Irene Yarovsky

Computer simulation of structure and properties of crosslinked polymers: application to epoxy resins ☆

In this work, a methodology has been developed for construction of atomistic models of crosslinked polymer networks. The methodology has been applied to low molecular weight water soluble epoxy resins crosslinked with different curing agents that are being considered for use as a primer coating on steel. The simulations allowed the crosslink density and the amount of free crosslinking sites in the coatings to be predicted. Shrinkage of the resin upon curing was reproduced by the simulation. In addition, the barrier properties of the model coatings were estimated. The interface between an inorganic substrate and cured epoxy resin has been constructed and the strength and molecular mechanisms of adhesion have been revealed. The developed methodology has a potential to significantly impact on the design and development of new coatings with improved barrier and adhesion properties.

Ordering surfaces on the nanoscale: implications for protein adsorption.

Monolayer-protected metal nanoparticles (MPMNs) are a newly discovered class of nanoparticles with an ordered, striped domain structure that can be readily manipulated by altering the ratio of the hydrophobic to hydrophilic ligands. This property makes them uniquely suited to systematic studies of the role of nanostructuring on biomolecule adsorption, a phenomenon of paramount importance in biomaterials design. In this work, we examine the interaction of the simple, globular protein cytochrome C (Cyt C) with MPMN surfaces using experimental protein assays and computational molecular dynamics simulations. Experimental assays revealed that adsorption of Cyt C generally increased with increasing surface polar ligand content, indicative of the dominance of hydrophilic interactions in Cyt C-MPMN binding. Protein-surface adsorption enthalpies calculated from computational simulations employing rigid-backbone coarse-grained Cyt C and MPMN models indicate a monotonic increase in adsorption enthalpy with respect to MPMN surface polarity. These results are in qualitative agreement with experimental results and suggest that Cyt C does not undergo significant structural disruption upon adsorption to MPMN surfaces. Coarse-grained and atomistic simulations furthermore elucidated the important role of lysine in facilitating Cyt C adsorption to MPMN surfaces. The amphipathic character of the lysine side chain enables it to form close contacts with both polar and nonpolar surface ligands simultaneously, rendering it especially important for interactions with surfaces composed of adjacent nanoscale chemical domains. The importance of these structural characteristics of lysine suggests that proteins may be engineered to specifically interact with nanomaterials by targeted incorporation of unnatural amino acids possessing dual affinity to differing chemical motifs.

Nanomaterials in biological environment: a review of computer modelling studies

Nanotechnology is set to impact a vast range of fields, including computer science, materials technology, engineering/manufacturing and medicine. As nanotechnology grows so does exposure to nanostructured materials, thus investigation of the effects of nanomaterials on biological systems is paramount. Computational techniques can allow investigation of these systems at the nanoscale, providing insight into otherwise unexaminable properties, related to both the intentional and unintentional effects of nanomaterials. Herein, we review the current literature involving computational modelling of nanoparticles and biological systems. This literature has highlighted the common modes in which nanostructured materials interact with biological molecules such as membranes, peptides/proteins and DNA. Hydrophobic interactions are the most favoured, with π-stacking of the aromatic side-chains common when binding to a carbonaceous nanoparticle or surface. van der Waals forces are found to dominate in the insertion process of DNA molecules into carbon nanotubes. Generally, nanoparticles have been observed to disrupt the tertiary structure of proteins due to the curvature and atomic arrangement of the particle surface. Many hydrophobic nanoparticles are found to be able to transverse a lipid membrane, with some nanoparticles even causing mechanical damage to the membrane, thus potentially leading to cytotoxic effects. Current computational techniques have revealed how some nanoparticles interact with biological systems. However, further research is required to determine both useful applications and possible cytotoxic effects that nanoparticles may have on DNA, protein and membrane structure and function within biosystems.

Hybrid approach for generating realistic amorphous carbon structure using metropolis and reverse Monte Carlo

An improved method for the modelling of carbon structures based on a hybrid reverse Monte Carlo (HRMC) method is presented. This algorithm incorporates an accurate environment dependent interaction potential (EDIP) in conjunction with the commonly used constraints derived from experimental data. In this work, we compare this new method with other modelling results for a small system of 2.9 g/cc amorphous carbon. We find that the new approach greatly improves the structural description, alleviating the common problem in standard reverse Monte Carlo method (RMC) of generating structures with a high proportion of unphysical small rings. The advantage of our method is that larger systems can now be modelled, allowing the incorporation of mesoscopic scale features.

Application of numerical basis sets to hydrogen bonded systems: A density functional theory study

We have investigated and compared the ability of numerical and Gaussian-type basis sets to accurately describe the geometries and binding energies of a selection of hydrogen bonded systems that are well studied theoretically and experimentally. The numerical basis sets produced accurate results for geometric parameters but tended to overestimate binding energies. However, a comparison of the time taken to optimize phosphinic acid dimer, the largest complex considered in this study, shows that calculations using numerical basis sets offer a definitive advantage where geometry optimization of large systems is required.

Modular assembly of superstructures from polyphenol-functionalized building blocks

The organized assembly of particles into superstructures is typically governed by specific molecular interactions or external directing factors associated with the particle building blocks, both of which are particle-dependent. These superstructures are of interest to a variety of fields because of their distinct mechanical, electronic, magnetic and optical properties. Here, we establish a facile route to a diverse range of superstructures based on the polyphenol surface-functionalization of micro- and nanoparticles, nanowires, nanosheets, nanocubes and even cells. This strategy can be used to access a large number of modularly assembled superstructures, including core-satellite, hollow and hierarchically organized supraparticles. Colloidal-probe atomic force microscopy and molecular dynamics simulations provide detailed insights into the role of surface functionalization and how this facilitates superstructure construction. Our work provides a platform for the rapid generation of superstructured assemblies across a wide range of length scales, from nanometres to centimetres.

Systematic Comparison of Empirical Forcefields for Molecular Dynamic Simulation of Insulin

The use of atomistic simulation methodologies based on empirical forcefields has enhanced our understanding of many physical processes governing protein structure and dynamics. However, the forcefields used in classical modeling studies are often designed for a particular class of proteins and rely on continuous improvement and validation by comparison of simulations with experimental data. We present a comprehensive comparison of five popular forcefields for simulating insulin. The effect of each forcefield on the conformational evolution and structural properties of the peptide is analyzed in detail and compared with available experimental results. In this study we observed that different forcefields favor different structural trends. However, the all-atom forcefield CHARMM27 and the united-atom forcefield GROMOS 43A1 delivered the best representation of the experimentally observed dynamic behavior of chain B of insulin.

Atomistic simulation of interfaces in materials: theory and applications

The theoretical background, methodology and some applications of atomistic simulation of interfaces in materials are described in this paper. Interfaces between crystalline solids and polymers as well as between two polymers can be simulated using the methods described. Applications include various interfaces in a multilayered coated metal system. The methodology enables such properties as the work of adhesion, interfacial stability, degree of curing in polymers and permeability to small molecules to be predicted. In addition, interfacial structure and molecular mechanisms of adhesion and barrier performance of coatings can be revealed.

Structural analysis of carbonaceous solids using an adapted reverse Monte Carlo algorithm

We present microstructural analysis of a disordered carbonaceous solid using simulations that employ a modified reverse Monte Carlo (RMC) algorithm. This algorithm incorporates an accurate environment dependent interaction potential (EDIP) in addition to commonly used constraints derived from experimental data, such as the sp2/sp3 bonding ratio. Our approach improves the microstructural description for carbon, alleviating the common problem in standard RMC of generating structures with large proportions of highly strained and physically unreasonable small rings. We also compare the electron diffraction data used in the modified RMC algorithm to our recent results from a neutron diffraction investigation of the carbonaceous material under consideration.

Performance of Numerical Basis Set DFT for Aluminum Clusters

We have investigated and compared the ability of numerical and Gaussian-type basis sets combined with density functional theory (DFT) to accurately describe the geometries, binding energies, and electronic properties of aluminum clusters, Al12XHn (X = Al, Si; n = 0, 1, 2). DFT results are compared against high-level benchmark calculations and experimental data where available. Properties compared include geometries, binding energies, ionization potentials, electron affinities, and HOMO-LUMO gaps. Generally, the PBE functional with the double numerical basis set with polarization (DNP) performs very well against experiment and the analytical basis sets for considerably less computational expense.