Multiscale Computational Study Research Articles

Exploring the suppression methods of helium-induced damage in tungsten by investigating the interaction between beryllium and helium

Abstract Helium (He)-induced damage is a sensitive concern for the performance of tungsten plasma facing materials (W-PFMs). Recent experiments have revealed that trace impurities in He plasma can effectively prevent the formation of He bubbles and fuzz on W surfaces. To explore its plausibility and underlying mechanism, we performed a multiscale computational study that combines density functional theory calculations and object kinetic Monte Carlo simulations to investigate the effects of a small quantity of beryllium (Be) on the evolution of He bubbles. It is found that there is a strong attractive interaction between He and Be, which can be attributed to the decrease in electron density and the lattice distortion induced by embedded Be atoms. Therefore, the co-implantation of Be continuously introduces trapping centers for He. Due to the low implantation depth and high migration energy of Be, the Be atoms are located close to the surface, leading to the trapping of the majority of He within the near-surface region and the development of a shielding layer for He permeation. The presence of Be facilitates the dispersion of the trapped He, skewing the He clusters into smaller sizes. More importantly, the Be trapping centers bring the He clusters closer to the surface, significantly increasing the probability of bubble bursting and the release of He back to the vacuum. This ultimately leads to a lower retention of He in the case of He + Be co-irradiation, compared with the case of He-only irradiation. Consequently, our findings elucidate the suppressive effect of a low flux of Be atoms on the growth of He bubbles, highlighting the need to focus on synergetic effects between plasma species.

Multiscale Computational Study of Cellulose Acetate-Water Miscibility: Insights from Molecularly Informed Field-Theoretic Modeling.

Cellulose acetate (CA), a prominent water-soluble derivative of cellulose, is a promising biodegradable ingredient that has applications in films, membranes, fibers, drug delivery, and more. In this work, we present a molecularly informed field-theoretic model for CA to explore its phase behavior in aqueous solutions. By integrating atomistic details into large-scale field-theoretic simulations via the relative entropy coarse-graining framework, our approach enables efficient calculations of CA's miscibility window as a function of the degree of substitution (DS) of cellulose hydroxyl groups with acetate side chains. This allows us to capture the intricate phase behavior of CA, particularly its unique miscibility at intermediate substitution, without relying on experimental input. Additionally, the model directly probes CA solution behavior specific to the relative DS at C2, C3, and C6 alcohol sites, providing insights for the rational design of water-soluble CA for diverse applications. This work demonstrates a promising integration of molecularly informed field theories, complementing wet-lab experimentation, for engineering the next-generation polymeric materials with precisely tailored properties.

Multiscale computational study to predict the irradiation-induced change in engineering properties of fusion reactor materials

In this study, we address the impact of irradiation conditions in a tokamak on the engineering properties of materials, leading to potential degradation of in-vessel components over their lifecycle. Our approach involves a predictive model for irradiation-induced damage, employing a multiscale computational framework. This framework integrates various simulation techniques, including Monte Carlo-based neutronics (OpenMC), dislocation dynamics (DD) using MoDELib, and finite element analysis (FEA) with Code_Aster. This integration offers a versatile solver capable of analysing tokamak components exposed to different irradiation doses and temperature conditions.To showcase the utility of this multiscale computational framework, we present a case study focused on tungsten monoblock designs. We assess the failure probabilities of these designs at different stages of their lifecycle. Neutron heating and damage energy values are obtained from OpenMC neutronics simulations. The neutron heating values serve as volumetric heat sources for the FEA thermal simulation. We calculate the displacement per atom (dpa) across the monoblock at various full power days (day 0, day 100, and day 1000) using the damage energy values. The irradiation-induced defect densities, dependent on temperature and dpa, are inputs to DD microstructural simulations performed on the representative volume element (RVE) using MoDELib. This allows us to obtain the yield stress of the material. Subsequently, the thermal fields from the FEA thermal simulation, along with the dpa and temperature-dependent yield stress from the DD simulation, are implemented for FEA mechanical simulations.To evaluate the failure probability of the monoblock designs at different stages of their lifecycle, we conduct an SDC-IC assessment, incorporating a plastic flow localization rule within the current framework. This comprehensive approach provides insights into the thermo-mechanical behaviour of in-vessel components subjected to neutron irradiation, offering a predictive capability for assessing their performance over time.

Tailoring vibrational behavior in hybrid cellular sandwich nanobeams: a multiscale computational study

Tailoring vibrational behavior in hybrid cellular sandwich nanobeams: a multiscale computational study

To elucidate the effect of alloying elements for enhanced nitriding of aluminum: A multiscale computational study

The limitations of aluminum (Al) are discussed in certain industrial applications, owing to its low hardness and wear resistance. To overcome these limitations, surface modification techniques, such as the formation of aluminum nitride (AlN) layers on the Al surface, have been explored. However, conventional surface-nitriding processes have low productivity, and it is essential to develop a cost-effective method to ensure higher nitriding rates. This paper presents the results of a multiscale computational study on the atomic-scale mechanisms involved in gas nitriding processes and the effects of alloying elements on reaction kinetics using density functional theory calculations. This study also provides insights into the thermodynamic stability and kinetics of the process, contributing to the design and optimization of the nitriding process for the formation of AlN on Al surfaces using Thermo-Calc. and microkinetic analyses. This study demonstrates the potential of multiscale computational methods for advancing the understanding of surface modification techniques and their applications for nitriding on the surface of Al and suggests the best alloying element to enhance the nitriding rate.

A two-scale numerical study on the mechanobiology of abdominal aortic aneurysms.

Abdominal aortic aneurysms (AAAs) are a serious condition whose pathophysiology is related to phenomena occurring at different length scales. To gain a better understanding of the disease, this work presents a multi-scale computational study that correlates AAA progression with microstructural and mechanical alterations in the tissue. Macro-scale geometries of a healthy aorta and idealized aneurysms with increasing diameter are developed on the basis of existing experimental data and subjected to physiological boundary conditions. Subsequently, microscopic representative volume elements of the abluminal side of each macro-model are employed to analyse the local kinematics at the cellular scale. The results suggest that the formation of the aneurysm disrupts the micromechanics of healthy tissue, which could trigger collagen growth and remodelling by mechanosensing cells. The resulting changes to the macro-mechanics and microstructure of the tissue seem to establish a new homeostatic state at the cellular scale, at least for the diameter range investigated.

Exploring the Role of Functional Groups in Modulating NO and CO Adsorption and Diffusion in 2D (Zn)MOF-470: A Multiscale Computational Study

Exploring the Role of Functional Groups in Modulating NO and CO Adsorption and Diffusion in 2D (Zn)MOF-470: A Multiscale Computational Study

Effect of Shoe Outsole Wear on Friction during Dry and Wet Slips: A Multiscale Experimental and Computational Study

Effect of Shoe Outsole Wear on Friction during Dry and Wet Slips: A Multiscale Experimental and Computational Study

Experimental and multiscale computational static and dynamic study of 3D printed elements with mesostructure

Experimental and multiscale computational static and dynamic study of 3D printed elements with mesostructure

Origins of near-infrared-II emission tail and fluorescence enhancement of albumin-chaperoned cyanine dyes from a multiscale computational study

The quantum nature of the typically ignored but important NIR-II emission tail and the fluorescence enhancement mechanism for cyanine–protein assemblies have been revealed from a multiscale computational investigation.

A multiscale computational study of the effects of fluid flow and drug metabolism on drug delivery

A multiscale computational study of the effects of fluid flow and drug metabolism on drug delivery

In silico prediction of protein binding affinities onto core-shell PEGylated noble metal nanoparticles for rational design of drug nanocarriers.

Polymer-coated nanoparticles (NP) are commonly used as drug carriers or theranostic agents. Their uptake rates are modulated by the interactions with essential serum proteins such as transferrin and albumin. Understanding the control parameters of these interactions is crucial for improving the efficiency of these nanoscale devices. In this work, we perform a multiscale computational study of protein adsorption onto polyethylene glycol (PEG) coated gold and silver NPs, producing protein-NP adsorption rankings as a function of PEG grafting density, which are validated against previously reported experimental protein-NP binding constants. Furthermore, the applied nano-docking method provides information on the preferred orientation of proteins immobilised on the surface of NPs. We propose a method of construction of model core-shell NPs in silico. The presented protocol can provide molecular level insights for the experimental development of biosensors, nanocarriers, or other nanoplatforms where information on the preferred orientation of protein at the bio-nano interface is crucial, and enables fast in silico prescreening of assays of various nanocarriers, i.e., combinations of proteins, NPs, and coatings.

Effect of fibre aspect ratio and aggregation on nonlinear material property of random cellulose reinforced composites: A multiscale computational study

Multiscale finite element analysis based on the asymptotic homogenisation theory was performed on cellulose-fibre-reinforced plastics. Polypropylene, a thermoplastic resin, was used as the matrix, and the effect of the fibre morphology on the nonlinear response of the material was clarified. A new indicator, that is, the proportion of the fibre contribution based on strain energy, was applied to quantify the effect of reinforcing fibres in improving the mechanical properties. Representative volume element models of randomly oriented fibre dispersion were constructed using a random sequential adsorption algorithm, and the effects of the fibre aspect ratio and aggregation on the nonlinear response under a tensile load were clarified. Consequently, it was deduced that the elastic properties and maximum tensile stress fluctuated by 26.8 % and 39.6 %, respectively, depending on the fibre aspect ratio, and 5.95 % and 4.20 %, respectively, owing to fibre aggregation. Furthermore, by introducing the fibre contribution proportion, we found that both the fibre aspect ratio and aggregation belong to the secondary factor, and their influence can be evaluated almost uniformly by the increase rate determined from the fibre orientation distribution of the primary factor.

Axial crushing response of carbon/glass hybrid composite tubes: An experimental and multi-scale computational study

This study focuses on the axial crushing behavior of double hat-shaped composite tubes prepared using plain woven fabrics reinforced by carbon fibers, glass fibers and their intra-layer hybrids. A multi-scale modelling strategy was adopted to study the crushing response of composite structures. The Chamis model was used to calculate the effective properties of carbon and glass yarns at the micro-scale level. At the meso-scale level, representative volume element (RVE) models were constructed considering the real fabric pattern. Intra-layer hybridized lamina properties can be obtained using RVE models subjected to typical loading cases. The results were validated through tension, compression and in-plane shear tests. At the macro-scale level, we used a stacked-shell strategy to create the axial crushing finite element models. The effect of intra-layer hybrid on the crushing failure mechanisms of composite tubes was analyzed and compared to that of non-hybrid carbon fiber reinforced polymers (CFRP) or glass fiber reinforced polymers (GFRP) tubes.

Aspirin's effect on kinetic parameters of cells contributes to its role in reducing incidence of advanced colorectal adenomas, shown by a multiscale computational study.

Aspirin intake has been shown to lead to significant protection against colorectal cancer, for example with an up to twofold reduction in colorectal adenoma incidence rates at higher doses. The mechanisms contributing to protection are not yet fully understood. While aspirin is an anti-inflammatory drug and can thus influence the tumor microenvironment, in vitro and in vivo experiments have recently shown that aspirin can also have a direct effect on cellular kinetics and fitness. It reduces the rate of tumor cell division and increases the rate of cell death. The question arises whether such changes in cellular fitness are sufficient to significantly contribute to the epidemiologically observed protection. To investigate this, we constructed a class of mathematical models of in vivo evolution of advanced adenomas, parameterized it with available estimates, and calculated population level incidence. Fitting the predictions to age incidence data revealed that only a model that included colonic crypt competition can account for the observed age-incidence curve. This model was then used to predict modified incidence patterns if cellular kinetics were altered as a result of aspirin treatment. We found that changes in cellular fitness that were within the experimentally observed ranges could reduce advanced adenoma incidence by a sufficient amount to account for age incidence data in aspirin-treated patient cohorts. While the mechanisms that contribute to the protective effect of aspirin are likely complex and multi-factorial, our study demonstrates that direct aspirin-induced changes of tumor cell fitness can significantly contribute to epidemiologically observed reduced incidence patterns.

Multiscale Computational Study of the Conformation of the Full-Length Intrinsically Disordered Protein MeCP2.

The malfunction of the methyl-CpG binding protein 2 (MeCP2) is associated with the Rett syndrome, one of the most common causes of cognitive impairment in females. MeCP2 is an intrinsically disordered protein (IDP), making its experimental characterization a challenge. There is currently no structure available for the full-length MeCP2 in any of the databases, and only the structure of its MBD domain has been solved. We used this structure to build a full-length model of MeCP2 by completing the rest of the protein via ab initio modeling. Using a combination of all-atom and coarse-grained simulations, we characterized its structure and dynamics as well as the conformational space sampled by the ID and transcriptional repression domain (TRD) domains in the absence of the rest of the protein. The present work is the first computational study of the full-length protein. Two main conformations were sampled in the coarse-grained simulations: a globular structure similar to the one observed in the all-atom force field and a two-globule conformation. Our all-atom model is in good agreement with the available experimental data, predicting amino acid W104 to be buried, amino acids R111 and R133 to be solvent-accessible, and having a 4.1% α-helix content, compared to the 4% found experimentally. Finally, we compared the model predicted by AlphaFold to our Modeller model. The model was not stable in water and underwent further folding. Together, these simulations provide a detailed (if perhaps incomplete) conformational ensemble of the full-length MeCP2, which is compatible with experimental data and can be the basis of further studies, e.g., on mutants of the protein or its interactions with its biological partners.

Individual Contributions of Cardiac Ion Channels on Atrial Repolarization and Reentrant Waves: A Multiscale In-Silico Study.

The excitation, contraction, and relaxation of an atrial cardiomyocyte are maintained by the activation and inactivation of numerous cardiac ion channels. Their collaborative efforts cause time-dependent changes of membrane potential, generating an action potential (AP), which is a surrogate marker of atrial arrhythmias. Recently, computational models of atrial electrophysiology emerged as a modality to investigate arrhythmia mechanisms and to predict the outcome of antiarrhythmic therapies. However, the individual contribution of atrial ion channels on atrial action potential and reentrant arrhythmia is not yet fully understood. Thus, in this multiscale in-silico study, perturbations of individual atrial ionic currents (INa, Ito, ICaL, IKur, IKr, IKs, IK1, INCX and INaK) in two in-silico models of human atrial cardiomyocyte (i.e., Courtemanche-1998 and Grandi-2011) were performed at both cellular and tissue levels. The results show that the inhibition of ICaL and INCX resulted in AP shortening, while the inhibition of IKur, IKr, IKs, IK1 and INaK prolonged AP duration (APD). Particularly, in-silico perturbations (inhibition and upregulation) of IKr and IKs only minorly affected atrial repolarization in the Grandi model. In contrast, in the Courtemanche model, the inhibition of IKr and IKs significantly prolonged APD and vice versa. Additionally, a 50% reduction of Ito density abbreviated APD in the Courtemanche model, while the same perturbation prolonged APD in the Grandi model. Similarly, a strong model dependence was also observed at tissue scale, with an observable IK1-mediated reentry stabilizing effect in the Courtemanche model but not in the Grandi atrial model. Moreover, the Grandi model was highly sensitive to a change on intracellular Ca2+ concentration, promoting a repolarization failure in ICaL upregulation above 150% and facilitating reentrant spiral waves stabilization by ICaL inhibition. Finally, by incorporating the previously published atrial fibrillation (AF)-associated ionic remodeling in the Courtemanche atrial model, in-silico modeling revealed the antiarrhythmic effect of IKr inhibition in both acute and chronic settings. Overall, our multiscale computational study highlights the strong model-dependent effects of ionic perturbations which could affect the model’s accuracy, interpretability, and prediction. This observation also suggests the need for a careful selection of in-silico models of atrial electrophysiology to achieve specific research aims.

EEG slowing in predementia Alzheimer's disease is compatible with neuronal hyperactivity: A multiscale computational modeling study.

Evidence from animal models of Alzheimer's disease (AD) for neuronal hyperactivity and inhibitory dysfunction is accumulating, but the exact mechanism by which these microscale abnormalities lead to large-scale brain dysfunction and cognitive impairment in AD remains unclear. This study investigated empirically informed pathophysiological mechanisms of neuronal hyperactivity in a computational neurophysiological brain model and compared its output to EEG data of predementia AD patients. EEG-like oscillations were simulated by a computational brain network model of 78 neural masses, each representing regional activity of a large population of interconnected excitatory and inhibitory neurons, coupled according to human DTI-derived structural topology. Several scenarios of amyloid-β mediated neuronal hyperactivity were investigated, including excitatory hyperexcitability and inhibitory neuronal dysfunction. The model offered a direct readout of neuronal activity by showing excitatory neuron pulse density. Consequently, we performed spectral analysis and analyzed absolute power and relative power in relevant frequency bands and peak frequency on the model output of each simulated AD scenario as well as for resting state EEG of predementia AD patients with positive amyloid biomarkers (n = 196) and elderly controls (n = 180). Similarity in outcome measure changes between the modelled output of each scenario and human EEG resulted in a scenario compatibility score (range 0-6). Predementia AD patients showed increased relative theta power and decreased relative higher alpha power. The modeled scenarios involving 'inhibitory neuron hypoexcitability' and 'excessive neuronal excitation' were most compatible with the observed EEG changes in AD (score of 5), providing support for their presumed role. Other scenarios produced improbable physiologically parameter change combinations. Of the two best compatible scenarios, inhibitory hypoexcitability was the only scenario that led to neuronal hyperactivity in the model. We found that neuronal hyperactivity scenarios (i.e. increased pulse density) is compatible with large-scale oscillatory slowing of predementia AD patients. This study provides support for amyloid-driven neuronal disinhibition due to inhibitory hypoexcitability as most likely pathophysiological mechanism to explain oscillatory slowing in predementia AD.

Multiscale computational study of ligand binding pathways: Case of p38 MAP kinase and its inhibitors

Protein kinases are one of the most important drug targets in the past 10 years. Understanding the inhibitor association processes will profoundly impact new binder designs with preferred binding kinetics. However, after more than a decade of effort, a complete atomistic-level study of kinase inhibitor binding pathways is still lacking. As all kinases share a similar scaffold, we used p38 kinase as a model system to investigate the conformational dynamics and free energy transition of inhibitor binding toward kinases. Two major kinase conformations, Asp-Phe-Gly (DFG)-in and DFG-out, and three types of inhibitors, type I, II, and III, were thoroughly investigated in this work. We performed Brownian dynamics simulations and up to 340 μs Gaussian-accelerated molecular dynamics simulations to capture the inhibitor binding paths and a series of conformational transitions of the p38 kinase from its apo to inhibitor-bound form. Eighteen successful binding trajectories, including all types of inhibitors, are reported herein. Our simulations suggest a mechanism of inhibitor recruitment, a faster ligand association step to a pre-existing DFG-in/DFG-out p38 protein, followed by a slower molecular rearrangement step to adjust the protein-ligand conformation followed by a shift in the energy landscape to reach the final bound state. The ligand association processes also reflect the energetic favor of type I and type II/III inhibitor binding through ATP and allosteric channels, respectively. These different binding routes are directly responsible for the fast (type I binders) and slow (type II/III binders) kinetics of different types of p38 inhibitors. Our findings also echo the recent study of p38 inhibitor dissociation, implying that ligand unbinding could undergo a reverse path of binding, and both processes share similar metastates. This study deepens the understanding of molecular and energetic features of kinase inhibitor-binding processes and will inspire future drug development from a kinetic point of view.

Separating Binary Gaseous Mixtures of Ethene + Ethyne Using Cement Hydrate: A Multiscale Computational Study.

Cement production is a carbon intensive industry and is responsible for large quantities of greenhouse gases released into the atmosphere. Due to the significant embedded carbon costs of cement, it might be promising to investigate waste cement for alternative uses so as to maximize utility of this material. Recent computational work on the sorption of natural gas constitutions in cement hydrate suggested that it might be worthwhile examining its usefulness in separating mixtures of C2 hydrocarbons. In light of this and the ongoing challenges of separating ethene and ethyne in industry, this study employed a multiscale approach to assess the feasibility of pressure swing adsorption to separate mixtures of ethene + ethyne. By combining stochastic atomistic simulations with macroscale batch equilibrium modeling, ethene recovery, product gas composition, and the separation power were computed over a range of temperatures (from 273 to 323 K), pressures (100 to 2000 kPa), and adsorbent masses (10 to 40 g per mole of feed gas). The results of this study include a look at the intermolecular interactions in the system and their relationship to the adsorption behavior as described by well-known adsorption isotherm models. This can help point the way to selecting materials that are promising for gas separations.