Magic number behavior for heat capacities of medium sized classical Lennard-Jones clusters

Monte Carlo methods were used to calculate heat capacities as functions of temperature for classical atomic clusters of aggregate sizes $25 \leq N \leq 60$ that were bound by pairwise Lennard-Jones potentials. The parallel tempering method was used to overcome convergence difficulties due to quasiergodicity in the solid-liquid phase-change regions. All of the clusters studied had pronounced peaks in their heat capacity curves, most of which corresponded to their solid-liquid phase-change regions. The heat capacity peak height and location exhibited two general trends as functions of cluster size: for $N = 25$ to 36, the peak temperature slowly increased, while the peak height slowly decreased, disappearing by $N = 37$; for $N = 30$, a very small secondary peak at very low temperature emerged and quickly increased in size and temperature as $N$ increased, becoming the dominant peak by $N = 36$. Superimposed on these general trends were smaller fluctuations in the peak heights that corresponded to ``magic number'' behavior, with local maxima found at $N = 36, 39, 43, 46$ and 49, and the largest peak found at $N = 55$. These magic numbers were a subset of the magic numbers found for other cluster properties, and can be largely understood in terms of the clusters' underlying geometries. Further insights into the melting behavior of these clusters were obtained from quench studies and by examining rms bond length fluctuations.Comment: 15 pages, 17 figures (PDF format

A computational study of 13-atom Ne-Ar cluster heat capacities

Heat capacity curves as functions of temperature were calculated using Monte Carlo methods for the series of Ne_(13-n)Ar_n clusters (0 <= n <= 13). The clusters were modeled classically using pairwise additive Lennard-Jones potentials. The J-walking (or jump-walking) method was used to overcome systematic errors due to quasiergodicity. Substantial discrepancies between the J-walking results and those obtained using standard Metropolis methods were found. Results obtained using the atom-exchange method, another Monte Carlo variant for multi-component systems, also did not compare well with the J-walker results. Quench studies were done to investigate the clusters' potential energy surfaces. Only those Ne-Ar clusters consisting predominately of either one or the other component had lowest energy isomers having the icosahedral-like symmetry typical of homogeneous 13-atom rare gas clusters; non-icosahedral structures dominated the lowest-energy isomers for the other clusters. This resulted in heat capacity curves that were very much different than that of their homogeneous counterpart. Evidence for coexistence behavior different than that seen in homogenous clusters is also presented.Comment: 45 pages, 11 Figures, figures in .gif format files. Journal of Chemical Physics, AIP ID number 513730JC

A Computational Study of Thirteen-atom Ar-Kr Cluster Heat Capacities

Heat capacity curves as functions of temperature were calculated using Monte Carlo methods for the series of Ar_{13-n}Kr_n clusters (0 <= n <= 13). The clusters were modeled classically using pairwise additive Lennard-Jones potentials. J-walking (or jump-walking) was used to overcome convergence difficulties due to quasiergodicity present in the solid-liquid transition regions, as well as in the very low temperature regions where heat capacity anomalies arising from permutational isomers were observed. Substantial discrepancies between the J-walking results and the results obtained using standard Metropolis Monte Carlo methods were found. Results obtained using the atom-exchange method, another Monte Carlo variant designed for multi-component systems, were mostly similar to the J-walker results. Quench studies were also done to investigate the clusters' potential energy surfaces; in each case, the lowest energy isomer had an icosahedral-like symmetry typical of homogeneous thirteen-atom rare gas clusters, with an Ar atom being the central atom.Comment: 46 pages, 13 Figures combined in 2 .gif files, Journal of Chemical Physics, AIP ID number 508646JC

Reducing Quasi-Ergodic Behavior in Monte Carlo Simulations by J-Walking: Applications to Atomic Clusters

A method is introduced that is easy to implement and greatly reduces the systematic error resulting from quasi-ergodicity, or incomplete sampling of configuration space, in Monte Carlo simulations of systems containing large potential energy barriers. The method makes possible the jumping over these barriers by couplingn the usual Metropolis sampling to the Boltzmann distribution generated by another random walker at a higher temperature. the basic techniques are illustrated on some simple classical systems, beginning for heuristic purposes with a simple one-dimensional double well potential based on a quartic polynomial. the method\u27s suitability for typical multidimensional Monte Carlo systems is demonstrated by extending the double well potential to several dimensions, and then by applying the method to a multiparticle cluster system consisting of argon atoms bound by pairwise Lennard-Jones potentials. Remarkable improvements are demonstrated in the convergence rate for the cluster configuration energy, and especially for the heat capacity, at temperatures near the cluster melting transition region. Moreover, these improvements can be obtained even in the worst-case scenario where clusters are initialized from random configurations

A Micro-Analytical Technique for Determination of Aluminum in Aqueous Solutions

A flow-cell colorimetric technique has been developed by which Al concentrations of 0.0003to 0.1 wt. percent can be analyzed in one-microliter fluid samples.An A1-complexing reagent (Ferron) continuously flowing through teflon capillary tubing is spiked with one-microliter aliquots of solutions containing A1. The sample reacts with the reagent and subsequently passes through a microcolorimeter. Measurement of less than one nanomole A1 is possible by comparing the integrated absorbance of unknown samples with standard solutions

Restriction of Late Cerebral Cortical Progenitors to an Upper-Layer Fate

AbstractEarly in development, neural progenitors in cerebral cortex normally produce neurons of several layers during successive cell divisions. The laminar fate of their daughters depends on environmental cues encountered just before mitosis. At the close of neurogenesis, however, cortical progenitors normally produce neurons destined only for the upper layers. To assess the developmental potential of these cells, upper-layer progenitors were transplanted into the cerebral cortex of younger hosts, in which deep-layer neurons were being generated. These studies reveal that late cortical progenitors are not competent to generate deep-layer neurons and are instead restricted to producing the upper layers

Encouraging practitioners in infection prevention and control to publish: a cross-sectional survey

Aim: The aim of this cross-sectional survey was to determine the views of infection prevention and control practitioners (IPCPs) on publishing research. Methods: A convenience sample was obtained by approaching delegates at the 2015 Infection Prevention Society conference and data was captured via a hand-held electronic device. Findings: Of the 79 respondents most (83%) read Journal of Infection Prevention (JIP) and found it useful for informing their practice (72%). However, most (91%) had never published in JIP, and less than half (40%) published elsewhere. The main barrier to publication was not having work suitable for publication (38%). Support (37%), training in writing for publication (10%) and time (9%) were considered to be important facilitators in encouraging respondents to publish. Discussion: Strategies that support IPCPs in developing their writing skills may encourage more IPCPs to disseminate evidence to support best practice by publishing their work in peer reviewed journals

Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises’ applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.info:eu-repo/semantics/acceptedVersio