Atmospheric humidity strongly influences the interactions between dry granular particles in process containers. To reduce the energy loss in industrial production processes caused by particle agglomeration, a basic understanding of the dependence of particle interactions on humidity is necessary. Hence, in this study, molecular dynamic simulations were carried out to calculate the adhesion between silica surfaces in the presence of adsorbed water. For a realistic description, the choice of force field is crucial. Because of their frequent use and transferability to biochemical systems, the Clay and CWCA force fields were investigated with respect to their ability to describe the water–silica interface in comparison to the more advanced Reax force field, ab initio calculations, and experiments.

Adhesion forces between nanoparticles strongly depend on the amount of adsorbed condensed water from ambient atmosphere. Liquid water forms bridges in the cavities separating the particles, giving rise to the so-called capillary forces which in most cases dominate the van der Waals and long-range electrostatic interactions. Capillary forces promote the undesirable agglomeration of particles to large clusters, thereby hindering the flowability of dry powders in process containers. In process engineering macroscopic theories based on the Laplace pressures are used to estimate the strength of the capillary forces. However, especially for low relative humidity and when the wetting of rough or small nanoparticles is studied, those theories can fail. Molecular dynamic simulations can help to give better insight into the water–particle interface. The simulated force versus distance curve as well as adhesion forces and the adsorption isotherm for silica nanoparticles at varying relative humidity will be discussed in comparison to experiments, theories, and simulations.

We present an implementation of a (mesh-free) smooth particle hydrodynamics (SPH) algorithm, intended for the application to solid bodies, and use it to simulate scratch-induced surface damage on an elasto-viscoplastic material. If conventional SPH is used to simulate solids, an unphysically high artificial viscosity is required to damp strong oscillatory modes and thus stabilize the system. To overcome these intrinsic difficulties associated with modeling solid bodies, the recently implemented so-called total-Lagrangian SPH utilizes an hourglass control scheme similar to what is known from finite element algorithms. Elasto-viscoplastic material properties are modeled by using the Mie–Grüneisen equation of state and the Johnson–Cook model. The material parameters are selected to reproduce the strain-stress behavior of annealed oxygen-free high conductivity copper. The spherical indenter is modeled as a rigid sphere. The topographies of the calculated scratch-induced surface damage are in excellent agreement with experimental scratch tests carried out at comparable normal loads. We also calculate the real contact area between the indenter and the surface, allowing a sound estimation of the scratch hardness

We present a method for calculating the thermodynamic and structural properties of a polydisperse liquid by means of a thermodynamic perturbation theory: the optimized random phase approximation (ORPA). The approach is an extension of a method proposed recently by one of us for an integral equation application [Phys. Rev. E 54, 4411 (1996)]. The method is based on expansions of all sigma-dependent functions in the orthogonal polynomials p(i)(sigma) associated with the weight function f(Sigma)(sigma), where sigma is a random variable (in our case the size of the particles) with distribution f(Sigma)(sigma). As in the one-component or general N-component case, one can show that the solution of the ORPA is equivalent to the minimization of a suitably chosen functional with respect to variations of the direct correlation functions. To illustrate the method, we study a polydisperse system of square-well particles; extension to other hard-core or soft-core systems is straightforward.

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