104 research outputs found
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 . These
properties give rise for Knight models to an ergodicity breaking transition at
: at and above 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
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