SAFT-γ force field for the simulation of molecular fluids: 8. hetero-group coarse-grained models of perfluoroalkylalkanes assessed with new vapour-liquid interfacial tension data

The air-liquid interfacial behaviour of linear perfluoroalkylalkanes (PFAAs) is reported through a combined experimental and computer simulation study. The surface tensions of seven liquid PFAAs (perfluorobutylethane, F4H2; perfluorobutylpentane, F4H5; perfluorobutylhexane, F4H6, perfluorobutyloctane, F4H8; perfluorohexylethane, F6H2; perfluorohexylhexane, F6H6; and perfluorohexyloctane, F6H8) are experimentally determined over a wide temperature range (276 to 350 K). The corresponding surface thermodynamic properties and the critical temperatures of the studied compounds are estimated from the temperature dependence of the surface tension. Experimental density and vapour pressure data are employed to parameterize a generic heteronuclear coarse-grained intermolecular potential of the SAFT- γ family for PFAAs. The resulting force field is used in direct molecular dynamics simulations to predict with quantitative agreement the experimental tensions and to explore the conformations of the molecules in the interfacial region revealing a preferential alignment of the PFAA molecules towards the interface and an enrichment of the perfluoro-groups at the outer interface region

Jet Modification in a Brick of QGP Matter

We have implemented the LPM effect into a microscopic transport model with partonic degrees of freedom by following the algorithm of Zapp & Wiedemann. The Landau-Pomeranchuk-Migdal (LPM) effect is a quantum interference process that modifies the emission of radiation in the presence of a dense medium. In QCD this results in a quadratic length dependence for radiative energy loss. This is an important effect for the modification of jets by their passage through the QGP. We verify the leading parton energy loss in the model against the leading order Baier-Dokshitzer-Mueller-Peigne-Schiff-Zakharov (BDMPS-Z) result. We apply our model to the recent observations of the modification of di-jets at the LHC.Comment: Presented at Panic 1

The effect of precipitation and application rate on dicyandiamide persistence and efficiency in two Irish grassland soils

peer-reviewedThe nitrification inhibitor dicyandiamide (DCD) has had variable success in reducing nitrate (NO3-) leaching and nitrous oxide (N2O) emissions from soils receiving nitrogen (N) fertilisers. Factors such as soil type, temperature and moisture have been linked to the variable efficacy of DCD. Since DCD is water soluble it can be leached from the rooting zone where it is intended to inhibit nitrification. Intact soil columns (15 cm diameter by 35 cm long) were taken from luvic gleysol and haplic cambisol grassland sites and placed in growth chambers. DCD was applied at 15 or 30 kg DCD ha-1, with high or low precipitation. Leaching of DCD, mineral N and the residual soil DCD concentrations were determined over eight weeks High precipitation increased DCD in leachate and decreased recovery in soil. A soil x DCD rate interaction was detected for the DCD unaccounted (proxy for degraded DCD). In the cambisol degradation of DCD was high (circa 81%) and unaffected by DCD rate. In contrast DCD degradation in the gleysol was lower and differentially affected by rate, 67 and 46% for the 15 and 30 kg ha-1 treatments, respectively. Differences DCD degradation rates between soils may be related to differences in organic matter content and associated microbiological activity. Variable degradation rates of DCD in soil, unrelated to temperature or moisture, may contribute to varying DCD efficacy. Soil properties should be considered when tailoring DCD strategies for improving nitrogen use efficiency and crop yields, through the reduction of reactive nitrogen loss.This research was financially supported under the National Development Plan, through the Research Stimulus Fund, administered by the Department of Agriculture, Food and the Marine under grants 07519 and 07545

Development of thermodynamically consistent machine-learning equations of state: Application to the Mie fluid

A procedure for deriving thermodynamically consistent data-driven equations of state (EoS) for fluids is presented. The method is based on fitting the Helmholtz free energy using artificial neural networks to obtain a closed-form relationship between the thermophysical properties of fluids (FE-ANN EoS). As a proof-of-concept, an FE-ANN EoS is developed for the Mie fluids, starting from a database obtained by classical molecular dynamics simulations. The FE-ANN EoS is trained using first- (pressure and internal energy) and second-order (e.g., heat capacities, Joule–Thomson coefficients) derivative data. Additional constraints ensure that the data-driven model fulfills thermodynamically consistent limits and behavior. The results for the FE-ANN EoS are shown to be as accurate as the best available analytical model while being developed in a fraction of the time. The robustness of the “digital” equation of state is exemplified by computing physical behavior it has not been trained on, for example, fluid phase equilibria. Furthermore, the model’s internal consistency is successfully assessed using Brown’s characteristic curves