Excess entropy scaling for the segmental and global dynamics of polyethylene melts.

The range of validity of the Rosenfeld and Dzugutov excess entropy scaling laws is analyzed for unentangled linear polyethylene chains. We consider two segmental dynamical quantities, i.e. the bond and the torsional relaxation times, and two global ones, i.e. the chain diffusion coefficient and the viscosity. The excess entropy is approximated by either a series expansion of the entropy in terms of the pair correlation function or by an equation of state for polymers developed in the context of the self associating fluid theory. For the whole range of temperatures and chain lengths considered, the two estimates of the excess entropy are linearly correlated. The scaled bond and torsional relaxation times fall into a master curve irrespective of the chain length and the employed scaling scheme. Both quantities depend non-linearly on the excess entropy. For a fixed chain length, the reduced diffusion coefficient and viscosity scale linearly with the excess entropy. An empirical reduction to a chain length-independent master curve is accessible for both dynamic quantities. The Dzugutov scheme predicts an increased value of the scaled diffusion coefficient with increasing chain length which contrasts physical expectations. The origin of this trend can be traced back to the density dependence of the scaling factors. This finding has not been observed previously for Lennard-Jones chain systems (Macromolecules, 2013, 46, 8710-8723). Thus, it limits the applicability of the Dzugutov approach to polymers. In connection with diffusion coefficients and viscosities, the Rosenfeld scaling law appears to be of higher quality than the Dzugutov approach. An empirical excess entropy scaling is also proposed which leads to a chain length-independent correlation. It is expected to be valid for polymers in the Rouse regime.

NanoConstruct: A web application builder of ellipsoidal nanoparticles for the investigation of their crystal growth, stability, and the calculation of atomistic descriptors.

NanoConstruct is a state-of-the-art computational tool that enables a) the digital construction of ellipsoidal neutral energy minimized nanoparticles (NPs) in vacuum through its graphical user-friendly interface, and b) the calculation of NPs atomistic descriptors. It allows the user to select NP's shape and size by inserting its ellipsoidal axes and rotation angle while the NP material is selected by uploading its Crystallography Information File (CIF). To investigate the stability of materials not yet synthesised, NanoConstruct allows the substitution of the chemical elements of an already synthesized material with chemical elements that belong into the same group and neighbouring rows of the periodic table. The process is divided into three stages: 1) digital construction of the unit cell, 2) digital construction of NP using geometry rules and keeping its stoichiometry and 3) energy minimization of the geometrically constructed NP and calculation of its atomistic descriptors. In this study, NanoConstruct was applied for the investigation of the crystal growth of Zirconia (ZrO2) NPs when in the rutile form. The most stable configuration and the crystal growth route were identified, showing a preferential direction for the crystal growth of ZrO2 in its rutile form. NanoConstruct is freely available through the Enalos Cloud Platform (https://enaloscloud.novamechanics.com/riskgone/nanoconstruct/).

ASCOT: A web tool for the digital construction of energy minimized Ag, CuO, TiO2 spherical nanoparticles and calculation of their atomistic descriptors.

ASCOT (an acronym derived from Ag-Silver, Copper Oxide, Titanium Oxide) is a user-friendly web tool for digital construction of electrically neutral, energy-minimized spherical nanoparticles (NPs) of Ag, CuO, and TiO2 (both Anatase and Rutile forms) in vacuum, integrated into the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/sabydoma/ascot/). ASCOT calculates critical atomistic descriptors such as average potential energy per atom, average coordination number, common neighbour parameter (used for structural classification in simulations of crystalline phases), and hexatic order parameter (which measures how closely the local environment around a particle resembles perfect hexatic symmetry) for both core (over 4 Å from the surface) and shell (within 4 Å of the surface) regions of the NPs. These atomistic descriptors assist in predicting the most stable NP size based on lowest per atom energy and serve as inputs for developing machine learning models to predict the toxicity of these nanomaterials. ASCOT's automated backend requires minimal user input in order to construct the digital NPs: inputs needed are the material type (Ag, CuO, TiO2-Anatase, TiO2-Rutile), target diameter, a Force-Field from a pre-validated list, and the energy minimization parameters, with the tool providing a set of default values for novice users.

Defining the Surface Oxygen Threshold That Switches the Interaction Mode of Graphene Oxide with Bacteria.

As antimicrobials, graphene materials (GMs) may have advantages over traditional antibiotics due to their physical mechanisms of action which ensure less chance of development of microbial resistance. However, the fundamental question as to whether the antibacterial mechanism of GMs originates from parallel interaction or perpendicular interaction, or from a combination of these, remains poorly understood. Here, we show both experimentally and theoretically that GMs with high surface oxygen content (SOC) predominantly attach in parallel to the bacterial cell surface when in the suspension phase. The interaction mode shifts to perpendicular interaction when the SOC reaches a threshold of ∼0.3 (the atomic percent of O in the total atoms). Such distinct interaction modes are highly related to the rigidity of GMs. Graphene oxide (GO) with high SOC is very flexible and thus can wrap bacteria while reduced GO (rGO) with lower SOC has higher rigidity and tends to contact bacteria with their edges. Neither mode necessarily kills bacteria. Rather, bactericidal activity depends on the interaction of GMs with surrounding biomolecules. These findings suggest that variation of SOC of GMs is a key factor driving the interaction mode with bacteria, thus helping to understand the different possible physical mechanisms leading to their antibacterial activity.

Sensitivity Analysis of ReaxFF Potential: The Case of Si/O System.

Sensitivity analysis of the ReaxFF potential for two Si and SiO2 systems has been carried out using the method of Morris. The goal is to identify the most important force field parameters for selected properties. Thus, a clearer physical interpretation for some of the parameters can be obtained while the ranking of the parameters per magnitude of sensitivity for each property facilitates the development of parametrizations with improved quality. The parameters related to the σ-bond and van der Waals interactions have the greatest influence on the properties of the cubic diamond Si phase. Counterintuitively, parameters which have an impact on mechanical properties, such as the ones for ππ-bonds, do not influence pressure and vice versa. For the ß-cristobalite SiO2 phase, the Si-O cross-interaction parameters have stronger effect on all properties than the Si-Si and O-O elemental ones. This dependence is attributed to the tetrahedral structure of the SiO2 phase. Regarding the sensitivity measures, the mean of the absolute values of the elementary effect (EE) distribution correlates with the standard deviation of the EE distribution in all cases; it quantifies the sensitivity of a property on a parameter and the cross-correlation of the parameter with the other ones. The mean of the EE distribution has a poor performance and its use should be avoided. It is also argued that performing a sensitivity analysis before force field optimization might greatly enhance the efficiency of the optimization methods by considering a two-step procedure, where the most relevant parameters are optimized first.

Atomistic Modeling of Hydrogen and Oxygen Solubility in Semicrystalline PA-6 and HDPE Materials.

Hydrogen is a clean and sustainable energy carrier which plays a major role in the transition of the global energy market to a less fossil fuel dependent future. Polymer-based materials are crucial in the production, storage, transportation, and energy extraction of hydrogen. More insights in the hydrogen-polymers interactions are required to guide material design and product development, especially for hydrogen solubility in polymers, which is crucial in many applications. The current study aims at rationalizing the determining factors of hydrogen solubility in two relevant polymers: polyamide-6 (PA-6) and high density polyethylene (HDPE). Based on atomistic molecular dynamics simulations and experimental data, we have reached several conclusions related to hydrogen and oxygen solubility in these two polymers. The crystal phases of PA-6 and HDPE are impenetrable to hydrogen and oxygen at elevated pressures, despite the small molecular size of hydrogen and oxygen. The practical implication for gas barrier applications is that polymer crystals act as impermeable obstacles and gas migration takes place primarily in the amorphous phase. Experimental hydrogen and oxygen solubilities in PA-6 and HDPE at elevated pressures can be predicted in a semiquantitative manner by molecular simulations. The discrepancies between experimental and predicted values could be attributed to neglect of the effect of crystal regions on the amorphous polymer domains. Although hydrogen is smaller than oxygen, it has been experimentally observed that hydrogen has a lower solubility in PA-6 and HDPE than oxygen. This observation has been confirmed by molecular simulations and attributed to the more favorable energetic interactions of oxygen with PA-6 and PE than of hydrogen. These interactions dominate the solubility behavior over the distribution of the accessible volume in the polymers.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

Atomic and global mechanical properties of systems described by the Stillinger-Weber potential.

Analytical expressions for the stress and elasticity tensors of materials, in which the interactions are described by the Stillinger-Weber potential, are derived in the context of the stress fluctuation formalism. The derived formulas can be used both in Monte Carlo and molecular dynamics simulations. As an example of possible applications, they are employed to calculate the influence of the temperature and system size on the mechanical properties of crystalline cubic boron nitride. The system has been studied by molecular dynamics simulations. The computed mechanical properties are in good agreement with available experimental data and first principle calculations. In the studied crystalline cubic boron nitride system, the employed formalism is of higher accuracy than the 'small-strain' non-equilibrium method. The dominant contributions to the elastic constants stem from the Born and stress fluctuation terms. An increase in the system size reduces the statistical uncertainties in the computation of the mechanical properties. A rise of the temperature leads to a slight increase in the observed uncertainties. The derived expressions for the stress and elasticity tensors are further decomposed into sums of atomic level stress and atomic level elasticity tensors. The developed factorization enables us (i) to quantify the contribution of the various chemical groups, in the case under consideration of the different atoms, to the observed mechanical properties and (ii) to determine the elastic constants with reduced computational uncertainties. The reason is that the exact values of some terms of the proposed factorization can be determined theoretically beforehand. Thus, they can be substituted in the derived formulas leading to an enhanced convergence.