Influence of external magnetic fields on growth of alloy nanoclusters

Kinetic Monte Carlo simulations are performed to study the influence of external magnetic fields on the growth of magnetic fcc binary alloy nanoclusters with perpendicular magnetic anisotropy. The underlying kinetic model is designed to describe essential structural and magnetic properties of CoPt_3-type clusters grown on a weakly interacting substrate through molecular beam epitaxy. The results suggest that perpendicular magnetic anisotropy can be enhanced when the field is applied during growth. For equilibrium bulk systems a significant shift of the onset temperature for L1_2 ordering is found, in agreement with predictions from Landau theory. Stronger field induced effects can be expected for magnetic fcc-alloys undergoing L1_0 ordering.Comment: 10 pages, 3 figure

Time-Dependent Density Functional Theory for Driven Lattice Gas Systems with Interactions

We present a new method to describe the kinetics of driven lattice gases with particle-particle interactions beyond hard-core exclusions. The method is based on the time-dependent density functional theory for lattice systems and allows one to set up closed evolution equations for mean site occupation numbers in a systematic manner. Application of the method to a totally asymmetric site exclusion process with nearest-neighbor interactions yields predictions for the current-density relation in the bulk, the phase diagram of non-equilibrium steady states and the time evolution of density profiles that are in good agreement with results from kinetic Monte Carlo simulations.Comment: 11 pages, 3 figure

Discrimination of biofilm samples using pattern recognition techniques

Biofilms are complex aggregates formed by microorganisms such as bacteria, fungi and algae, which grow at the interfaces between water and natural or artificial materials. They are actively involved in processes of sorption and desorption of metal ions in water and reflect the environmental conditions in the recent past. Therefore, biofilms can be used as bioindicators of water quality. The goal of this study was to determine whether the biofilms, developed in different aquatic systems, could be successfully discriminated using data on their elemental compositions. Biofilms were grown on natural or polycarbonate materials in flowing water, standing water and seawater bodies. Using an unsupervised technique such as principal component analysis (PCA) and several supervised methods like classification and regression trees (CART), discriminant partial least squares regression (DPLS) and uninformative variable elimination–DPLS (UVE-DPLS), we could confirm the uniqueness of sea biofilms and make a distinction between flowing water and standing water biofilms. The CART, DPLS and UVE-DPLS discriminant models were validated with an independent test set selected either by the Kennard and Stone method or the duplex algorithm. The best model was obtained from CART with 100% correct classification rate for the test set designed by the Kennard and Stone algorithm. With CART, one variable describing the Mg content in the biofilm water phase was found to be important for the discrimination of flowing water and standing water biofilms

Energetics and performance of a microscopic heat engine based on exact calculations of work and heat distributions

We investigate a microscopic motor based on an externally controlled two-level system. One cycle of the motor operation consists of two strokes. Within each stroke, the two-level system is in contact with a given thermal bath and its energy levels are driven with a constant rate. The time evolution of the occupation probabilities of the two states are controlled by one rate equation and represent the system's response with respect to the external driving. We give the exact solution of the rate equation for the limit cycle and discuss the emerging thermodynamics: the work done on the environment, the heat exchanged with the baths, the entropy production, the motor's efficiency, and the power output. Furthermore we introduce an augmented stochastic process which reflects, at a given time, both the occupation probabilities for the two states and the time spent in the individual states during the previous evolution. The exact calculation of the evolution operator for the augmented process allows us to discuss in detail the probability density for the performed work during the limit cycle. In the strongly irreversible regime, the density exhibits important qualitative differences with respect to the more common Gaussian shape in the regime of weak irreversibility.Comment: 21 pages, 7 figure

Nonlinear hopping transport in ring systems and open channels

We study the nonlinear hopping transport in one-dimensional rings and open channels. Analytical results are derived for the stationary current response to a constant bias without assuming any specific coupling to the external fields. It is shown that anomalous large effective jump lengths, as observed in recent experiments by taking the ratio of the third order nonlinear and the linear conductivity, can occur already in ordered systems. Rectification effects due to site energy disorder in ring systems are expected to become irrelevant for large system sizes. In open channels in contrast, rectification effects occur already for disorder in the jump barriers and do not vanish in the thermodynamic limit. Numerical solutions for a sinusoidal bias show that the ring system provides a good description for the transport behavior in the open channel for intermediate and high frequencies. For low frequencies temporal variations in the mean particle number have to be taken into account in the open channel, which cannot be captured in the more simple ring model.Comment: 25 pages, 7 figure

Jamming percolation and glassy dynamics

We present a detailed physical analysis of the dynamical glass-jamming transition which occurs for the so called Knight models recently introduced and analyzed in a joint work with D.S.Fisher \cite{letterTBF}. Furthermore, we review some of our previous works on Kinetically Constrained Models. The Knights models correspond to a new class of kinetically constrained models which provide the first example of finite dimensional models with an ideal glass-jamming transition. This is due to the underlying percolation transition of particles which are mutually blocked by the constraints. This jamming percolation has unconventional features: it is discontinuous (i.e. the percolating cluster is compact at the transition) and the typical size of the clusters diverges faster than any power law when $\rho\nearrow\rho_c$. These properties give rise for Knight models to an ergodicity breaking transition at $\rho_c$: at and above $\rho_{c}$ a finite fraction of the system is frozen. In turn, this finite jump in the density of frozen sites leads to a two step relaxation for dynamic correlations in the unjammed phase, analogous to that of glass forming liquids. Also, due to the faster than power law divergence of the dynamical correlation length, relaxation times diverge in a way similar to the Vogel-Fulcher law.Comment: Submitted to the special issue of Journal of Statistical Physics on Spin glasses and related topic

Spatial regularity of InAs-GaAs quantum dots: quantifying the dependence of lateral ordering on growth rate.

The lateral ordering of arrays of self-assembled InAs-GaAs quantum dots (QDs) has been quantified as a function of growth rate, using the Hopkins-Skellam index (HSI). Coherent QD arrays have a spatial distribution which is neither random nor ordered, but intermediate. The lateral ordering improves as the growth rate is increased and can be explained by more spatially regular nucleation as the QD density increases. By contrast, large and irregular 3D islands are distributed randomly on the surface. This is consistent with a random selection of the mature QDs relaxing by dislocation nucleation at a later stage in the growth, independently of each QD's surroundings. In addition we explore the statistical variability of the HSI as a function of the number N of spatial points analysed, and we recommend N > 10(3) to reliably distinguish random from ordered arrays

Soil Contamination Interpretation by the Use of Monitoring Data Analysis

The presented study deals with the interpretation of soil quality monitoring data using hierarchical cluster analysis (HCA) and principal components analysis (PCA). Both statistical methods contributed to the correct data classification and projection of the surface (0–20 cm) and subsurface (20–40 cm) soil layers of 36 sampling sites in the region of Burgas, Bulgaria. Clustering of the variables led to formation of four significant clusters corresponding to possible sources defining the soil quality like agricultural activity, industrial impact, fertilizing, etc. Two major clusters were found to explain the sampling site locations according to soil composition—one cluster for coastal and mountain sites and another—for typical rural and industrial sites. Analogous results were obtained by the use of PCA. The advantage of the latter was the opportunity to offer more quantitative interpretation of the role of identified soil quality sources by the level of explained total variance. The score plots and the dendrogram of the sampling sites indicated a relative spatial homogeneity according to geographical location and soil layer depth. The high-risk areas and pollution profiles were detected and visualized using surface maps based on Kriging algorithm

Simulated Atmospheric N Deposition Alters Fungal Community Composition and Suppresses Ligninolytic Gene Expression in a Northern Hardwood Forest

High levels of atmospheric nitrogen (N) deposition may result in greater terrestrial carbon (C) storage. In a northern hardwood ecosystem, exposure to over a decade of simulated N deposition increased C storage in soil by slowing litter decay rates, rather than increasing detrital inputs. To understand the mechanisms underlying this response, we focused on the saprotrophic fungal community residing in the forest floor and employed molecular genetic approaches to determine if the slower decomposition rates resulted from down-regulation of the transcription of key lignocellulolytic genes, by a change in fungal community composition, or by a combination of the two mechanisms. Our results indicate that across four Acer-dominated forest stands spanning a 500-km transect, community-scale expression of the cellulolytic gene cbhI under elevated N deposition did not differ significantly from that under ambient levels of N deposition. In contrast, expression of the ligninolytic gene lcc was significantly down-regulated by a factor of 2–4 fold relative to its expression under ambient N deposition. Fungal community composition was examined at the most southerly of the four sites, in which consistently lower levels of cbhI and lcc gene expression were observed over a two-year period. We recovered 19 basidiomycete and 28 ascomycete rDNA 28S operational taxonomic units; Athelia, Sistotrema, Ceratobasidium and Ceratosebacina taxa dominated the basidiomycete assemblage, and Leotiomycetes dominated the ascomycetes. Simulated N deposition increased the proportion of basidiomycete sequences recovered from forest floor, whereas the proportion of ascomycetes in the community was significantly lower under elevated N deposition. Our results suggest that chronic atmospheric N deposition may lower decomposition rates through a combination of reduced expression of ligninolytic genes such as lcc, and compositional changes in the fungal community