Search Results (753)

Barium titanate (BaTiO₃) has long been recognized as a promising photocatalyst for solar-driven water splitting due to its unique ferroelectric, piezoelectric, and electronic properties. This review provides a comprehensive analysis of atomistic simulation studies of BaTiO₃, highlighting the role of density functional theory (DFT), ab initio molecular dynamics (MD), and classical all-atom MD in exploring its photocatalytic behavior, in line with various experimental findings. DFT studies have offered valuable insights into the electronic structure, density of state, optical properties, bandgap engineering, and other features of BaTiO₃, while MD simulations have enabled dynamic understanding of water-splitting mechanisms at finite temperatures. Experimental studies demonstrate photocatalytic water decomposition and certain modifications, often accompanied by schematic diagrams illustrating the principles. This review discusses the impact of doping, surface modifications, and defect engineering on enhancing charge separation and reaction kinetics. Key findings from recent computational works are summarized, offering a deeper understanding of BaTiO₃’s photocatalytic activity. This study underscores the significance of advanced multiscale simulation techniques for optimizing BaTiO₃ for solar water splitting and provides perspectives on future research in developing high-performance photocatalytic materials. Full article

The synthesis of “green ammonia” from “green hydrogen” represents a critical pathway for renewable energy integration and industrial decarbonization. This study investigates the green ammonia synthesis process using an axial–radial fixed-bed reactor equipped with three catalyst layers. A simplified two-dimensional physical model was developed, and a multiscale simulation approach combining computational fluid dynamics (CFD) with physics-informed neural networks (PINNs) employed. The simulation results demonstrate that the majority of fluid flows axially through the catalyst beds, leading to significantly higher temperatures in the upper bed regions. The reactor exhibits excellent heat exchange performance, ensuring effective preheating of the feed gas. High-pressure zones are concentrated near the top and bottom gas outlets, while the ammonia mole fraction approaches 100% near the bottom outlet, confirming superior conversion efficiency. By integrating PINNs, the prediction accuracy was substantially improved, with flow field errors in the catalyst beds below 4.5% and ammonia concentration prediction accuracy above 97.2%. Key reaction kinetic parameters (pre-exponential factor k₀ and activation energy Ea) were successfully inverted with errors within 7%, while computational efficiency increased by 200 times compared to traditional CFD. The proposed CFD–PINN integrated framework provides a high-fidelity and computationally efficient simulation tool for green ammonia reactor design, particularly suitable for scenarios with fluctuating hydrogen supply. The reactor design reduces energy per unit ammonia and improves conversion efficiency. Its radial flow configuration enhances operational stability by damping feed fluctuations, thereby accelerating green hydrogen adoption. By reducing fossil fuel dependence, it promotes industrial decarbonization. Full article

As an efficient clean energy technology, water electrolysis for hydrogen production has its efficiency limited by the sluggish oxygen evolution reaction (OER) kinetics, which drives the demand for the development of high-performance anode OER catalysts. This work constructs bimetallic (Al, Mn) co-doped nanoporous spinel CoFe₂O₄ (np-CFO) with a tunable structure and composition as an OER catalyst through a simple two-step dealloying strategy. The as-formed np-CFO (Al and Mn) features a hierarchical flaky configuration; that is, there are a large number of fine nanosheets attached to the surface of a regular micron-sized flake, which not only increases the number of active sites but also enhances mass transport efficiency. Consequently, the optimized catalyst exhibits a low OER overpotential of only 320 mV at a current density of 10 mA cm⁻², a minimal Tafel slope of 45.09 mV dec⁻¹, and exceptional durability. Even under industrial conditions (6 M KOH, 60 °C), it only needs 1.83 V to achieve a current density of 500 mA cm⁻² and can maintain good stability for approximately 100 h at this high current density. Theoretical simulations indicate that Al and Mn co-doping could indeed optimize the electronic structure of CFO and thus decrease the energy barrier of OER to 1.35 eV. This work offers a practical approach towards synthesizing efficient and stable OER catalysts. Full article

The production of iron and steel plays a significant role in the anthropogenic carbon footprint, accounting for 7% of global GHG emissions. In the context of CO₂ mitigation, the steelmaking industry is looking to potentially replace traditional carbon-based ironmaking processes with hydrogen-based direct reduction of iron ore in shaft furnaces. Before industrialization, detailed modeling and parametric studies were needed to determine the proper operating parameters of this promising technology. The modeling approach selected here was to complement REDUCTOR, a detailed finite-volume model of the shaft furnace, which can simulate the gas and solid flows, heat transfers and reaction kinetics throughout the reactor, with an extension that describes the whole gas circuit of the direct reduction plant, including the top gas recycling set up and the fresh hydrogen production. Innovative strategies (such as the redirection of part of the bustle gas to a cooling inlet, the use of high nitrogen content in the gas, and the introduction of a hot solid burden) were investigated, and their effects on furnace operation (gas utilization degree and total energy consumption) were studied with a constant metallization target of 94%. It has also been demonstrated that complete metallization can be achieved at little expense. These strategies can improve the thermochemical state of the furnace and lead to different energy requirements. Full article

Vacuum degassing (VD) is a critical refining step in electric arc furnace (EAF) steelmaking for producing clean steel with reduced nitrogen and hydrogen content. This study develops an Effective Equilibrium Reaction Zone (EERZ) model focused on denitrogenation (de-N) by simulating interfacial reactions at the bubble–steel interface (Z1). The model incorporates key process parameters such as argon flow rate, vacuum pressure, and initial nitrogen and sulfur concentrations. A robust empirical correlation was established between de-N efficiency and the mass of Z1, reducing prediction time from a day to under a minute. Additionally, the model was further improved by incorporating a dynamic surface exposure zone (Z_eye) to account for transient ladle eye effects on nitrogen removal under deep vacuum (<10 torr), validated using synchronized plant trials and Python-based video analysis. The integrated approach—combining thermodynamic-kinetic modeling, plant validation, and image-based diagnostics—provides a robust framework for optimizing VD control and enhancing nitrogen removal control in EAF-based steelmaking. Full article

Developing and identifying the correct reactor model for a reaction system characterized by a high number of reaction pathways and flow regimes can be challenging. In this work, artificial neural networks (ANNs), used in deep learning, are used to develop a hybrid modelling framework for physics-based model discovery in reactions systems. The model discovery accuracy of the framework is investigated considering kinetic model parametric uncertainty, noise level, features in the data structure and experimental design optimization via a differential evolution algorithm (DEA). The hydrodynamic behaviours of both a continuously stirred tank reactor and a plug flow reactor and rival chemical kinetics models are combined to generate candidate physics-based models to describe a benzoic acid esterification synthesis in a rotating cylindrical reactor. ANNs are trained and validated from in silico data simulated by sampling the parameter space of the physics-based models. Results show that, when monitored using test data classification accuracy, ANN performance improved when the kinetic parameters uncertainty decreased. The performance improved further by increasing the number of features in the data set, optimizing the experimental design and decreasing the measurements error (low noise level). Full article

This study developed a two-dimensional multiphysics-coupled model for co-electrolysis of CO₂ and H₂O in solid oxide electrolysis cells (SOECs) using COMSOL Multiphysics, systematically investigating the influence mechanisms of key operating parameters including temperature, voltage, feed ratio, and flow configuration on co-electrolysis performance. The results demonstrate that increasing temperature significantly enhances CO₂ electrolysis, with the current density increasing over 12-fold when temperature rises from 923 K to 1423 K. However, the H₂O electrolysis reaction slows beyond 1173 K due to kinetic limitations, leading to reduced H₂ selectivity. Higher voltages simultaneously accelerate all electrochemical reactions, with CO and H₂ production at 1.5 V increasing by 15-fold and 13-fold, respectively, compared to 0.8 V, while the water–gas shift reaction rate rises to 6.59 mol/m³·s. Feed ratio experiments show that increasing CO₂ concentration boosts CO yield by 5.7 times but suppresses H₂ generation. Notably, counter-current operation optimizes reactant concentration distribution, increasing H₂ and CO production by 2.49% and 2.3%, respectively, compared to co-current mode, providing critical guidance for reactor design. This multiscale simulation reveals the complex coupling mechanisms in SOEC co-electrolysis, offering theoretical foundations for developing efficient carbon-neutral technologies. Full article

During atmospheric reentry, a significant number of chemical reactions are produced inside the high-temperature shock wave formed upstream of the spacecraft. Chemical reactions can significantly alter the flowfield structure surrounding the vehicle and affect surface properties, including heat transfer, pressure, and skin friction coefficients. In this scenario, the primary goal of this investigation is to evaluate the Quantum-Kinetic chemistry model for computing rarefied reactive gas flow over simple and complex geometries. The results are compared with well-established reaction models available for the transitional flow regime. The study focuses on two configurations, a sphere and the Orion capsule, analyzed at different altitudes to assess the impact of chemical nonequilibrium across varying flow rarefaction levels. Including chemical reactions led to lower post-shock temperatures, broader shock structures, and significant species dissociation in both geometries. These effects strongly influenced the surface heat flux, pressure, and temperature distributions. Comparison with results from the literature confirmed the validity of the implemented QK model and highlighted the importance of including chemical kinetics when simulating hypersonic flows in the upper atmosphere. Full article

The enzyme ABH2, one of nine human DNA dioxygenases of the AlkB family, belongs to the superfamily of Fe(II)/α-ketoglutarate-dependent dioxygenases and plays a crucial role in the direct reversal repair of nonbulky alkyl lesions in DNA nucleobases. ABH2 has broad substrate specificity, directly oxidizing DNA damages such as N¹-methyladenine, N³-methylcytosine, 1,N⁶-ethenoadenine, 3,N⁴-ethenocytosine, and a number of others. In our investigation, we sought to uncover the subtleties of the mechanisms governing substrate specificity in ABH2 by focusing on several critical amino acid residues situated in its active site. To gain insight into the function of this enzyme, we performed a functional mapping of its active site region, concentrating on pivotal residues, participating in forming a damaged binding pocket of the enzyme (Val99 and Ser125), as well as the residues directly involved in interactions with damaged bases, namely Arg110, Phe124, Arg172, and Glu175. To support our experimental data, we conducted a series of molecular dynamics simulations, exploring the interactions between the ABH2 mutant forms, bearing corresponding substitutions and DNA substrates, and harboring various types of methylated bases, specifically N¹-methyladenine or N³-methylcytosine. The comparative studies revealed compelling data indicating that alterations in most of the studied amino acid residues significantly influence both the binding affinity of the enzyme for DNA and its catalytic efficiency. Intriguingly, the findings suggest that the mutations impact the catalytic activity of ABH2 to a greater extent than its ability to associate with DNA strands. Collectively, these results show how changes to the active site affect molecular dynamics and reaction kinetics, improving our understanding of the substrate recognition process in this pivotal enzyme. Full article

Background/Objectives: The projected rise in limb amputations highlights the need for advancements in prosthetic technology. Current transtibial prosthetic designs primarily focus on sagittal plane kinematics but often neglect both the ankle kinematics and kinetics in the coronal plane, and the metatarsophalangeal joint, which play critical roles in gait stability and efficiency. This study aims to evaluate the combined effects of compliance in the coronal plane and a flexible toe joint on prosthetic gait using non-amputated participants as a model. Methods: We conducted gait trials on ten non-amputated individuals in the presence and absence of compliance in the coronal plane and toe compliance, using a previously developed three-degree-of-freedom (DOF) prosthetic foot with a prosthetic simulator. We recorded and analyzed sagittal and coronal kinematic data, ground reaction forces, and electromyographic signals from muscles involved in the control of gait. Results: The addition of compliance in the coronal plane and toe compliance had significant kinematic and muscular effects. Notably, this compliance combination reduced peak pelvis obliquity by 27%, preserved the swing stance/ratio, and decreased gluteus medius’ activation by 34% on the non-prosthetic side, compared to the laterally rigid version of the prosthesis without toe compliance. Conclusions: The results underscore the importance of integrating compliance in the coronal plane and toe compliance in prosthetic feet designs as they show potential in improving gait metrics related to mediolateral movements and balance, while also decreasing muscle activation. Still, these findings remain to be validated in people with transtibial amputations. Full article

Combustion processes in compression ignition engines lead to the inevitable generation of nitrogen oxides, which cannot be limited to the currently desired levels just by optimising the in-cylinder processes. Therefore, simulation-based engine development needs to include all engine-related aspects which contribute to tailpipe emissions. Among them, the SCR (selective catalytic reduction) aftertreatment-related processes, such as urea–water solution injection, urea decomposition, mixing, NOx catalytic reduction, and deposits’ formation, are the most challenging, and require as much attention as the processes taking place inside the cylinder. Over the last decade, the urea-SCR aftertreatment systems have evolved from underfloor designs to close-coupled (to the engine) architecture, characterised by the short mixing length. Therefore, they need to be tailor-made for each application. This study presents the CFD-based development of a multi-platform SCR system with a short mixing length for mobile non-road applications, compliant with Stage V NRE-v/c-5 emission standard. It combines multiphase dispersed flow, including wall wetting and urea decomposition kinetic reaction modelling to account for the critical aspects of the SCR system operation. The baseline system’s design was characterised by the severe deposit formation near the mixer’s outlet, which was attributed to the intensive cooling in the mounting area. Moreover, as the simulations suggested, the spray was not appropriately mixed with the surrounding gas in its primary zone. The proposed measures to reduce the wall film formation needed to account for the multi-platform application (ranging from 56 to 130 kW) and large-scale production capability. The performed simulations led to the system design, providing excellent UWS–exhaust gas mixing without a solid deposit formation. The developed system was designed to be manufactured and implemented in large-scale series production. Full article

This work deals with the catalytic steam reforming of raw syngas to increase the efficiency of coupling gasification with downstream processes (such as fuel cells and catalytic chemical syntheses) by producing high-temperature, ready-to-use syngas without cooling it for cleaning and conditioning. Such a combination is considered a key point for the future exploitation of syngas produced by steam gasification of biogenic solid fuel. The design and construction of an integrated gasification and gas conditioning system were proposed approximately 20 years ago; however, they still require further in-depth study for practical applications. A 3D model of the freeboard of a pilot-scale, fluidized bed gasification plant equipped with catalytic ceramic candles was used to investigate the optimal operating conditions for in situ syngas upgrading. The global kinetic parameters for methane and tar reforming reactions were determined experimentally. A fluidized bed gasification reactor (~5 kWth) equipped with a 45 cm long segment of a fully commercial filter candle in its freeboard was used for a series of tests at different temperatures. Using a computational fluid dynamics (CFD) description, the relevant parameters for apparent kinetic equations were obtained in the frame of a first-order reaction model to describe the steam reforming of key tar species. As a further step, a CFD model of the freeboard of a 100 kWth gasification plant, equipped with six catalytic ceramic candles, was developed in ANSYS FLUENT^®. The composition of the syngas input into the gasifier freeboard was obtained from experimental results based on the pilot-scale plant. Simulations showed tar catalytic conversions of 80% for toluene and 41% for naphthalene, still insufficient compared to the threshold limits required for operating solid oxide fuel cells (SOFCs). An overly low freeboard temperature level was identified as the bottleneck for enhancing gas catalytic conversions, so further simulations were performed by injecting an auxiliary stream of O₂/steam (50/50 wt.%) through a series of nozzles at different heights. The best simulation results were obtained when the O₂/steam stream was fed entirely at the bottom of the freeboard, achieving temperatures high enough to achieve a tar content below the safe operating conditions for SOFCs, with minimal loss of hydrogen content or LHV in the fuel gas. Full article

Combustion modeling using computational fluid dynamics (CFD) offers detailed insights into the flame structure and thermo-chemical processes. Furthermore, it has been extensively used in the past to optimize industrial furnaces. Despite the increasing computational power, the prediction of the reaction kinetics in flames is still related to high calculation times, which is a major drawback for large-scale combustion systems. To speed-up the simulation, artificial neural networks (ANNs) were applied in this study to calculate the chemical source terms in the flame instead of using a chemistry solver. Since one ANN may lack accuracy for the entire input feature space (temperature, species concentrations), the space is sub-divided into four regions/ANNs. The ANNs were tested for different fuel mixtures, degrees of turbulence, and air-fuel/oxy-fuel combustion. It was found that the shape of the flame and its position were well predicted in all cases with regard to the temperature and CO. However, at low temperature levels (<800 K), in some cases, the ANNs under-predicted the source terms. Additionally, in oxy-fuel combustion, the temperature was too high. Nevertheless, an overall high accuracy and a speed-up factor for all simulations of 12 was observed, which makes the approach suitable for large-scale furnaces. Full article

This work presents a one-dimensional (1D) model for simulating the behavior of an FCC riser reactor processing bio-oil. The FCC riser is modeled as a plug-flow reactor, where the bio-oil feed undergoes vaporization followed by catalytic cracking reactions. The bio-oil droplets are represented using a Lagrangian framework, which accounts for their movement and evaporation within the gas-solid flow field, enabling the assessment of droplet size impact on reactor performance. The cracking reactions are modeled using a four-lumped kinetic scheme, representing the conversion of bio-oil into gasoline, kerosene, gas, and coke. The resulting set of ordinary differential equations is solved using a stiff, second- to third-order solver. The simulation results are validated against experimental data from a full-scale FCC unit, demonstrating good agreement in terms of product yields. The findings indicate that heat exchange by radiation is negligible and that the Buchanan correlation best represents the heat transfer between the droplets and the catalyst particles/gas phase. Another significant observation is that droplet size, across a wide range, does not significantly affect conversion rates due to the bio-oil’s high vaporization heat. The proposed reduced-order model provides valuable insights into optimizing FCC riser reactors for bio-oil processing while avoiding the high computational costs of 3D CFD simulations. The model can be applied across multiple applications, provided the chemical reaction mechanism is known. Compared to full models such as CFD, this approach can reduce computational costs by thousands of computing hours. Full article

The objective of this study is to examine the variations in the properties of cementitious materials subjected to bending loads in conjunction with dry and wet cycles of sulfate exposure. This investigation involved applying continuous bending loads at 0%, 20%, and 40% of the ultimate bending capacity to cementitious material specimens. Furthermore, three sets of mortars and concretes with differing water–cement ratios were formulated and analyzed using X-ray diffraction, scanning electron microscopy, and compressive strength tests. The findings indicated that while the flexural strength, compressive strength, and porosity of the specimens initially increased, they ultimately declined as the cementitious materials degraded over time within the sulfate solution. Additionally, it was observed that an increase in bending load corresponded with a decrease in flexural strength, alongside a rise in the internal sulfate ion concentration. By integrating an enhanced form of Fick’s second law with chemical reaction kinetics, a transport model for sulfate ions in cement-based materials was developed under the coupling effect of bending load and sulfate exposure, utilizing Comsol Multiphysics. The simulation results, which align well with the experimental observations, exhibit an error of approximately 5% at a depth of 5 mm. Full article