these pyridinone inhibitors show greater selectivity for BRD4‐BD1 compared to BRD4‐BD2, while non‐polar interactions, especially van der Waals interactions like CH‐π and π – π interactions are the key factors for interacting with BRD4‐BD1 and BRD4‐BD2. The energy differences of residues Val87/Val380, Leu92/Leu385, Asn140/Asn433, and Ile146/Val439 in BRD4‐BD1 and BRD4‐BD2 make contributions to their selectivity. The findings provide certain insights for the development of new BRD4 inhibitors.

ABSTRACT Perovskite oxides doped with transition metals play a critical role in several contemporary applications, such as electro‐ and photocatalysis and the synthesis of coke‐ and sintering‐resistant catalysts. In this study, we present a systematic investigation of the doping preferences and surface segregation trends of 4d transition metals in (A = Ca, Sr, Ba) perovskites using first‐principles density functional theory (DFT) calculations. We further consider the influence of different facets and terminations such as (001)‐AO/, (110)‐ABO/, and (111)‐/B, and the application of tensile and compressive strain on the segregation behavior of these dopants. Results indicate that doping and segregation behaviors depend strongly on the exposed facet and applied strain, but less so on the host perovskite oxide. Additionally, we find that early transition metals strongly prefer bulk and subsurface doping, while later metals, beginning with Ru, segregate to the surface. We rationalize and corroborate the computed trends against a comprehensive set of previously published experimental data, highlighting the critical role of facet, termination, and strain in determining doping and segregation behavior. The results serve as a suitable starting point for designing perovskite systems with tailored properties.

ABSTRACT The weak interaction between pristine 2D arsenic‐phosphorus (AsP) monolayers and nitrogen oxides (NO, NO 2 ) hinders their gas‐sensing utility. Density functional theory calculations investigate the enhanced adsorption mechanisms of boron‐doped AsP. Boron preferentially substitutes arsenic, forming a stable configuration (formation energy: −2.37 eV) that converts weak physisorption to strong chemisorption. Adsorption energies and residence times of NO/NO 2 are significantly increased, with Bader charge analysis showing a 2.3–2.9‐fold charge transfer enhancement. Electronic structure analysis reveals pronounced bandgap narrowing and orbital hybridization, confirmed by charge density difference plots. The climbing‐image nudged elastic band method and molecular dynamics simulations verify adsorbed species stability. Boron‐doped AsP exhibits superior NO 2 selectivity over interferents (adsorption energy difference >1.27 eV). This work elucidates a synergistic doping mechanism, laying a theoretical foundation for high‐performance 2D gas sensors.

ABSTRACT A carbon nanotube (CNT) interconnected network model is proposed to evaluate the electrical properties of polymer nanocomposites through a resistor‐inspired approach. The model is able to control morphological characteristics of CNTs such as length, degree of orientation, and waviness, and then analyze their impact on the effective electrical conductivity of polymer nanocomposites. An algorithm based on stochastic elements to generate, identify, and analyze interconnected CNTs networks by assigning equivalent electrical resistances is developed. For each case, electrical resistance values were assigned to the CNT network considering CNT‐CNT contact, electron tunneling, and intrinsic conduction mechanisms. The model was able to identify and correlate the impact of each type of CNT‐CNT interactions (direct contact/tunneling) on the global electrical properties, confirming the tunneling mechanism as the main driver at the electrical percolation stage. Longer, straighter, and moderately aligned CNTs generate nanocomposites with more electrical pathways. The model is capable to adequately predict experimental reports, evidencing its relevance for the optimization of CNT‐based nanocomposites for technological applications.

ABSTRACT Recently asymmetric squaraine dyes have drawn considerable interest for dye‐sensitized solar cells (DSSCs) owing to their strong absorption spanning the visible to near‐infrared region, remarkable molar extinction coefficients, and versatile structural. However, some squaraine dyes still suffer from limitations, such as narrow spectral coverage and insufficient intramolecular charge transfer (ICT), which adversely affect the power conversion efficiency (PCE) of the dyes. Here, two new molecules, AK4‐S and AK4‐DTS, are constructed by introducing conjugated bridges between the D2 moiety and the anchoring group of the reference dye AK4. Employing density functional theory (DFT) and time‐dependent density functional theory (TD‐DFT), the computational study revealed that incorporating a π‐conjugated spacer can reduce the energy gap (Eg), broaden the absorption spectrum, enhance the oscillator strength (f), suppress charge recombination, improve thermal stability, and significantly promote the ICT process, thereby facilitating electron injection into the TiO 2 substrate. Among them, AK4‐DTS demonstrated the best performance, achieving a theoretical PCE of 9.946%, which is markedly higher than that of the original AK4 (9.050%). These findings demonstrate that incorporating a π‐conjugated bridge serves as a viable approach to improve the PCE of squaraine‐based dyes, thereby providing key guidance for the development of high‐performance sensitizers in DSSCs.