Modelling radiation effects in solids with two-temperature molecular dynamics

The ability to predict the structural modifications of materials resulting from a broad range of irradiation scenarios would have a positive impact on many fields of science and technology. Established techniques for modelling large atomic systems, such as classical molecular dynamics, are limited by the neglect of the electronic degrees of freedom which restricts their application to irradiation events that primarily interact with atomic nuclei. Ab initio methods, on the other hand, include electronic degrees of freedom, but the requisite computational costs restrict their application to relatively small systems. Recent methodological developments aimed at overcoming some of these limitations are based on methods that couple atomistic models to a continuum model for the electronic energy, where energy is exchanged between the nuclei and electrons via electronic stopping and electron-phonon coupling mechanisms. Such two-temperature molecular dynamics models, as they are known, make it practicable to simulate the effects of electronic excitations on systems with millions, or even hundreds of millions, of atoms. They have been used to study laser irradiation of metallic films, swift heavy ion irradiation of metals and semiconductors, and moderately high ion irradiation of metals. In this review we describe the two-temperature molecular dynamics methodology and the various practical considerations required for its implementation. We provide example applications of the model to multiple irradiation scenarios that accommodate electronic excitations. We also describe the challenges of including the effects of the modification of the interatomic interactions, due to the excitation of electrons, in the simulations and how these challenges can be overcome

Computational insight into the molecular mechanisms that control the growth of inorganic crystals

After billions of years of evolution, nature has developed mechanisms for controlling the growth and assembly of materials right down to the nanoscale, an achievement that materials scientists hope to mimic. However, the underlying processes are extremely complex and depend on subtle behaviour at the molecular scale. In contrast to experimental methods, computer simulations can achieve the molecular resolution needed to investigate these mechanisms, and can therefore offer unique insight. Indeed, this dissertation employs a variety of state-of-the-art computational methodologies to investigate the molecular processes by which calcite, the most abundant biomineral on earth, grows, in addition to the role played by surfactants in soft templating technologically important inorganic materials. Microsecond-long simulations are performed to reveal the behaviour of individual ions in the vicinity of calcite steps, providing new insight into the mechanisms responsible for kink nucleation. Rare event methodologies are then used to study the dissolution process of kink sites in calcite crystals. It is discovered that this particular mineralisation process is too complex to be tamed by computational methods, which has far-reaching consequences for the development of highly predictive models of mineralisation. A coarse-grained model for calcite precipitation is presented that displays the ability to connect molecular processes with both the kinetic and morphological characters of a crystal. However, the simulation is found to conflict with experimental observations regarding the dependence of step velocity on step length. The implication being that present models are unable to correctly describe step pinning, which is a major limitation. Lastly, the role of surfactants in templating crystal growth via two very different mechanisms is investigated. In the one case, polymorph and orientation selection by self-assembled monolayers; and in the other, oriented heterogeneous nucleation of mesoporous organosilicas

Diagnosis and Decision-Making in Telemedicine

This article provides an analysis of the skills that health professionals and patients employ in reaching diagnosis and decision-making in telemedicine consultations. As governmental priorities continue to emphasize patient involvement in the management of their disease, there is an increasing need to accurately capture the provider–patient interactions in clinical encounters. Drawing on conversation analysis of 10 video-mediated consultations in 3 National Health Service settings in England, this study examines the interaction between patients, General Practitioner (GPs), nurses, and consultants during diagnosis and decision-making, with the aim to identify the range of skills that participants use in the process and capture the interprofessional communication and patient involvement in the diagnosis and decision-making phases of telemedicine consultations. The analysis shows that teleconsultations enhance collaborative working among professionals and enable GPs and nurses to develop their skills and actively participate in diagnosis and decision-making by contributing primary care–specific knowledge to the consultation. However, interprofessional interaction may result in limited patient involvement in decisionmaking. The findings of this study can be used to inform training programs in telemedicine that focus on the development of effective skills for professionals and the provision of information to patients

The effect of surface topography on the micellisation of hexadecyltrimethylammonium chloride at the silicon-aqueous interface

Amphiphilic aggregation at solid-liquid interfaces can generate mesostructured micelles that can serve as soft templates. In this study we have simulated the self-assembly of hexadecyltrimethylammonium chloride (C16TAC) surfactants at the Si(1 0 0)- and Si(1 1 1)-aqueous interfaces. The surfactants are found to form semicylindrical micelles on Si(1 0 0) but hemispherical micelles on Si(1 1 1). This difference in micelle structure is shown to be a consequence of the starkly different surface topographies that result from the reconstruction of the two silicon surfaces, and reveals that micelle structure can be governed by epitaxial matching even with non-polar substrates

Crystallisation rates of calcite from an amorphous precursor in confinement

Using molecular dynamics, we simulate the crystallisation of calcite from an amorphous calcium carbonate precursor within the confines of a cylindrical potential. The crystallisation rates of various low-index calcite surfaces are measured. A notable inhibition in the growth of the (00.1) surface is observed and a mechanism relating to the rotation of the carbonate ions is proposed to explain it

Structure and Orientation of MDBA Self-Assembled Monolayers and Their Interaction with Calcite: A Molecular Dynamics Study

We investigate the structure of self-assembled monolayers produced by different mercaptodecyl benzoic acid isomers using molecular dynamics. We examine their interaction with water and calcium carbonate, and analyse the headgroup orientations in each case. We compare our results to experimental findings and propose an explanation for their observed hydrophilicity, as well as for the preferentially oriented growth of calcite on one of the isomers

Calcite Kinetics for Spiral Growth and Two-Dimensional Nucleation

[Image: see text] Calcite crystals grow by means of molecular steps that develop on {10.4} faces. These steps can arise stochastically via two-dimensional (2D) nucleation or emerge steadily from dislocations to form spiral hillocks. Here, we determine the kinetics of these two growth mechanisms as a function of supersaturation. We show that calcite crystals larger than ∼1 μm favor spiral growth over 2D nucleation, irrespective of the supersaturation. Spirals prevail beyond this length scale because slow boundary layer diffusion creates a low surface supersaturation that favors the spiral mechanism. Sub-micron crystals favor 2D nucleation at high supersaturations, although diffusion can still limit the growth of nanoscopic crystals. Additives can change the dominant mechanism by impeding spiral growth or by directly promoting 2D nucleation

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/