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In this paper, mechanical properties of thermoset epoxy based two-phase and hybrid nanocomposites containing carbon nanotubes (CNTs), and carbon nano diamond particles (CNPs) are determined using two different multiscale modeling approaches. The effects of resin crosslinking, interphase mechanical properties, and filler agglomeration on nanocomposite mechanical properties are investigated. First, the crosslinking between Diglycidyl ether of bisphenol A (DGEBA) resin and Diethylenetriamine (DETA) hardener is modeled considering different crosslinking forms and ratios using molecular dynamics (MD) simulations. Results indicate that resin elastic modulus increases with increasing the crosslinking ratio especially above 75%, but crosslinking form has an insignificant effect on resin modulus. Next, different nanofillers and their interphases are modeled using MD simulations to determine the representative volume element (RVE) and the effective fiber sizes and mechanical properties. The thickness of the interphase for each nano filler type is determined from the radial distribution function (RDF) diagram in order to determine the effective fiber volume more realistically. Then, the general Halpin-Tsai model is modified by adding an interphase volume factor and is used to determine two-phase and hybrid nanocomposite mechanical properties using effective nano filler properties. In addition, a new numerical multiscale modeling technique was developed which uses the MD-determined effective filler properties along with finite element method (FEM) to determine nanocomposite mechanical properties. Nanocomposites reinforced with aligned and randomly oriented reinforcements are modeled. Good agreement is observed between the two multiscale modeling techniques for the two-phase nanocomposite mechanical properties. Finally, the effect of filler agglomeration on nanocomposite properties is investigated. The results indicate that agglomeration decreases elastic modulus of CNT/epoxy nanocomposite. However, agglomeration does not have a significant effect on elastic modulus of CNP/epoxy nanocomposite.

Subcutaneously injected formulations have been developed for many biological products including monoclonal antibodies (mAbs). A knowledge gap nonetheless remains regarding the absorption and catabolism mechanisms and kinetics of a large molecule at the administration site. A multiscale pharmacokinetic (PK) model was thus developed by coupling multiphysics simulations of subcutaneous (SC) absorption kinetics with whole-body pharmacokinetic (PK) modeling, bridged by consideration of the presystemic clearance by the initial lymph. Our local absorption simulation of SC-injected albumin enabled the estimation of its presystemic clearance and led to the whole-body PK modeling of systemic exposure. The local absorption rate of albumin was found to be influential on the PK profile. Additionally, nineteen mAbs were explored via this multiscale simulation and modeling framework. The computational results suggest that stability propensities of the mAbs are correlated with the presystemic clearance, and electrostatic charges in the complementarity-determining region influence the local absorption rate. Still, this study underscores a critical need to experimentally determine various biophysical characteristics of a large molecule and the biomechanical properties of human skin tissues.

The histone-like nucleoid structuring (H-NS) protein controls the expression of hundreds of genes in Gram-positive bacteria through its capability to coat and condense DNA. This mechanism requires the formation of superhelical H-NS protein filaments that are sensitive to temperature and salinity, allowing H-NS to act as an environment sensor. We use multiscale modeling and simulations to obtain detailed insights into the mechanism of H-NS filament’s sensitivity to environmental changes. Through the simulations of the superhelical H-NS filament, we reveal how different environments induce heterogeneity of H-NS monomers. Further, we observe that transient self-association within the H-NS filament creates temperature-inducible strain and might mildly oppose DNA binding. At last, we probe different H-NS–DNA complex architectures and show that complexation enhances the stability of both, DNA and H-NS superhelices. Overall, our results provide unprecedented molecular insights into the environmental sensing and DNA interactions of a prototypical nucleoid-structuring bacterial protein filament.

Limiting the degradation of solid oxide fuel cells is an important challenge for their widespread use and commercialization. The computational expense of long-term simulation of a full stack with conventional models is immense. In this study, we present a multiscale three-dimensional model of a degrading full stack of solid oxide cells, where we integrate degradation phenomena of nickel particle coarsening in the anode electrode, chromium poisoning of the cathode electrode, and oxidation of the interconnect into a multiscale model of the stack. This approach makes this type of simulation computationally feasible, and 38 thousand hours of the stack operation can be simulated in 1 h and 15 min on a high-end workstation. Hereby one can start to explore the optimum operating conditions for a range of parameters. The model is validated with experimental data from an 18-cell Julich Mark-F stack experiment and predicts common trends reported in the literature for evolutions of the stack performance, degradation phenomena, and the related model variables. Moreover, it captures how different regimes in the full stack degrades at different rates and how the various degradation phenomena interact over time. The model is used to investigate the effects of galvanostatic and potentiostatic operation modes, operating conditions, and flow configurations on the long-term performance of the stack. Results demonstrate, as expected, that potentiostatic operation mode, moderate temperature, lower load current, and counter-flow configuration improve the long-term performance of the stack.

While multiscale modeling significantly enhances the capability of molecular simulations of polymer systems, it is well realized that the systematically derived coarse-grained (CG) models generally underestimate the thermomechanical properties. In this work, a charge-based mapping scheme has been adopted to include explicit electrostatic interactions and benchmarked against two typical polymers, atactic poly(methyl methacrylate) (PMMA) and polystyrene (PS). The CG potentials are parameterized against the oligomer bulks of nine monomers per chain to match the essential structural features and the two basic pressure-volume-temperature (PVT) properties, which are obtained from the all-atomistic (AA) molecular dynamics (MD) simulations at a single elevated temperature. The so-parameterized CG potentials are extended with the MD method to simulate the two polymer bulks of one hundred monomers per chain over a wide temperature range. Without any scaling, all the simulated results, including mass densities and bulk moduli at room temperature, thermal expansion coefficients at rubbery and glassy states, and glass transition temperatures (Tg), compare well with the corresponding experimental data. The proposed scheme not only contributes to realistically simulating various thermomechanical properties of both apolar and polar polymers but also allows for directly simulating their electrical properties.

Polymeric materials have a broad range of mechanical and physical properties. They have been widely used in material science, biomedical engineering, chemical engineering, and mechanical engineering. The introduction of active elements into the soft matrix of polymers has enabled much more diversified functionalities of polymeric materials, such as self-healing, electroactive, magnetosensitive, pH-responsive, and many others. To further enable applications of these multifunctional polymers, a mechanistic modeling method is required and of great significance, as it can provide links between materials’ micro/nano-structures and their macroscopic mechanical behaviors. Towards this goal, molecular simulation plays an important role in understanding the deformation and evolution of polymer networks under external loads and stimuli. These molecular insights provide physical guidance in the formulation of mechanistic-based continuum models for multifunctional polymers. In this perspective, we present a molecular simulation-guided and physics-informed modeling framework for polymeric materials. Firstly, the physical theory for polymer chains and their networks is briefly introduced. It serves as the foundation for mechanistic-models of polymers, linking their chemistry, physics, and mechanics together. Secondly, the deformation of the polymer network is used to derive the strain energy density functions. Thus, the corresponding continuum models can capture the intrinsic deformation mechanisms of polymer networks. We then highlight several representative examples across multiphysics coupling problems to describe in detail for this proposed framework. Last but not least, we discuss potential challenges and opportunities in the modeling of multifunctional polymers for future research directions.

Due to the recent boom in shale gas production, aromatics production using direct nonoxidative methane dehydroaromatization (DHA) is being investigated extensively. However, due to rapid coke forma...

[EN] Tissue-level biomechanical properties and function derive from underlying cell signaling, which regulates mass deposition, organization, and removal. Here, we couple two existing modeling frameworks to capture associated multiscale interactions¿one for vessel-level growth and remodeling and one for cell-level signaling¿and illustrate utility by simulating aortic remodeling. At the vessel level, we employ a constrained mixture model describing turnover of individual wall constituents (elastin, intramural cells, and collagen), which has proven useful in predicting diverse adaptations as well as disease progression using phenomenological constitutive relations. Nevertheless, we now seek an improved mechanistic understanding of these processes; we replace phenomenological relations in the mixture model with a logic-based signaling model, which yields a system of ordinary differential equations predicting changes in collagen synthesis, matrix metalloproteinases, and cell proliferation in response to altered intramural stress, wall shear stress, and exogenous angiotensin II. This coupled approach promises improved understanding of the role of cell signaling in achieving tissue homeostasis and allows us to model feedback between vessel mechanics and cell signaling. We verify our model predictions against data from the hypertensive murine infrarenal abdominal aorta as well as results from validated phenomenological models, and consider effects of noisy signaling and heterogeneous cell populations., This work was supported by Grants from the US NIH (R01 HL105297, P01 HL134605, R01 HL139796, U01 HL142518, R01 HL146723)

Understanding the mechanisms behind lignin formation is an important research area with significant implications for the bioenergy and biomaterial industries. Computational models are indispensable tools for understanding this complex process. Models of the monolignol pathway in Populus trichocarpa and other plants have been developed to explore how transgenic modifications affect important bioenergy traits. Many of these models, however, only capture one level of biological organization and are unable to capture regulation across multiple biological scales. This limits their ability to predict how gene modification strategies will impact lignin and other wood properties. While the first multiscale model of lignin biosynthesis in P. trichocarpa spanned the transcript, protein, metabolic, and phenotypic layers, it did not account for cross-regulatory influences that could impact abundances of untargeted monolignol transcripts and proteins. Here, we present a multiscale model incorporating these cross-regulatory influences for predicting lignin and wood traits from transgenic knockdowns of the monolignol genes. The three main components of this multiscale model are (1) a transcript-protein model capturing cross-regulatory influences, (2) a kinetic-based metabolic model, and (3) random forest models relating the steady state metabolic fluxes to 25 physical traits. We demonstrate that including the cross-regulatory behavior results in smaller predictive error for 23 of the 25 traits. We use this multiscale model to explore the predicted impact of novel combinatorial knockdowns on key bioenergy traits, and identify the perturbation of PtrC3H3 and PtrCAld5H1&2 monolignol genes as a candidate strategy for increasing saccharification efficiencies while reducing negative impacts on wood density and height.

In this study, clay-soil organic matters (SOMs) interactions were investigated by combining first principles molecular dynamics (FPMD) and classical molecular dynamics (CMD) techniques. FPMD was employed to quantify the binding mechanisms of reactive SOMs groups on clay surfaces and to derive parameters for the bonding of the reactive groups on edge surfaces. By integrating the derived parameters with CVFF–CLAYFF force fields, CMD simulations were carried out to investigate the structures of large models of clay-SOMs associations. All possible reactive SOMs groups, including carboxylate, phosphate, quinone species and ammonium groups were taken into account. FPMD results showed that all these groups are important to the complexation of SOMs on clay surfaces under dry conditions but only chemical bonding of carboxylate and phosphate and cation bridging are of significance under wet conditions. pH dependence of carboxylate and phosphate and Eh-pH dependence of quinone species are presented. CMD simulations showed that SOMs bound on clay surfaces via direct bonding of carboxylate and Ca2+ bridging and revealed the effect of water on structures of the clay-SOMs association. Based on the computational results, a procedure was proposed for constructing realistic molecular models for soils. Possible applications and further improvements of these models are discussed.

Liquid metals typically do not intrinsically wet oxide surfaces due to chemical dissimilarity, preventing the formation of well-bonded interfaces in many applications. This paper investigates how the wetting and spreading of liquid silver (Ag) droplets on oxide surfaces can be enhanced via patterned nickel (Ni) particles using multiscale modeling. First, a force field (FF) for an Ag/yttria-stabilized-zirconia system was developed by fitting the interfacial binding energy to density-functional theory (DFT) calculations. Then, molecular dynamics (MD) simulations using these FF parameters revealed that the good wetting of Ag on Ni caused Ag droplets to be attracted to the Ni particles, promoting spreading. Thus, the optimal hexagonal Ni pattern was shown to enhance the wetting area by 224% compared to a bare oxide surface. Second, an analytical model predicting the maximum possible enhancement in wetting area as a function of the intrinsic wetting angle, size of the Ag droplet, and size of the Ni particles was derived and validated with MD simulations, to generalize this method to other substrates. Both the analytical model and MD results suggested that the ability of Ni patterns to enhance the Ag wetting area becomes more significant as the wettability of substrate becomes worse.

Supramolecular aggregates of synthetic dye molecules offer great perspectives to prepare biomimetic functional materials for light-harvesting and energy transport. The design is complicated by the fact that structure–property relationships are hard to establish, because the molecular packing results from a delicate balance of interactions and the excitonic properties that dictate the optics and excited state dynamics, in turn sensitively depend on this packing. Here we show how an iterative multiscale approach combining molecular dynamics and quantum mechanical exciton modeling can be used to obtain accurate insight into the packing of thousands of cyanine dye molecules in a complex double-walled tubular aggregate in close interaction with its solvent environment. Our approach allows us to answer open questions not only on the structure of these prototypical aggregates, but also about their molecular-scale structural and energetic heterogeneity, as well as on the microscopic origin of their photophysical properties. This opens the route to accurate predictions of energy transport and other functional properties., Multiscale modeling resolves the molecular structure of a synthetic light-harvesting complex, unraveling the microscopic origin of its photophysical properties.

Amoeboid cells often migrate using pseudopods, which are membrane protrusions that grow, bifurcate, and retract dynamically, resulting in a net cell displacement. Many cells within the human body, such as immune cells, epithelial cells, and even metastatic cancer cells, can migrate using the amoeboid phenotype. Amoeboid motility is a complex and multiscale process, where cell deformation, biochemistry, and cytosolic and extracellular fluid motions are coupled. Furthermore, the extracellular matrix (ECM) provides a confined, complex, and heterogeneous environment for the cells to navigate through. Amoeboid cells can migrate without significantly remodeling the ECM using weak or no adhesion, instead utilizing their deformability and the microstructure of the ECM to gain enough traction. While a large volume of work exists on cell motility on 2D substrates, amoeboid motility is 3D in nature. Despite recent progress in modeling cellular motility in 3D, there is a lack of systematic evaluations of the role of ECM microstructure, cell deformability, and adhesion on 3D motility. To fill this knowledge gap, here we present a multiscale, multiphysics modeling study of amoeboid motility through 3D-idealized ECM. The model is a coupled fluid‒structure and coarse-grain biochemistry interaction model that accounts for large deformation of cells, pseudopod dynamics, cytoplasmic and extracellular fluid motion, stochastic dynamics of cell-ECM adhesion, and microstructural (pore-scale) geometric details of the ECM. The key finding of the study is that cell deformation and matrix porosity strongly influence amoeboid motility, while weak adhesion and microscale structural details of the ECM have secondary but subtle effects.

A new computational framework based on multiscale modeling approach is developed to calculate the current–voltage (V–I) characteristics of a double stranded DNA (dsDNA) attached between two gold el...

Monoamine oxidase B (MAO-B) is a potential biomarker for Parkinson's disease (PD), a neurodegenerative disease associated with the loss of motor activities in human subjects. The disease state is associated with dopamine deprival, and so the inhibitors of MAO-B can serve as therapeutic drugs for PD. Since the expression level of MAO-B directly correlates to the disease progress, the distribution and population of this enzyme can be employed to monitor disease development. One of the approaches available for estimating the population is two-photon imaging. The ligands used for two-photon imaging should have high binding affinity and binding specificity toward MAO-B along with significant two-photon absorption cross sections when they are bound to the target. In this article, we study using a multiscale modeling approach, the binding affinity and spectroscopic properties (one- and two-photon absorption) of three (Flu1, Flu2, Flu3) of the currently available probes for monitoring the MAO-B level. We report that the binding affinity of the probes can be explained using the molecular size and binding cavity volume. The experimentally determined one-photon absorption spectrum is well reproduced by the employed QM/MM approaches, and the most accurate spectral shifts, on passing from one probe to another, are obtained at the coupled-cluster (CC2) level of theory. An important conclusion from this study is also the demonstration that intrinsic molecular two-photon absorption strengths (δ2PA) increase in the order δ2PA (Flu1) > δ2PA (Flu2) > δ2PA (Flu3). This is in contrast with experimental data, which predict similar values of two-photon absorption cross sections for Flu1 and Flu3. We demontrate, based on the results of electronic-structure calculations for Flu1 that this discrepancy cannot be explained by an explicit account for neighboring residues (which could lead to charge transfer between a probe and neighboring aromatic amino acids thus boosting δ2PA). In summary, we show that the employed multiscale approach not only can optimize two-photon absorption properties and verify binding affinity, but it can also help in detailed analyses of experimental data.

The change in surface damage/microstructures and its effects on the hydrogen (H) isotope/helium (He) dynamic behavior are the key factors for investigating issues of tungsten (W)-based plasma-facing materials (PFMs) in fusion such as surface erosion, H/He retention and tritium (T) inventory. Complex surface damage/microstructures are introduced in W by high-temperature plasma irradiation and new material design, typically including pre-damage and multi-ion co-deposition induced structures, solute elements and related composites, native defects like dislocations and interfaces, and nanostructures. Systematic experimental and theoretical researches were performed on H isotope/He retention in complex W-based materials in the past decades. In this review, we aim to provide an overview of typical surface damage/microstructures and their effects on H/He retention in W, both in the experiment and multiscale modeling. The distribution/state, dynamics evolution, and interaction with defects/microstructures of H/He are generally summarized at different scales. Finally, the current difficulties, challenges and future directions are also discussed about H/He retention in complex W-based PFMs.

Purpose The purpose of this paper is the development of a multiscale model which simulates the effect of the dispersion, the waviness, the interphase geometry as well as the agglomerations of multi-walled carbon nanotubes (MWCNTs) on the Young’s modulus of a polymer filled with 0.4 Vol.% MWCNTs. Design/methodology/approach For the determination of the homogenized elastic properties of the hybrid material representative unit cells (RUCs) have been used. The predicted homogenized elastic properties were used for the prediction of the Young’s modulus of the filled material by simulating a finite element (FE) model of a tensile specimen. Moreover, the model has been validated by comparing the predicted values of the numerical analysis with experimental tensile results. Findings As the MWCNT agglomerates increase, the results showed a remarkable decrease of the Young’s modulus regarding the polymer filled with aligned MWCNTs while only slight differences on the Young’s modulus have been found in the case of randomly oriented MWCNTs. This might be attributed to the low concentration of the MWCNTs (0.4 Vol.%) into the polymer. For low MWCNTs concentrations, the interphase seems to have negligible effect on the Young’s modulus. Furthermore, as the MWCNTs waviness increases, a remarkable decrease of the Young’s modulus of the polymer filled with aligned MWCNTs is observed. In the case that MWCNTs are randomly dispersed into the polymer, both numerical and experimental results have been found to be consistent regarding the Young’s modulus. Practical implications The methodology used can be adopted by any system containing nanofillers. Originality/value Although several studies on the effect of the MWCNTs distribution on the Young’s modulus have been conducted, limited results exist by using a more realistic RUC including a periodic geometry of more than 20 MWCNTs with random orientation and a more realistic waviness of MWCNTs with aspect ratio exceeding 150.

In this work, controlled bidirectional deformation of suspended nanostructures by site-specific ion irradiation is presented. Multiscale modeling of the bidirectional deformation of nanostructures by site-specific ion irradiation is presented, incorporating molecular dynamics (MD) simulations together with finite element analysis, to substantiate the bending mechanism. Strain engineering of the free-standing nanostructure is employed for controlled deformation through site-specific kiloelectronvolt ion irradiation experimentally using a focused ion beam. We report the detailed bending mechanism of suspended silicon (Si) nanostructures through ion-induced irradiations. MD simulations are presented to understand the ion-solid interactions, defects formation in the silicon nanowire. The atomic-scale simulations reveal that the ion irradiation-induced bidirectional bending occurs through the development of localized tensile-compressive stresses in the lattice due to defect formation associated with atomic displacements. With an increasing ion dose, the evolution of localized tensile to compressive stress is observed, developing the alternate bending directions calculated through finite element analysis. The findings of multiscale modeling are in excellent agreement with the bidirectional nature of bending observed through the experiments. The developed in situ approach for bidirectional controlled manipulation of nanostructures in this work can be used for nanofabrication of numerous novel three-dimensional configurations and can provide a route toward functional nanostructures and devices.

Understanding the specifics of interaction between protein and nanomaterial is crucial for designing efficient, safe, and selective nanoplatforms, such as biosensor or nanocarrier systems. Routing experimental screening for the most suitable complementary pair of biomolecule and nanomaterial used in such nanoplatforms might be a resource-intensive task. While a variety of computational tools is available for pre-screening libraries of small drug molecules interacting with proteins, options for high-throughput screening of protein libraries for binding affinities to new and existing nanomaterials are limited. In the current work, we present the results of a systematic computational study of protein interaction with zero-valent silver nanoparticles using a multiscale approach. A variety of blood plasma and dietary proteins, namely, bovine and human serum albumins, bovine and human hemoglobin, papain, bromelain, lysozyme, and bovine lactoferrin, were examined. Selected combinations of nanomaterial and proteins can serve as a starting model for developing noble metal-based nanocarriers and biosensors. The computed binding (adsorption) characteristics for selected proteins were validated by experimental data reported in the literature. An advanced in silico nano-QSAR/QSPR interfacial descriptor 〖log⁡P〗^NM was also introduced to characterize the relative hydrophobicity/hydrophilicity of the nanomaterial.

A three‐step multiscale modeling approach to predict and thus control polymer properties, such as flow behavior and linear as well as non‐linear rheology, based on polymerization conditions, is developed and applied for long‐chain branched low‐density polyethylene (LDPE). The approach consists of i) a deterministic kinetic model for the description of conversion and average polymer characteristics, ii) a hybrid stochastic Monte Carlo model for the description of the polymeric microstructure, and iii) a rheology model for the evaluation of polymer melt flow properties. The modeling approach is validated via high‐pressure miniplant LDPE samples with a special focus on long‐chain branching. In the next step, the modeling approach can be successfully transferred to a tubular reactor of industrial scale. Due to its universality the approach opens up possible applications for other polymer and also copolymer systems.

Luminescent molecular aggregates have attracted worldwide attention because of their potential applications in many fields. The luminescent properties of organic aggregates are complicated and highly morphology-dependent, unraveling the intrinsic mechanism behind is urgent. This review summarizes recent works on investigating the structure–property relationships of organic molecular aggregates at different environments, including crystal, cocrystal, amorphous aggregate, and doped systems by multiscale modeling protocol. We aim to explore the influence of intermolecular non-covalent interactions on molecular packing and their photophysical properties and then pave the effective way to design, synthesize, and develop advanced organic luminescent materials.

Computational tools provide the automation and power that enable detailed modeling and analysis of many biomolecular phenomena of interest. Open source programs and automated tools empower researchers and provide opportunities for improvement to existing software. In the past few years, I have developed several open-source scientific software packages for the purposes of automating difficult or menial tasks pertaining to computational biophysics. These software packages involve the analysis of molecular dynamics simulations, Brownian dynamics simulations, electrostatics, pocket volume measurement, solvent fragment mapping, binding site characterization, milestoning theory, and allosteric network communications. In addition to allowing my research group and me to approach biomedical challenges that would otherwise be intractable, I hope and intend that these tools will be useful to the computational and theoretical biophysics research community.

A computational homogenization of heterogeneous solids is presented based on the data-driven approach for both linear and nonlinear elastic responses. Within the Double-Scale Finite Element Method (FE2) framework, a data-driven model is proposed to substitute the micro-level Finite Element (FE) simulations to reduce computational costs in multiscale simulations. The heterogeneity of porous solids at the micro-level is considered in various material properties and geometrical attributes. For material properties, elastic constants, which are Lame’s coefficients, are subjected to be heterogeneous in the linear elastic responses. For geometrical features, different numbers, sizes, and locations of voids are considered to reflect the heterogeneity of porous solids. A database for homogenized microstructural responses is constructed from a series of micro-level FE simulations, and machine learning is used to train and test our proposed model. In particular, four geometrical descriptors are designed, based on N-probability and lineal-path functions, to clearly reflect the geometrical heterogeneity of various microstructures. This study indicates that a simple deep neural networks model can capture diverse microstructural heterogeneous responses well when given proper input sources, including the geometrical descriptors, are considered to establish a computational data-driven homogenization scheme.

We formulate a multiscale mathematical model that describes the bioreduction of uranium in porous media. On the mesoscale we describe the bioreduction of uranium [VI] to uranium [IV] using a multispecies one-dimensional biofilm model with suspended bacteria and thermodynamic growth inhibition. We upscale the mesoscopic (colony scale) model to the macroscale (reactor scale) and investigate the behavior of substrate utilization and production, attachment and detachment processes, and thermodynamic effects not usually considered in biofilm growth models. Simulation results of the reactor model indicate that thermodynamic inhibition quantitatively alters the dynamics of the model and neglecting thermodynamic effects may over- or underestimate chemical concentrations in the system. Furthermore, we numerically investigate uncertainties related to the specific choice of attachment and detachment rate coefficients and find that while increasing the attachment rate coefficient or decreasing the detachment rate coefficient leads to thicker biofilms, performance of the reactor remains largely unaffected.

An increasingly utilized way for the production of propene is propane dehydrogenation. The reaction presents an alternative to conventional processes based on petroleum resources. In this work, we investigate theoretically how Cr2O3 catalyzes this reaction in oxidative and reducing environments. Although previous studies showed that the reduced catalyst is selective for the non-oxidative dehydrogenation of propane, real operating conditions are oxidative. Herein, we use multiscale modeling to investigate the difference between the oxidized and reduced catalyst and their performance. The complete reaction pathway for propane dehydrogenation, including C-C cracking, formation of side products (propyne, ethane, ethylene, acetylene, and methane), and catalyst coking on oxidized and reduced surfaces of α-Cr2O3(0001), is calculated using density functional theory with the Hubbard correction. Parameters describing adsorption, desorption, and surface reactions are used in a kinetic Monte Carlo simulation, which employed industrially relevant conditions (700-900 K, pressures up to 2 bar, and varying oxidants: N2O, O2, and none). We observe that over the reduced surface, propene and hydrogen form with high selectivity. When oxidants are used, the surface is oxidized, which changes the reaction mechanism and kinetics. During a much faster reaction, H2O forms as a coproduct in a Mars-van Krevelen cycle. Additionally, CO2 is also formed, which represents waste and adversely affects the selectivity. It is shown that the oxidized surface is much more active but prone to the formation of CO2, while the reduced surface is less active but highly selective toward propene. Moreover, the effect of the oxidant used is investigated, showing that N2O is preferred to O2 due to higher selectivity and less catalyst coking. We show that there exists an optimum degree of surface oxidation, where the yield of propene is maximized. The coke, which forms during the reaction, can be burnt away as CO2 with oxygen.

Memory B cells and antibody-secreting plasma cells are generated within germinal centers during affinity maturation in which B-cell proliferation, selection, differentiation, and self-renewal play important roles. The mechanisms behind memory B cell and plasma cell differentiation in germinal centers are not well understood. However, it has been suggested that cell fate is (partially) determined by asymmetric cell division, which involves the unequal distribution of cellular components to both daughter cells. To investigate what level and/or probability of asymmetric segregation of several fate determinant molecules, such as the antigen and transcription factors (BCL6, IRF4, and BLIMP1) recapitulates the temporal switch and DZ-to-LZ ratio in the germinal center, we implemented a multiscale model that combines a core gene regulatory network for plasma cell differentiation with a model describing the cellular interactions and dynamics in the germinal center. Our simulations show that BLIMP1 driven plasma cell differentiation together with coupled asymmetric division of antigen and BLIMP1 with a large segregation between the daughter cells results in a germinal center DZ-to-LZ ratio and a temporal switch from memory B cells to plasma cells that have been observed in experiments.

Recently, molecular covalent docking has been extensively developed to design new classes of inhibitors that form chemical bonds with their biological targets. This strategy for the design of such inhibitors, in particular boron-based inhibitors, holds great promise for the vast family of β-lactamases produced,inter alia, by Gram-negative antibiotic-resistant bacteria. However, the description of covalent docking processes requires a quantum-mechanical approach, and so far, only a few studies of this type have been presented. This study accurately describes the covalent docking process between two model inhibitors - representing two large families of inhibitors based on boronic-acid and bicyclic boronate scaffolds, and three β-lactamases which belong to the A, C, and D classes. Molecular fragments containing boron can be converted from a neutral, trigonal, planar state with sp2hybridization to the anionic, tetrahedral sp3state in a process sometimes referred to asmorphing. This study applies multi-scale modeling methods, in particular, the hybrid QM/MM approach which has predictive power reaching well beyond conventional molecular modeling. Time-dependent QM/MM simulations indicated several structural changes and geometric preferences, ultimately leading to covalent docking processes. With current computing technologies, this approach is not computationally expensive, can be used in standard molecular modeling and molecular design works, and can effectively support experimental research which should allow for a detailed understanding of complex processes important to molecular medicine. In particular, it can support the rational design of covalent boron-based inhibitors for β-lactamases as well as for many other enzyme systems of clinical relevance, including SARS-CoV-2 proteins.

In this study, a three-dimensional model of single carbon-fiber composites enhanced by radially grown zinc oxide (ZnO) nanowires is investigated numerically. Due to the different length scales of the composites and the theories used in the system, a multi-scale analysis is employed to simulate the behavior of the fiber-reinforced composites. The effective mechanical properties of the enhancement layer are extracted at the micro-scale by the homogenization analysis of an appropriate representative volume element. The fiber interface is modeled at the meso-scale utilizing the cohesive zone method. A thin layer of interface with the cohesive element is modeled around the fiber. The material properties of the interface are evaluated based on the properties of fiber and the enhancement layer. The macro-scale damage behavior of fiber is defined by user-defined mechanical material behavior. Single fiber fragmentation test is simulated in ABAQUS by applying the tensile loads on the structure. The load transfer mechanism is evaluated by capturing the number of fiber fragmentation and calculating the interfacial shear strength. The effect of different ZnO diameters and volume fractions are also investigated. The results show stronger interface and higher load transfer capacity in the enhanced composite compared to the bare composite.