Multiscale Simulation Research Articles

Hybrid multiscale computational approach for heat transfer in GaN semiconductors

Abstract With ongoing advancements in semiconductor technology, understanding thermal transport in semiconductor devices—where phonons are the primary charge carriers—is crucial. This involves a multiscale process in which phenomena at different scales are governed by distinct control equations, necessitating various numerical methods. In this context, this paper introduces a novel multiscale simulation method called the Second-Order Reconstruction Operator Finite Difference Lattice Boltzmann Method (ROsFDLBM). This method effectively combines the Lattice Boltzmann Method (LBM) with the Finite Difference Method (FDM). ROsFDLBM employs reconstruction operators to accurately map the relationship between distribution functions and macroscopic physical quantities, significantly reducing errors caused by the transmission of interface information. The efficacy of this coupling scheme has been confirmed through an interface-coupled thermal diffusion model. Comparative analyses have revealed that the relative error of ROsFDLBM does not exceed 1.2%, demonstrating its superior accuracy. Further simulations of GaN devices have confirmed that traditional macroscopic methods tend to underestimate the temperature in the heat source area at micro-nano scales, with discrepancies reaching up to 60.9 K. Overall, the ROsFDLBM aligns with current research findings and offers an accurate and efficient simulation strategy for addressing complex heat transfer problems.

Multiscale simulation of liquid chromatography: Effective diffusion in macro–mesoporous beds and the B-term of the plate height equation

We performed multiscale simulations of analyte sorption and diffusion in hierarchical porosity models of monolithic silica columns for reversed-phase liquid chromatography to investigate how the mean mesopore size of the chromatographic bed and the analyte-specific interaction with the chromatographic interface influence the analyte diffusivity at various length scales. The reproduced experimental conditions comprised the retention of six analyte compounds of low to moderate solute polarity on a silica-based, endcapped, C18 stationary phase with water‒acetonitrile and water–methanol mobile phases whose elution strength was varied via the volumetric solvent ratio. Detailed information about the analyte-specific interfacial dynamics received from molecular dynamics simulations was incorporated through appropriate linker schemes into Brownian dynamics diffusion simulations in three hierarchical porosity models received from physical reconstructions of silica monoliths with a mean macropore size of 1.23 µm and mean mesopore sizes of 12.3, 21.3, or 25.7 nm. The mean mesopore size was found to have a similar influence on the effective mesopore diffusivity as the analyte polarity and the mobile-phase elution strength, which together determine the analyte residence time on a column. A smaller mesopore size attenuated the increase of the effective mesopore diffusivity with increasing mobile-phase elution strength significantly. The effective bed diffusivity was limited by the analyte residence time rather than by morphological details of the mesopore space. The stronger an analyte was retained by the chromatographic interface inside the mesopores, the slower became the mass transfer between the pore space hierarchies and the lower was the effective bed diffusivity. The B-terms of the plate height equation were finally generated with the bed diffusivities and phase-based retention factors derived from the hierarchical porosity models using additional information about the stationary-phase limit obtained from the analysis of analyte–bonded phase contacts. The B-terms highlight analyte- and mobile phase-specific behavior relevant to isocratic and gradient elution conditions in chromatographic practice. In particular, U-shaped B-term curves are observed due to the dominating contribution of the retention factor and the bed diffusivity to the B-term at low and high elution strength of the mobile phase, respectively.

A multi-scale method for the study of deep drying of ethylene with 3A zeolite

Coal-based ethanol to ethylene (ETE) is an emerging production pathway with the advantages of a short construction period, high product purity, and few by-products. N2 is commonly used in industry to regenerate zeolites during the ETE process, with the problems of excessive material consumption, energy consumption and environmental pollution. To solve this problem, this paper proposed a method of using the ethylene product as regeneration gas to regenerate the zeolite and recover the desorbed gas. In this work, we adopted a multi-scale simulation method combining microscale apparent diffusivity and macroscale mass transfer models. Reactive force field and molecular dynamics (ReaxFF-MD) were used to obtain an equation for the diffusion coefficient versus temperature. The start-up of the twin-tower temperature and pressure swing adsorption (TPSA) process was simulated using Computational Fluid Dynamics (CFD). The TPSA process achieved an outlet water content below 5 ppm, fulfilling the process specifications.

Design principles for selective nitrogen adsorption via chelation of metals in metal organic frameworks

Designing porous materials with N2 selectivity performance is highly attractive for the efficient removing N2 from natural gas. However, selectively capturing N2 over CH4 is challenging due to their similar physical properties. In this work, we proposed achieving N2 selectivity adsorption over CH4 by chelating Fe2+ sites on the ligands of MOFs. The significant enhancement of N2/CH4 separation by Fe chelation was demonstrated using multiscale simulations, including quantum mechanics calculations, force field development, and GCMC simulations. Specifically, Fe chelated MOFs can achieve a record-high N2 adsorption capacity while maintaining N2/CH4 selectivity comparable to that of current benchmark N2 selective capture MOFs. Moreover, based on simulation data from 331 Fe chelated MOFs with various topologies, we quantified the structure-properties relationships for N2/CH4 separation using Fe chelated MOFs through interpretable machine learning, which provides precise and intuitive guidance for designing novel adsorbents for selective N2 adsorption.

Multi-scale three-dimensional simulation of the solidification microstructure evolution in laser welding of aluminum alloys under dynamic spatial thermal cycling

Laser welding process involves three-dimensional (3D) highly dynamic spatial thermal cycling that results in intricate microstructure evolution in different zones. Understanding of the 3D topological evolution of microstructure under spatial thermal cycling is crucial for effective control in solidification processes. In this paper, a 3D multiphase-field model combined with accelerated methods and multi-scale coupling algorithms was developed for high-precision prediction of the dynamic microstructure evolution in laser welding. The heat flow distribution during laser welding varied significantly, with the cooling rates of 1.7–1.9 × 104 K/s at the middle region and 7.5–9.0 × 104 K/s at the bottom region. Considering the effects of 3D heat flow, a large angle may appear between the grains' primary growth direction and the cross-sectional plane, thereby the ultimate morphology through 2D analysis may lead to distortion from reality. The simulation of 3D microstructure evolution during distinct regions results showed that the grains at the bottom region exhibited larger characteristic dimensions (Length/Height >2.5, Length/Width >2.0) and columnar shape, while the middle region tended to form equiaxed structures. The grain size statistics revealed that the grains in the middle region exhibited larger scale due to their smaller GR (G: temperature gradient, R: growth rate) values. The simulation results were in good agreement with the electro-back-scattered diffraction testing results and the theoretical analysis.

A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling

In this study, we systematically investigate an inverse method for heterogeneous porous media to obtain porosity and permeability fields considering only production well data. The forward modeling of the input data, namely pressure and saturation fields, is based on the motion equations of a coupled Darcy flow system involving two phases of isothermal fluid flow. We discretize these equations using a multiscale finite volume simulation technique in the spatial domain, and the backward Euler method in time domain. In the inversion procedure, we combine a global optimization method, the Markov Chain Monte Carlo (MCMC) method, with a local optimization method, the Levenberg–Marquardt (LM) method. The MCMC was implemented as the Random Walk algorithm, and to generate the samples of the porosity and permeability fields, we employed Karhunen–Loève (KL) expansion of second-order stationary fields with Gaussian covariance. The coefficients of the KL expansion are estimated by minimizing the norm of the residual dependent on these fields. At the end of the MCMC iterations, we refine the KL coefficients associated with the porosity and permeability fields using the LM method. We verify in the numerical experiments that the accuracy of the porosity and permeability fields is improved by the LM refinement step.

Multiscale Analysis of Impact-Resistance in Self-Healing Poly(ethylene-co-methacrylic acid) (EMAA) Plain Woven Composites.

A study combining multiscale numerical simulation and low-velocity impact (LVI) experiments was performed to explore the comprehensive effects on the impact-resistance of EMAA filaments incorporated as thermoplastic healing agents into a plain woven composite. A multiscale micro-meso-macro modeling framework was established, sequentially propagating mechanical performance parameters among micro-meso-macro models. The equivalent mechanical parameters of the carbon fiber bundles were predicted based on the microscopic model. The mesoscopic representative volume element (RVE) model was crafted by extracting the actual architecture of the monolayer EMAA filaments encompassing the plain woven composite. Subsequently, the fiber and matrix of the mesoscopic model were transformed into a monolayer-equivalent cross-panel model containing monolayers aligned at 0° and 90° by local homogenization, which was extended into a macroscopic equivalent model to study the impact-resistance behavior. The predicted force-time curves, energy-time curves, and damage profile align closely with experimental measurements, confirming the reliability of the proposed multiscale modeling approach. The multiscale analysis reveals that the EMAA stitching network can effectively improve the impact-resistance of plain woven composite laminates. Furthermore, there exist positive correlations between EMAA content and both impact-resistance and self-healing efficiency, achieving a self-healing efficiency of up to 98.28%.

Integrating strategy to investigate the formation and stabilization mechanisms of Curcumin-Cyclodextrin inclusion complexes: experimental characterizations and multi-scale computational simulations.

The Cyclodextrin (CD) encapsulation technology can significantly enhance the water solubility of Curcumin (CUR) and effectively protect it against chemical degradations. In the current study, a combination of experimental and multi-scale computational approaches was used to investigate the binding mechanism between CUR and β-CDs (β-CD, 2-HP-β-CD, Me-β-CD, and DM-β-CD). Phase solubility studies showed that β-CDs exhibited a strong binding affinity with CUR and significantly enhanced its solubility. SEM, PXRD, DSC, and TG analysis confirmed the formation of inclusion complexes, resulting in the transition of CUR from crystalline to an amorphous state. FTIR, 1H, and 13CNMR spectra deduced that CUR may have multiple binding conformations with β-CDs. The rationality of molecular dynamics simulation systems was confirmed by comparing the simulated IR spectrum from quantum mechanics with the experimental IR spectrum. MM-PBSA and umbrella sampling simulation calculated the binding free energy of the CUR/CD inclusion complexes, ranking Me-β-CD > DM-β-CD > 2-HP-β-CD > β-CD, and clarified that van der Waals interaction played a major role in stabilizing the inclusion complexes. RDF analysis confirmed that the release of high-energy water molecules drove the formation of the CUR/CD inclusion complex, and the β-CD substituents affected their exterior hydration layer. Additionally, principal component analysis (PCA) and non-covalent interaction analysis revealed three CUR/CD binding modes: bead-on-string, terminal insertion, and V-shaped-folding. The research strategy adopted here can serve as a paradigm for investigating the encapsulation mechanism of poorly soluble drugs with CDs.

Multi-scale collaborative modeling and deep learning-based thermal prediction for air-cooled data centers: An innovative insight for thermal management

Investigating the data center (DC) thermal environment and temperature distribution is crucial to responding to unforeseen events such as equipment failure or environmental changes. However, building full-scale simulation models from DC room level to chip level faces significant challenges. In this paper, we propose a distinctive approach that combines multi-scale collaborative modeling with deep learning techniques for thermal prediction in air-cooled DCs. By taking the simulation results of the parent model as the boundary conditions of the child model, we constructed the DC multi-scale simulation model, which significantly reduces the model complexity and computational resources. Leveraging experimental data, the models at different scales were validated separately. The effects of different cooling strategies, air supply temperatures and air supply flow rates on multi-scale simulation models were investigated. Based on the parametric simulation approach, datasets for training data-driven models are constructed. Simultaneously, we propose the CNN-BiLSTM-Attention neural network model to predict the maximum CPU temperature and optimize the hyperparameters of the neural network through by Bayesian optimization. The prediction results of the coupled multi-scale model and the deep learning prediction model show that the absolute error is controlled within ±0.1 K, and the R2 value of the model evaluation metric is as high as 0.9899. Herein, the results provide valuable insights for enhancing thermal management in air-cooled DCs, paving the way for more efficient and resilient DC operations in the future.

A Multi-Scale Numerical Simulation Method Considering Anisotropic Relative Permeability

Most of the oil reservoirs in China are fluvial deposits with firm reservoir heterogeneity, where differences in fluid flow capacity in individual directions should not be ignored; however, the available commercial reservoir simulation software cannot consider the anisotropy of the relative permeability. To handle this challenge, this paper takes full advantage of the parallelism of the multi-scale finite volume (MsFV) method and establishes a multi-scale numerical simulation approach that incorporates the effects of reservoir anisotropy. The methodology is initiated by constructing an oil–water black-oil model considering the anisotropic relative permeability. Subsequently, the base model undergoes decoupling through a sequential solution, formulating the pressure and transport equations. Following this, a multi-scale grid system is configured, within which the pressure and transport equations are progressively developed in the fine-scale grid domain. Ultimately, the improved multi-scale finite volume (IMsFV) method is applied to mitigate low-frequency error in the coarse-scale grid, thereby enhancing computational efficiency. This paper introduces two primary innovations. The first is the development of a multi-scale solution method for the pressure equation incorporating anisotropic relative permeability. Validated using the Egg model, a comparative analysis with traditional numerical simulations demonstrates a significant improvement in computational speed without sacrificing accuracy. The second innovation involves applying the multi-scale framework to investigate the impact of anisotropy relative permeability on waterflooding performance, uncovering distinct mechanisms by which absolute and relative permeability anisotropy influence waterflooding outcomes. Therefore, the IMsFV method can be used as an effective tool for high-resolution simulation and precise residual oil prediction in anisotropic reservoirs.

Advanced multi-scale mechanobiological simulations enable the distinction between healthy and pathological bone growth patterns

Advanced multi-scale mechanobiological simulations enable the distinction between healthy and pathological bone growth patterns

Sensitivity analysis of a multi-scale biofuel primary atomization simulation tool

The purpose is to produce a benchmarked commercial Volume of Fluid (VOF)-to-Discrete Phase Modeling (DPM) numerical algorithm using an experimental study of high-viscosity jet fuel blend C-3. Where the mesh resolution was too coarse to resolve sufficient gas-liquid interfacial curvature, the solver automatically converted VOF biofuel phase mass, momentum, and energy into the equivalent DPM biofuel phase at the sub-grid scale level. Particular attention herein was given to the sensitivity of the results to various numerical degrees of freedom related to the local criteria for conversion of a biofuel from VOF to DPM. The proliferation of commercial simulation tools mandates detailed parametric evaluations. It was found that, while instantaneous conversion rates were extremely sensitive to conversion criteria, atomization physics were not sensitive to the conversion rate effects. Also, this flow system exhibited an outstanding dependence on simulated azimuthal angle, rendering lower order models ineffective. Data sampling methods and durations, along with mesh resolution, were explored such that a particular set of parameters provided a validated model.

A multiscale model to understand the interface chemistry, contacts, and dynamics during lithium stripping

A reversible Li-metal electrode, paired with a solid electrolyte, is critical for attaining higher energy density and safer batteries beyond the current lithium-ion cells. A stable stripping process may be even harder to attain as the stripping process will remove Li-atoms from the surface, and naturally reduce surface contact area, if not self-corrected by other mechanisms, such as diffusion and plastic deformation under an applied external stack pressure. Here, we capture these mechanisms occurring at multiple length- and time- scales, i.e., interface interactions, vacancy hopping, and plastic deformation, by integrating density functional theory (DFT) simulations, kinetic Monte Carlo (KMC), and continuum finite element method (FEM). By assuming the self-affine nature of multiscale contacts, we predict the steady-state contact area as a function of stripping current density, interface wettability, and stack pressure. We further estimate the exponential increase of overpotential due to contact area loss to maintain the same stripping current density. We demonstrate that a lithiophilic interface requires less stack pressure to reach the same steady-state contact area fraction than a lithiophobic interface. A “tolerable steady-state” contact area loss for maintaining stable stripping is estimated at 20 %, corresponding to a 10 % increase in overpotential. To constrain contact loss within the tolerance, the required stack pressure is 0.1, 0.5, and 2 times the yield strength of lithium metal for three distinct interfaces, lithiophilic Li/lithium oxide(Li2O), Li/lithium lanthanum zirconium oxide(LLZO), and lithiophoblic Li/lithium fluoride(LiF), respectively. The modeling results agree with experiments on the impact of the stack pressure quantitatively, while the discrepancy in stripping rate sensitivity is attributed to the simplifying interface interaction in our simulations. Overall, this multiscale simulation framework demonstrates the importance of electrochemical-mechanical coupling in understanding the dynamics of the Li/SE interface during stripping.

TOPAS-Tissue: A Framework for the Simulation of the Biological Response to Ionizing Radiation at the Multi-Cellular Level.

This work aims to develop and validate a framework for the multiscale simulation of the biological response to ionizing radiation in a population of cells forming a tissue. We present TOPAS-Tissue, a framework to allow coupling two Monte Carlo (MC) codes: TOPAS with the TOPAS-nBio extension, capable of handling the track-structure simulation and subsequent chemistry, and CompuCell3D, an agent-based model simulator for biological and environmental behavior of a population of cells. We verified the implementation by simulating the experimental conditions for a clonogenic survival assay of a 2-D PC-3 cell culture model (10 cells in 10,000 µm2) irradiated by MV X-rays at several absorbed dose values from 0-8 Gy. The simulation considered cell growth and division, irradiation, DSB induction, DNA repair, and cellular response. The survival was obtained by counting the number of colonies, defined as a surviving primary (or seeded) cell with progeny, at 2.7 simulated days after irradiation. DNA repair was simulated with an MC implementation of the two-lesion kinetic model and the cell response with a p53 protein-pulse model. The simulated survival curve followed the theoretical linear-quadratic response with dose. The fitted coefficients α = 0.280 ± 0.025/Gy and β = 0.042 ± 0.006/Gy2 agreed with published experimental data within two standard deviations. TOPAS-Tissue extends previous works by simulating in an end-to-end way the effects of radiation in a cell population, from irradiation and DNA damage leading to the cell fate. In conclusion, TOPAS-Tissue offers an extensible all-in-one simulation framework that successfully couples Compucell3D and TOPAS for multiscale simulation of the biological response to radiation.

A Small Molecule Impedes the Aβ1-42 Tetramer Neurotoxicity by Preserving Membrane Integrity: Microsecond Multiscale Simulations.

Amyloid-β (Aβ1-42) peptides aggregated into plaques deposited in the brain are the main hallmark of Alzheimer's disease (AD), a social and economic burden worldwide. In this context, insoluble Aβ1-42 fibrils are the main components of plaques. The recent trials that used approved AD drugs show that they can remove the fibrils from AD patients' brains, but they did not halt the course of the disease. Mounting evidence envisages that the soluble Aβ1-42 oligomers' interactions with the neuronal membrane trigger higher cell death than Aβ1-42 fibril interactions. Developing a compound that can alleviate the oligomer's toxicity is one of the most demanding tasks for curing the disease. We performed two molecular dynamics (MD) simulations in an explicit solvent model. In the first case, 55-μs of multiscale all-atom (AA)/coarse-grained (CG) MD simulations were carried out to decipher the impact of a previously described small anti-Aβ molecule, termed M30 (2-octahydroisoquinolin-2(1H)-ylethanamine), on an Aβ1-42 tetramer structure in close contact with a DMPC bilayer. In the second case, 15-μs AA/CG MD simulations were performed to rationalize the dynamics between Aβ1-42 and Aβ1-42-M30 tetramer complexes embedded in DMPC. On the membrane bilayer, we found that the Aβ1-42 tetramer penetrates the bilayer surface due to unrestricted conformational flexibility and many contacts with the membrane phosphate groups. In contrast, no Aβ1-42-M30 tetramer penetration was observed during the entire course of the simulation. In the case of the membrane-embedded Aβ1-42 tetramer, the integrity of the bottom bilayer leaflet was severely affected by the interactions between the negatively charged phosphate groups and the positively charged residues of the Aβ1-42 tetramer, resulting in a deep tetramer penetration into the bilayer hydrophobic region. These contacts were not observed in the case of the membrane-embedded Aβ1-42-M30 tetramer. It was noted that M30 molecules bind to Aβ1-42 tetramer through hydrogen bonds, resulting in a conformational stable Aβ1-42-M30 complex. The associated complex has reduced conformational changes and an enhanced rigidity that prevents the tetramer dissociation by interfering with the tetramer-membrane contacts. Our findings suggest that the M30 molecules could bind to Aβ1-42 tetramer resulting in a rigid structure, and that such complexes do not significantly perturb the membrane bilayer organization. These observations support the in vitro and in vivo experimental evidence that the M30 molecules prevent synaptotocity, improving AD-affected mice memory.

A Lennard-Jones potential based cohesive zone model and its application in multiscale damage simulation of graphene reinforced nanocomposites

A new mixed-mode cohesive zone model based on Lennard-Jones potential (LJCZM) is proposed to simulate the interface failure between graphene and epoxy matrix. The values of model parameters are obtained from a large number of molecular dynamics simulations, and a UMAT subroutine is programmed and validated to introduce this model into the ABAQUS platform. This process spans from the nanoscale to the microscale, which provides a new routine for the multiscale damage modeling of the graphene reinforced epoxy nanocomposite at microscale. In addition, the continuous damage phase-field model is used to simulate the matrix damage, and the values of model parameters are determined from the molecular dynamic simulations of the bulk epoxy at nanoscale. At last, the effects of parameters such as volume fraction, aspect ratio, orientation, and curvature of graphene nanoplatelets are investigated. The results indicate that the nanocomposite reinforced with high content and large aspect ratio graphene nanoplatelets presents the lower ultimate stress and fracture strain. In addition, the orientation and waviness of the graphene also significantly affect the mechanical properties of the nanocomposites. The nanocomposite reinforced with graphene platelets with greater waviness has higher stiffness and strength but lower toughness. The rationality and effectiveness of the model are verified through comparison with other existing results.

Microscopic mechanism investigation of extraction of lignin by benzyltriethylammonium chloride-based deep eutectic solvents

Benzyltriethylammonium chloride (BTEAC)-based deep eutectic solvent (DES) has a high delignification rate in the pretreatment of lignocellulose, and the lignin obtained from the removal has the advantages of high purity and low molecular weight. The interaction mechanism between BTEAC-based DES and lignin at molecular level was investigated by multiscale simulation analytical approach. By analyzing the types of weak interaction, charge distribution, number of hydrogen bonds, energy distribution and density distribution in different systems, the influence of six hydrogen bond donor (HBD) structures on the extraction process was clarified. The interaction between Cl- and lignin is mainly electrostatic interaction, and the interaction between BTEA+ and lignin is mainly van der Waals (vdW) interaction. The original π-π stacking of lignin is replaced by the new π-π stacking between BTEA+ and lignin. The effects of different functional groups and length of carbon chain on the interaction were researched. The acidic HBD with short carbon chain has a larger interaction and can affect the ether bond on the lignin. Among them, due to the smaller volume of formic acid (FA), it provides more space for BTEA+ to contact with lignin, resulting in stronger interaction. The molecular-level theoretical foundation underpinning the efficacy of BTEAC-FA in lignin removal during the pretreatment process is provided.

Simulation-Directed Construction of Bamboo-Forest-Like Heat Conduction Networks to Enhance Silicon Rubber Composites' Heat Conduction Properties.

Highly vertically thermally conductive silicon rubber (SiR) composites are widely used as thermal interface materials (TIMs) for chip cooling. Herein, inspired by water transport and transpiration of Moso bamboo-forests extensively existing in south China, and guided by filler self-assembly simulation, bamboo-forest-like heat conduction networks, with bamboo-stems-like vertically aligned polydopamine-coated carbon fibers (VA-PCFs), and bamboo-leaves-like horizontally layered Al2O3(HL-Al2O3), are rationally designed and constructed. VA-PCF/HL-Al2O3/SiR composites demonstrated enhanced heat conduction properties, and their through-plane thermal conductivity and thermal diffusivity reached 6.47W(mK)-1 and 3.98mm2s-1 at 12vol% PCF and 4vol% Al2O3 loadings, which are 32% and 38% higher than those of VA-PCF (12vol%) /SiR composites, respectively. The heat conduction enhancement mechanisms of VA-PCF/HL-Al2O3 networks on their SiR composites are revealed by multiscale simulation: HL-Al2O3 bridges the separate VA-PCF heat flow channels, and transfers more heat to the matrix, thereby increasing the vertical heat flux in composites. Along with high volume resistivity, low compression modulus, and coefficient of thermal expansion, VA-PCF/HL-Al2O3/SiR composites demonstrate great application potential as TIMs, which is proven using multiphysics simulation. This work not only makes a meaningful attempt at simulation-driven biomimetic material structure design but also provides inspiration for the preparation of TIMs.

Boosted Intracavity Aperture in Macrocyclic Amines Enabling Finely Regulated Microporous Membranes.

Finely tuning the pore structure of traditional nanofiltration (NF) membranes is challenging but highly effective for achieving efficient separations. Herein, we propose a concept of using macrocyclic amines (1,4,7-triazacyclononane, 3A; 1,4,7,10-tetraazacyclododecane, 4A1; and 1,4,8,11-tetraazacyclotetradecane, 4A2) with different intra-annular apertures to finely modulate the pore structure of microporous membranes via interfacial polymerization (IP). The boost in the intracavity size of the building blocks results in heightened steric hindrance of these amine monomers, leading to a controlled increase in membrane pore size, as demonstrated by both film characterizations and multiscale simulations. In conjunction with the increased intracavity size, the water permeability follows an augmented trend of 3A-TMC, 4A1-TMC, and 4A2-TMC (TMC: trimesoyl chloride) while exhibiting increased molecular weight cut-offs due to larger free-volume elements and stronger pore interconnectivity. Our proposed macrocyclic amine design strategy provides a guideline for finely regulated microporous membranes with high potential in NF-related applications.

Extra trees regression assisted 1D monolith reactor simulations based on microkinetic analysis and rate transformation

The incorporation of microkinetic method for surface reaction calculation is a key development direction in monolith reactor simulations, albeit posing significant computational stability and efficiency challenges. A data-driven multi-scale simulation framework is proposed to bridge the gap between macroscopic numerical model and microscopic reaction solvers. After the generating of precomputed microkinetic dataset, we utilize a tailor-designed preprocessing transformation, AD_log10, to effectively manage the common challenges of large variances and reversed directions in microkinetic rate data. A suite of machine learning regression models is developed as reaction rate solver integrated into the 1D two-phase monolith model. We employ NH3 selective catalytic oxidation on Cu(100) and Cu(111) as probe systems to validate our framework’s efficacy. In the context of on-board simulations under operating conditions, the Extra Trees Regression (with best regression performance) based model exhibits commendable accuracy and efficiency, successfully addressing complex processes previously unmanageable with interpolation-based models. This innovative approach paves the way for advanced monolith reactor modeling and sheds light on multi-scale simulation methodologies in chemical reaction engineering.