Search Results (26)

This study comprehensively investigates the dynamic vibration behavior of multilayer carbon nanotubes with stepped cross-sectional geometries under various boundary conditions, which is crucial for their advanced engineering applications. The methodology integrates classical molecular dynamics simulations to determine the bending stiffness of single-walled and multi-walled atomistic structures, which are subsequently utilized in the Euler–Bernoulli beam theory based on nonlocal elasticity for vibration analysis. The research focuses on elucidating the influence of the μ/L ratio (a key length parameter) and different support conditions on the natural frequencies and mode shapes of these nanostructures. Key findings reveal that the cross-sectional geometry significantly impacts the vibrational characteristics. A consistent trend observed across all examined boundary conditions is a decrease in natural frequencies as the μ/L ratio increases, indicating that increased free length or reduced fixed length leads to lower stiffness and, consequently, reduced natural frequencies. The study presents Frequency Response Functions (FRFs) and the first four mode shapes, which visually confirm these dynamic characteristics. Graphical representations further reinforce the sensitivity of natural frequencies to both the μ/L ratio and support conditions. The systematic analysis presented in this work provides vital data for predicting resonance phenomena, optimizing structural stability, and enabling precise control over the vibrational response of these advanced nanomaterials in diverse engineering applications. Full article

A quantitative characterization of the change in coal molecular structures with different cyclical microwave modification parameters and a better understanding of the reaction mechanism of the modification are of great significance for the commercial extraction of coal bed methane (CBM). Therefore, long-flame coal samples obtained from the Ordos Basin, China, were modified by microwave radiation with different times, and the long-flame coal molecular structure parameters were determined by solid-state ¹³C nuclear magnetic resonance (ss¹³C NMR), Fourier transform infrared (FTIR) spectrometry, and X-ray photoelectron spectrometry (XPS). Atomistic representations of the raw long-flame coal molecular model and modified long-flame coal molecular models were established. The temperature rise, pore volume increase, mineral removal, and functional group changes after the modification have a negative effect on methane adsorption. After the modification, the decrease in surface area of the micropores reduced the adsorption site of methane in coal. As a result, the methane adsorption amount decreased linearly with the decreasing surface area. The CH₄ adsorption isotherms of the long-flame models were dynamically simulated and analyzed. The results of this study can prove that after multiple cycles of microwave modifications, the functional groups in long-flame coal were fractured, and the number of micropores was reduced, which effectively decreased the methane adsorption performance in long-flame coal seams, thereby promoting methane extraction. Microwave modification is a promising method for enhancing CBM recovery. Full article

This article reviews the literature on both (a) the general theory of public corruption and its evolution over time; and (b) the history of urban public corruption during the modern period into the present. In addition, this article conducts a historical narrative form of representation to structure the historical facts, synthesizing chronologically disparate elements into a synchronic unity, with the focus of the narrative on “urban public corruption”, both empirically (i.e., how its manifestation changed over time in particular times and places) and conceptually (i.e., how the ideas about these manifestations altered with the passage of time). The period considered is mainly from the onset of the Industrial Revolution in the mid-18th century, up to the present time, with a major focus on contemporary developments. This review illustrates that the extraction of rents has always been a key feature of urban corruption. Nonetheless, not all extraction is alike institutionally and historically. The analysis presented here indicates that the modern notion of corruptions has been the product of a long evolution which, no doubt, is not over. The present idea is of corruption as a technocratic one of a “coherent, discrete referent” based on rational choice theory, which can be applied to define acts as corrupt regardless of social context. This is found to be not applicable in all times and places. Defining what constitutes a “corrupt” act varies considerably across cultures, across times, between nations and cities, and sometimes even within them. This article closes with a template about possible behavioral spheres of urban corruption, incorporating overlaps and gaps between “rational”, “maximizing”, and “atomistic” motivations. The conclusion is that the current consensual “rational choice” understandings only apply to those acts which incorporate all three aspects, with variants drawn from “behavioral” economics expanding applicability somewhat, but still leaving out a great deal of city corruption on the ground which is much more social and institutional than present understandings allow for. Full article

Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems. The ezAlign v1.1 software package is publicly available for download at github.com/LLNL/ezAlign. Its underlying methodology is based on a simple alignment of an atomistic template molecule, followed by position-restraint energy minimization, which forces the atomistic molecule to adopt a conformation consistent with the coarse-grained molecule. The molecules are then combined, solvated, minimized, and equilibrated with position restraints. Validation of the process was conducted on a pure POPC membrane and compared with other popular methods to construct atomistic membranes. Additional examples, including surfactant self-assembly, membrane proteins, and more complex bacterial and human plasma membrane models, are also presented. By providing these examples, parameter files, code, and an easy-to-follow recipe to add new molecules, this work will aid future multiscale modeling efforts. Full article

Simulation of atomic force microscopy (AFM) computationally emulates experimental scanning of a biomolecular structure to produce topographic images that can be correlated with measured images. Its application to the enormous amount of available high-resolution structures, as well as to molecular dynamics modelling data, facilitates the quantitative interpretation of experimental observations by inferring atomistic information from resolution-limited measured topographies. The computation required to generate a simulated AFM image generally includes the calculation of contacts between the scanning tip and all atoms from the biomolecular structure. However, since only contacts with surface atoms are relevant, a filtering method shall highly improve the efficiency of simulated AFM computations. In this report, we address this issue and present an elegant solution based on graphics processing unit (GPU) computations that significantly accelerates the computation of simulated AFM images. This method not only allows for the visualization of biomolecular structures combined with ultra-fast synchronized calculation and graphical representation of corresponding simulated AFM images (live simulation AFM), but, as we demonstrate, it can also reduce the computational effort during the automatized fitting of atomistic structures into measured AFM topographies by orders of magnitude. Hence, the developed method will play an important role in post-experimental computational analysis involving simulated AFM, including expected applications in machine learning approaches. The implementation is realized in our BioAFMviewer software (ver. 3) package for simulated AFM of biomolecular structures and dynamics. Full article

Geopolymers offer a potential alternative to ordinary Portland cement owing to their performance in mechanical and thermal properties, as well as environmental benefits stemming from a reduced carbon footprint. This paper endeavors to build upon prior atomistic computational work delving deeper into the intricate relationship between pH levels and the resulting material’s properties, including pore size distribution, geopolymer nucleate cluster dimensions, total system energy, and monomer poly-condensation behavior. Coarse-grained Monte Carlo (CGMC) simulation inputs include tetrahedral geometry and binding energy parameters derived from DFT simulations for aluminate and silicate monomers. Elevated pH values may can alter reactivity and phase stability, or, in the structural concrete application, may passivate the embedded steel reinforcement. Thus, we examine the effects of pH values set at 11, 12, and 13 (based on silicate speciation chemistry), investigating their respective contributions to the nucleation of geopolymers. To simulate a larger system to obtain representative results, we propose the numerical implementation of an Octree cell. Finally, we further digitize the resulting expanded structure to ascertain pore size distribution, facilitating a comparative analysis. The novelty of this study is underscored by its expansion in both system size, more accurate monomer representation, and pH range when compared to previous CGMC simulation approaches. The results unveil a discernible correlation between the number of clusters and pores under specific pH levels. This links geopolymerization mechanisms under varying pH conditions to the resulting chemical properties and final structural state. Full article

In poorly consolidated carbonate rock reservoirs, solids production risk, which can lead to increased environmental waste, can be mitigated by injecting formation-strengthening chemicals. Classical atomistic molecular dynamics (MD) simulation is employed to model the interaction of polyacrylamide-based polymer additives with a calcite structure, which is the main component of carbonate formations. Amongst the possible calcite crystal planes employed as surrogates of reservoir rocks, the (1 0 4) plane is shown to be the most suitable surrogate for assessing the interactions with chemicals due to its stability and more realistic representation of carbonate structure. The molecular conformation and binding energies of pure polyacrylamide (PAM), hydrolysed polyacrylamide in neutral form (HPAM), hydrolysed polyacrylamide with 33% charge density (HPAM 33%) and sulfonated polyacrylamide with 33% charge density (SPAM 33%) are assessed to determine the adsorption characteristics onto calcite surfaces. An adsorption-free energy analysis, using an enhanced umbrella sampling method, is applied to evaluate the chemical adsorption performance. The interaction energy analysis shows that the polyacrylamide-based polymers display favourable interactions with the calcite structure. This is attributed to the electrostatic attraction between the amide and carboxyl functional groups with the calcite. Simulations confirm that HPAM33% has a lower free energy than other polymers, presumably due to the presence of the acrylate monomer in ionised form. The superior chemical adsorption performance of HPAM33% agrees with Atomic Force Microscopy experiments reported herein. Full article

Over the past ten years, breakthroughs in battery technology have dramatically propelled the evolution of electric vehicle (EV) technologies. For EV applications, accurately estimating the state-of-charge (SOC) is critical for ensuring safe operation and prolonging the lifespan of batteries, particularly under complex loading scenarios. Despite progress in this area, modeling and forecasting the evaluation of multiphysics and multiscale electrochemical systems under realistic conditions using first-principles and atomistic calculations remains challenging. This study proposes a solution by designing a specialized Transformer-based network architecture, called Bidirectional Encoder Representations from Transformers for Batteries (BERTtery), which only uses time-resolved battery data (i.e., current, voltage, and temperature) as an input to estimate SOC. To enhance the Transformer model’s generalization, it was trained and tested under a wide range of working conditions, including diverse aging conditions (ranging from 100% to 80% of the nominal capacity) and varying temperature windows (from 35 °C to −5 °C). To ensure the model’s effectiveness, a rigorous test of its performance was conducted at the pack level, which allows for the translation of cell-level predictions into real-life problems with hundreds of cells in-series conditions possible. The best models achieve a root mean square error (RMSE) of less than 0.5 test error and approximately 0.1% average percentage error (APE), with maximum absolute errors (MAE) of 2% on the test dataset, accurately estimating SOC under dynamic operating and aging conditions with widely varying operational profiles. These results demonstrate the power of the self-attention Transformer-based model to predict the behavior of complex multiphysics and multiscale battery systems. Full article

In recent years, a number of achiral liquid crystal dimer molecules have been shown to exhibit nematic–nematic phase transitions. The lower temperature phase has been identified as the N${}_{\mathrm{TB}}$ phase, which demonstrates emergent chirality in the spontaneous formation of a heliconical structure. Recent fully atomistic simulations of the molecule CB7CB (1,7-bis-4-(4${}^{\prime }$-cyanobiphenyl) heptane), a dimer with an odd number of carbon spacers between the mesogenic parts of the molecule, have captured the N${}_{\mathrm{TB}}$–N–I phase sequence, providing a picture of the order at a molecular level. In this paper, we use atomistic simulations of CB7CB to develop a coarse-grained model using systematic coarse graining in the N${}_{\mathrm{TB}}$ phase. We use both force matching (in the form of the MS-CG method) and iterative Boltzmann inversion (IBI) methodologies. Both techniques capture the heliconical order within the N${}_{\mathrm{TB}}$ phase. Moreover, the model developed via force matching is shown to provide an excellent representation of the atomistic simulation reference model and, remarkably, demonstrates good transferability across temperatures, allowing the N${}_{\mathrm{TB}}$–N and N–I phase transitions to be simulated. We also compare results with those of a Martini 3-based coarse-grained model. Full article

The Gromov-Wasserstein (GW) formalism can be seen as a generalization of the optimal transport (OT) formalism for comparing two distributions associated with different metric spaces. It is a quadratic optimization problem and solving it usually has computational costs that can rise sharply if the problem size exceeds a few hundred points. Recently fast techniques based on entropy regularization have being developed to solve an approximation of the GW problem quickly. There are issues, however, with the numerical convergence of those regularized approximations to the true GW solution. To circumvent those issues, we introduce a novel strategy to solve the discrete GW problem using methods taken from statistical physics. We build a temperature-dependent free energy function that reflects the GW problem’s constraints. To account for possible differences of scales between the two metric spaces, we introduce a scaling factor s in the definition of the energy. From the extremum of the free energy, we derive a mapping between the two probability measures that are being compared, as well as a distance between those measures. This distance is equal to the GW distance when the temperature goes to zero. The optimal scaling factor itself is obtained by minimizing the free energy with respect to s. We illustrate our approach on the problem of comparing shapes defined by unstructured triangulations of their surfaces. We use several synthetic and “real life” datasets. We demonstrate the accuracy and automaticity of our approach in non-rigid registration of shapes. We provide numerical evidence that there is a strong correlation between the GW distances computed from low-resolution, surface-based representations of proteins and the analogous distances computed from atomistic models of the same proteins. Full article

Accurate representations of slip and transitional flow regimes present a challenge in the simulation of rarefied gas flow in confined systems with complex geometries. In these regimes, continuum-based formulations may not capture the physics correctly. This work considers a regularized multi-relaxation time lattice Boltzmann (LB) method with mixed Maxwellian diffusive and halfway bounce-back wall boundary treatments to capture flow at high Kn. The simulation results are validated against atomistic simulation results from the literature. We examine the convergence behavior of LB for confined systems as a function of inlet and outlet treatments, complexity of the geometry, and magnitude of pressure gradient and show that convergence is sensitive to all three. The inlet and outlet boundary treatments considered in this work include periodic, pressure, and a generalized periodic boundary condition. Compared to periodic and pressure treatments, simulations of complex domains using a generalized boundary treatment conserve mass but require more iterations to converge. Convergence behavior in complex domains improves at higher magnitudes of pressure gradient across the computational domain, and lowering the porosity deteriorates the convergence behavior for complex domains. Full article

Cytosine-rich DNA sequences are able to fold into noncanonical structures, in which semi-protonated cytosine pairs develop extra hydrogen bonds, and these bonds are responsible for the overall stability of a structure called the i-motif. The i-motif can be formed in many regions of the genome, but the most representative is the telomeric region in which the CCCTAA sequences are repeated thousands of times. The ability to reverse folding/unfolding in response to pH change makes the above sequence and i-motif very promising components of nanomachines, extended DNA structures, and drug carriers. Molecular dynamics analysis of such structures is highly beneficial due to direct insights into the microscopic structure of the considered systems. We show that Amber force fields for DNA predict the stability of the i-motif over a long timescale; however, these force fields are not able to predict folding of the cytosine-rich sequences into the i-motif. The reason is the kinetic partitioning of the folding process, which makes the transitions between various intermediates too time-consuming in atomistic force field representation. Application of coarse-grained force fields usually highly accelerates complex structural transitions. We, however, found that three of the most popular coarse-grained force fields for DNA (oxDNA, 3SPN, and Martini) were not able to predict the stability of the i-motif structure. Obviously, they were not able to accelerate the folding of unfolded states into an i-motif. This observation must be strongly highlighted, and the need to develop suitable extensions of coarse-grained force fields for DNA is pointed out. However, it will take a great deal of effort to successfully solve these problems. Full article

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of A${\beta }_{1–40}$, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs. Full article

The paper aims to study the mechanical properties of epoxy resin filled with clay nanoparticles (NPs), depending on their shapes and content on the surface of a modifying agent capable of forming covalent bonds with a polymer. The cylindrical clay nanoparticles with equal volume and different aspects ratios (disks, barrel, and stick) are addressed. The NPs’ bonding ratio with the polymer (RGC) is determined by the fraction of reactive groups and conversion time and varies from RGC = 0 (non-bonded nanoparticles) to RGC = 0.65 (more than half of the surface groups are linked with the polymer matrix). The performed simulations show the so-called load-bearing chains (LBCs) of chemically cross-linked monomers and modified nanoparticles to determine the mechanical properties of the simulated composites. The introduction of nanoparticles leads to the breaking of such chains, and the chemical cross-linking of NPs with the polymer matrix restores the LBCs and strengthens the composite. At small values of RGC, the largest value of the elastic modulus is found for systems filled with nanoparticles having the smallest surface area, and at high values of RGC, on the contrary, the systems containing disk-shaped particles with the largest surface area have a larger elastic modulus than the others. All calculations are performed within the framework of a mesoscopic model based on accurate mapping of the atomistic structures of the polymer matrix and nanoparticles into coarse-grained representations, which, if necessary, allow reverse data mapping and quantitative assessment of the state of the filled epoxy resin. On the other hand, the obtained data can be used to design the functional materials with specified mechanical properties based on other practically significant polymer matrices and nanofillers. Full article

Assemblage theory complicates an already extensive literature on religious urbanity, cultural heritage, the social construction of space and the power of place. Nevertheless, the concept can be applied to social and religious history in locations such as West Asia. As this paper argues, avoiding dichotomous and politicised treatments of Karbala in dynamics of the Sunni–Shia divide and debates concerning the so-called “Karbala Paradigm”, the “Ashura Assemblage” demonstrates how space contributes to the reproduction of collective Shiʿi identity in Karbala. It outlines aspects of territoriality and sacralised Shiʿi rituals, tracing constantly recurring connections between various sacred spots, spaces and places of ritual. This study then illustrates how collective Shiʿi identity is mnemonically connected to Ashura-oriented spaces. This study utilises representational tools such as maps and diagrams to depict Karbala’s religious urban landscape. Ethnographic fieldwork and an array of primary and secondary source research uncover granularities in the Ashura Assemblage, suggesting a more prominent role for interpretive approaches to atomistic assemblages of urban religious spaces. Full article