On the Calculation of Solid-Fluid Contact Angles from Molecular Dynamics

A methodology for the determination of the solid-fluid contact angle, to be employed within molecular dynamics (MD) simulations, is developed and systematically applied. The calculation of the contact angle of a fluid drop on a given surface, averaged over an equilibrated MD trajectory, is divided in three main steps: (i) the determination of the fluid molecules that constitute the interface, (ii) the treatment of the interfacial molecules as a point cloud data set to define a geometric surface, using surface meshing techniques to compute the surface normals from the mesh, (iii) the collection and averaging of the interface normals collected from the post-processing of the MD trajectory. The average vector thus found is used to calculate the Cassie contact angle (i.e., the arccosine of the averaged normal z-component). As an example we explore the effect of the size of a drop of water on the observed solid-fluid contact angle. A single coarse-grained bead representing two water molecules and parameterized using the SAFT-γ Mie equation of state (EoS) is employed, meanwhile the solid surfaces are mimicked using integrated potentials. The contact angle is seen to be a strong function of the system size for small nano-droplets. The thermodynamic limit, corresponding to the infinite size (macroscopic) drop is only truly recovered when using an excess of half a million water coarse-grained beads and/or a drop radius of over 26 nm

Optimization of Cortisol-Selective Molecularly Imprinted Polymers Enabled by Molecular Dynamics Simulations

Today, we heavily rely on technology and increasingly utilize it to monitor our own health. The identification of sensitive, accurate biosensors that are capable of real-time cortisol analysis is one important potential feature for these technologies to aid us in the maintenance of our physical and mental wellbeing. Detection and quantification of cortisol, a well-known stress biomarker present in sweat, offers a noninvasive and potentially real-time method for monitoring anxiety. Molecularly imprinted polymers are attractive candidates for cortisol recognition elements in such devices as they can selectively rebind a targeted template molecule. However, mechanisms of imprinting and subsequent rebinding depend on the choice and composition of the prepolymerization mixture where the molecular interactions between the template, functional monomer, cross-linker, and solvent molecules are not fully understood. Here, we report the synthesis and evaluation of a molecularly imprinted polymer selective for cortisol detection. Molecular dynamics simulations were used to investigate the interactions between all components in the prepolymerization mixture of the as-synthesized molecularly imprinted polymer. Varying the component ratio of the prepolymerization mixture indicates that the number of cross-linker molecules relative to the template impacts the quality of imprinting. It was determined that a component ratio of 1:6:30 of cortisol, methacrylic acid, and ethylene glycol dimethacrylate, respectively, yields the optimal theoretical complexation of cortisol for the polymeric systems investigated. Experimental synthesis and rebinding results demonstrate an imprinting factor of up to 6.45. The trends in cortisol affinity predicted by molecular dynamics simulations of the prepolymerization mixture were also corroborated through experimental analysis of those modeled molecularly imprinted compositions, demonstrating the predictive capabilities of these simulations

N-Doped Fe@CNT for Combined RWGS/FT CO ₂ Hydrogenation

The conversion of CO₂ into chemical fuels represents an attractive route for greenhouse gas emission reductions and renewable energy storage. Iron nanoparticles supported on graphitic carbon materials (e.g., carbon nanotubes (CNTs)) have proven themselves to be effective catalysts for this process. This is due to their stability and ability to support simultaneous reverse water-gas shift (RWGS) and Fischer–Tropsch (FT) catalysis. Typically, these catalytic iron particles are postdoped onto an existing carbon support via wet impregnation. Nitrogen doping of the catalyst support enhances particle–support interactions by providing electron-rich anchoring sites for nanoparticles during wet impregnation. This is typically credited for improving CO₂ conversion and product selectivity in subsequent catalysis. However, the mechanism for RWGS/FT catalysis remains underexplored. Current research places significant emphasis on the importance of enhanced particle–support interactions due to N doping, which may mask further mechanistic effects arising from the presence or absence of nitrogen during CO₂ hydrogenation. Here we report a clear relationship between the presence of nitrogen in the CNT support of an RWGS/FT iron catalyst and significant shifts in the activity and product distribution of the reaction. Particle–support interactions are maximized (and discrepancies between N-doped and pristine support materials are minimized) by incorporating iron and nitrogen directly into the support during synthesis. Reactivity is thus rationalized in terms of the influence of C–N dipoles in the support upon the adsorption properties of CO₂ and CO on the surface rather than improved particle–support interactions. These results show that the direct hydrogenation of CO₂ to hydrocarbons is a potentially viable route to reduce carbon emissions from human activities

Prediction of the water/oil interfacial tension from molecular simulations using the coarse-grained SAFT-γ Mie force field

This work is framed within the Ninth Industrial Fluid Properties Simulation Challenge, with the aim of assessing the capability of molecular simulation methods and force fields to accurately predict the interfacial tension of oil + water mixtures at high temperatures and pressures. The challenge focused on predicting the liquid-liquid interfacial tension of binary mixtures of dodecane + water, toluene + water and a 50:50 (wt%) mixture of dodecane:toluene + water at 1.825 MPa (250 psig) and temperatures from 110 to 170 °C. In our entry for the challenge, we employed coarse-grained intermolecular models parametrized via a top-down technique in which an accurate equation of state is used to link experimentally observed macroscopic properties of fluids with the force-field parameters. The state-of-the-art version of the statistical associating fluid theory (SAFT) for potentials of variable range as reformulated in terms of the Mie potential is employed here. Interfacial tensions are calculated through a direct method, where an elongated simulation cell is sampled through molecular dynamics in the isobaric-isothermal constant area ensemble (NPzzAT). The coarse-grained nature of the force field allows for the accelerated calculation of relatively large systems. The binary interaction parameters that describe the cross-interactions have been obtained in previous works by fitting to interfacial tensions of the constituent binaries at lower pressures and temperatures; these are taken as constant for all conditions and mixtures studied. After disclosure of the challenge results, we observe that the interfacial properties of the mixtures are described with an error of less than 5 mN/m over the whole range of conditions, demonstrating the accuracy and transferability of the top-down SAFT-γ Mie force field approach

Molecular Recognition Effects in Atomistic Models of Imprinted Polymers

In this article we present a model for molecularly imprinted polymers, which considers both complexation processes in the pre-polymerization mixture and adsorption in the imprinted structures within a single consistent framework. As a case study we investigate MAA/EGDMA polymers imprinted with pyrazine and pyrimidine. A polymer imprinted with pyrazine shows substantial selectivity towards pyrazine over pyrimidine, thus exhibiting molecular recognition, whereas the pyrimidine imprinted structure shows no preferential adsorption of the template. Binding sites responsible for the molecular recognition of pyrazine involve one MAA molecule and one EGDMA molecule, forming associations with the two functional groups of the pyrazine molecule. Presence of these specific sites in the pyrazine imprinted system and lack of the analogous sites in the pyrimidine imprinted system is directly linked to the complexation processes in the pre-polymerization solution. These processes are quite different for pyrazine and pyrimidine as a result of both enthalpic and entropic effects

Crystallization processes in bi-component thin film depositions::towards a realistic Kinetic Monte-Carlo simulation

The kinetic Monte Carlo (KMC) method is a powerful and simple tool to simulate the growth of thin films by deposition. However, one of its major drawbacks is the artificial order induced by the use of regular lattices. An algorithm that mimics the crystallization processes in bi-component thin film depositions via a novel KMC approach is presented in this work. This new algorithm, named GEM-CA (Geometrical Energy Modification-Crystallization Algorithm), modifies the hopping energy barrier depending on the geometrical configuration of the atoms surrounding one particular position. The novel approach allows obtaining amorphous, crystalline and mixed structures (i.e. nanocomposites), depending solely on the synthesis parameters. In addition, we have developed a method for the analysis of deposited structures based on their degree of order. The influence of different deposition parameters such as temperature or composition is discussed in detail. GEM-CA reproduces experimentally observed trends of bi-component film deposition.</p

Characterization of metformin/functional groups associates in aqueous system.

Characterization of metformin/functional groups associates in aqueous system