Evaluation of tin-whisker growth during thermal-cycle testing using stress- and mass-diffusion analysis

Dynamic growth mechanism of tin whisker driven by compressive stress under thermal-mechanic-electric-diffusion coupling

Molecular dynamics (MD) simulation and finite element (FE) method have been successfully applied in the simulation of the machining process, but the two methods have their own limitations. For example, the MD simulation can only explain the phenomena occurring at nanometric scale because of the computational cost and nanoscale, while the FE method is suited to model meso-macroscale machining and to simulate macro parameters such as the temperature in cutting zone, the stress/strain distribution, and cutting forces. With the successful application of multiscale simulation in many research fields, the multiscale simulation of the machining process is becoming possible in relation to the machined surface generation including the surface roughness, residual stress, microhardness, microstructure, and fatigue. Based on the quasicontinuum (QC) method, this paper presents the multiscale simulation of nanometric cutting of crystal copper to demonstrate that a combined MD—FE technique can be applied to a multiscale simulation of the machining process. The study shows that the multiscale simulation is feasible, not withstanding that there is still more work needing to be done to make the multiscale simulation more practical.

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

Molecular dynamics (MD) and finite-element (FE) simulations of a nano-crystalline diamond (NCD) grain system subjected to indentation were undertaken to understand how contact behavior pertaining to the surface roughness of NCD crystals can be spanned from the molecular to the continuum scale. It is shown that if the same surface roughness morphology, grain orientations, and elastic properties are used for both MD and FE simulations, there is agreement between contact pressures for relatively low indentation loads and shallow substrates. It should be emphasized that the strong correlation between MD and FE methods can also be due to the lack of defect nucleation associated with the elastic deformation of the NCD system. These predictions can be used for a hierarchical computational framework to harness the advantages of both computational approaches. Inherent to this approach is representation of surface roughness and crystal orientation that is physically consistent for both computational approaches. Using the techniques presented herein, MD simulations of fluids and fluid solid interactions may be able to be accurately performed using computational fluid dynamics (CFD) and FE simulations if an appropriate set of translation rules can be found for converting MD models to CFD models. An initial analysis of the requirements to convert MD models to CFD is undertaken and a subset of the necessary rules is postulated.

P097 Direct measurement of electric fields in human and monkeys during transcranial electric stimulation

The term molecular mechanics (MM) refers to the use of simple potential-energy functions (e.g., harmonic oscillator or Coulombic potentials) to model molecular systems. Molecular mechanics approaches are widely applied in molecular structure refinement, molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, and ligand-docking simulations. Typically, molecular mechanics models consist of spherical atoms connected by springs which represent bonds. Internal forces experienced in the model structure are described using simple mathematical functions. For example, Hooke’s law is commonly used to describe bonded interactions, and the nonbonded atoms might be treated as inelastic hard spheres or may interact according to a Lennard-Jones potential. Using these simple models, a molecular dynamics simulation numerically solves Newton’s equations of motion, thus allowing structural fluctuations to be observed with respect to time. Dynamic simulation methods are widely used to obtain information on the time evolution of conformations of proteins and other biological macromolecules1–4 and also kinetic and thermodynamic information. Simulations can provide fine detail concerning the motions of individual particles as a function of time. They can be utilized to quantify the properties of a system at a precision and on a time scale that is otherwise inaccessible, and simulation is, therefore, a valuable tool in extending our understanding of model systems. Theoretical consideration of a system additionally allows one to investigate the specific contributions to a property through “computational alchemy”,5 that is, modifying the simulation in a way that is nonphysical but nonetheless allows a model’s characteristics to be probed. One particular example is the artificial conversion of the energy function from that representing one system to that of another during a simulation. This is an important technique in free-energy calculations.6 Thus, molecular dynamics simulations, along with a range of complementary computational approaches, have become valuable tools for investigating the basis of protein structure and function. This review offers an outline of the origin of molecular dynamics simulation for protein systems and how it has developed into a robust and trusted tool. This review then covers more recent advances in theory and an illustrative selection of practical studies in which it played a central role. The range of studies in which MD has played a considerable or pivotal role is immense, and this review cannot do justice to them; MD simulations of biomedical importance were recently reviewed.4 Particular emphasis will be placed on the study of dynamic aspects of protein recognition, an area where molecular dynamics has scope to provide broad and far-ranging insights. This review concludes with a brief discussion of the future potential offered to advancement of the biological and biochemical sciences and the remaining issues that must be overcome to allow the full extent of this potential to be realized. 1.1. Historical Background MD methods were originally conceived within the theoretical physics community during the 1950s. In 1957, Alder and Wainwright7 performed the earliest MD simulation using the so-called hard-sphere model, in which the atoms interacted only through perfect collisions. Rahman8 subsequently applied a smooth, continuous potential to mimic real atomic interactions. During the 1970s, as computers became more widespread, MD simulations were developed for more complex systems, culminating in 1976 with the first simulation of a protein9,10 using an empirical energy function constructed using physics-based first-principles assumptions. MD simulations are now widely and routinely applied and especially popular in the fields of materials science11,12 and biophysics. As will be discussed later in this review, a variety of experimental conditions may be simulated with modern theories and algorithms. The initial simulations only considered single molecules in vacuo. Over time, more realistic or at least biologically relevant simulations could be performed. This trend is continuing today. The initial protein MD simulation, of the small bovine pancreatic trypsin inhibitor (BPTI), covered only 9.2 ps of simulation time. Modern simulations routinely have so-called equilibration periods much longer than that, and production simulations of tens of nanoseconds are routine, with the first microsecond MD simulation being reported in 1998.13 In addition, the original BPTI simulation included only about 500 atoms rather than the 104-106 atoms that are common today. While much of this advancement results from an immense increase in availability of computing power, major theoretical and methodological developments also contribute significantly. The number of publications regarding MD theory and application of MD to biological systems is growing at an extraordinary pace. A single review cannot do justice to the recent applications of MD. Using data from ISI Web of Science, the authors estimate that during 2005 at least 800 articles will be published that discuss molecular dynamics and proteins. The historical counts are shown in Figure 1. Open in a separate window Figure 1 Articles matching ISI Web of Science query “TS=(protein) AND TS=(molecular dynamics)”.

Carbon nanorings (CNRs) serve as an ideal quantum system for novel electronic and magnetic properties. Although extensive theoretical studies utilizing molecular dynamics (MD) simulations have investigated the formation and structural characteristics of CNRs, systematically analyzing their properties across various toric sizes remains challenging due to the inherent complexity of this system. In this study, we introduce a novel finite element method, the Chebyshev-Ritz method, as an alternative approach to investigating the structural properties of CNRs. Previous MD simulations demonstrated that stable CNRs adopt a regular buckled shape at specific toric sizes. By meticulously selecting mechanical parameters, we observe that the critical deformation of a CNR with 50 repeated units, as determined by the Chebyshev-Ritz method, aligns with an MD simulation presenting a buckling number of 14. Additionally, the implementation of the Chebyshev-Ritz method with a constant mechanical parameter for 50 repeated units reveals a structural transition at varying toric sizes, leading to the stabilization of buckling numbers 13, 14, and 15. This structural transition across different buckling modes has also been corroborated by MD simulations. Our approach offers a reliable and accurate means of examining the structural properties of large-scale nanomaterials and paves the way for further exploration in nanoscale mechanics.

Structural analysis and molecular dynamics simulations on the surface extraction process of copper from sulfide ores: A case study of experimental and theoretical results

Selectivity and transport in aquaporins from molecular simulation studies.

Molecular dynamics simulation of the mixed anion glasses Li 4SiO 4Li 3BO 3

Diffuse X-Ray Scattering for Ensemble Modeling of Crystalline Proteins

Introduction: Milk kefir, a fermenting milk made with kefir grains, has shown potential in promoting apoptosis, regulating the cell cycle, and reducing tumor growth in breast cancer cells. This study aimed to investigate the stability and potential of milk kefir metabolites as inhibitors of breast cancer growth by interacting with the estrogen receptor alpha (ER-α), a key protein involved in breast cancer cell proliferation. We used computational methods, specifically molecular docking simulations with AutoDock and molecular dynamics (MD) simulations with Gromacs, to analyze how these metabolites bind to ER-α. Methods: A combination of molecular docking and MD simulations was used to explore how metabolites derived from milk kefir interact with ER-α, a crucial target in breast cancer therapy. The methodology included multiple stages: preparation of target proteins, preparation and screening of the metabolites, geometry optimization, molecular docking, and MD simulations. Results: The molecular docking simulations of 43 metabolites revealed three promising candidates: 2-Methyl (S35), benzeneethanol (S42), and 2,6-dimethyl-4-heptanone (S54), with binding affinities (ΔG) of -5.08, -5.06, and -4.90 kcal/mol, respectively. MD simulations further showed that the selected metabolites stabilized the ER-α-metabolite complex, with the 2,6-dimethyl-4-heptanone (S54) metabolite demonstrating the most negative total MM-GBSA energy value (ΔG = -22.98 kcal/mol), indicating a strong and stable binding interaction. Conclusion: 2,6-Dimethyl-4-heptanone, a metabolite from milk kefir, showed promising potential as a candidate for further development as a breast cancer treatment, offering a novel alternative to conventional therapies.

Modelling the behaviour of Pneumatic Artificial Muscle (PAM) has proven difficult due to its highly complicated structure, nonlinear nature of rubbery material, and air compressibility. To overcome these limitations, a FEM (Finite Element Method) model using Abaqus and CATIA is derived for the quantitative analysis on the impact of different factors on the pulling force of PAM. In the Abaqus a two parameter Mooney–Rivlin model is utilized to consider the hyper-elastic nature of flexible material. Then both Abaqus and CATIA are used in the parametric design of a 3-Dimensional model of PAM. Furthermore, the FEM model is employed to predict the static force exerted by PAM and the results show that the model is promising. The FEM model produces closer results to the test data for the typical PAM. Nonlinear behaviour of PAM is found to be obvious with an increase in both the contraction and the air pressure, different from the linear curves obtained by the fundamental geometrical model. Nonlinear changes in the PAM force are also observed in the numerical study on the effect of structural factors including initial braid angle, initial diameter, initial wall thickness, and flexible material. Besides, these phenomena can be explained by a connection between mechanical and morphological behaviour of PAMs with the FEM model. Generally, this modelling approach is more accurate compared to the fundamental theoretical model and more cost competitive compared to the empirical methods.

The thermal conductivity of emulsion-polymerized styrene–butadiene rubber (ESBR) composites filled with carbon nanotubes (CNTs), zinc oxide (ZnO) and alumina (Al2O3) was predicted using the finite element method (FEM). Two-dimensional (2D) FEM models, which involved the effects of aspect ratio (AR), shape, orientation and thermal conductivity anisotropy (TCA) of the CNTs, and interfacial thermal resistance (ITR), were used to simulate the microstructure of CNT filled ESBR composites. Also, 2D and three-dimensional (3D) FEM models were developed to simulate the microstructure of Al2O3 or ZnO filled ESBR composites. An increase in the thermal conductivity with increasing Al2O3 or ZnO loadings was predicted by the FEM. The orientation angle (OA) of the CNTs and the ITR strongly affect the thermal conductivity as predicted by the FEM. The TCA of the CNTs also has a prominent effect on the thermal conductivity when CNTs have a relatively small OA. At a given filler loading, the thermal conductivity increased with the increasing intrinsic thermal conductivity of the filler over a certain range for a particular shape of filler. The thermal conductivities predicted by the FEM were compared with those predicted by Agari's models and the experimental results. The trends of the thermal conductivity predicted by the FEM agreed with the experimental data. The thermal conductivity of the ESBR composites predicted by 2D and 3D spherical particle filler (SPF) FEM models as a function of ZnO and Al2O3 loading showed that the 3D SPF FEM model agreed well with the experimental results at low loadings (not higher than 20 phr), while the 2D SPF FEM model agreed well with the experimental results at high loadings (higher than 80 phr). In addition to being used for the analysis of existing composites, the proposed FEM models are useful for the design and optimization of new composite materials, and are expected to provide a more insightful understanding into the thermal conductivity of polymeric composites.

The microstructures of pure water and aqueous NaCl solutions over a wide range of salt concentrations (0-4 m) under ambient conditions are characterized by X-ray scattering and molecular dynamics (MD) simulations. MD simulations are performed with the rigid SPC water model as a solvent, while the ions are treated as charged Lennard-Jones particles. Simulated data show that the first peaks in the O...O and O...H pair correlation functions clearly decrease in height with increasing salt concentration. Simultaneously, the location of the second O...O peak, the signature of the so-called tetrahedral structure of water, gradually disappears. Consequently, the degree of hydrogen bonding in liquid water decreases when compared to pure fluid. MD results also show that the hydration number around the cation decreases as the salt concentration increases, which is most likely because some water molecules in the first hydration shell are occasionally substituted by chlorine. In addition, the fraction of contact ion pairs increases and that of solvent-separated ion pairs decreases. Experimental data are analyzed to deduce the structure factors and the pair correlation functions of each system. X-ray results clearly show a perturbation of the association structure of the solvent and highlight the appearance of new interactions between ions and water. A model of intermolecular arrangement via MD results is then proposed to describe the local order in each system, as deduced from X-ray scattering data.