Adsorption of Lennard-Jones fluids in carbon slit pores of a finite length. A computer simulation study

The adsorption of simple Lennard-Jones fluids in a carbon slit pore of finite length was studied with Canonical Ensemble (NVT) and Gibbs Ensemble Monte Carlo Simulations (GEMC). The Canonical Ensemble was a collection of cubic simulation boxes in which a finite pore resides, while the Gibbs Ensemble was that of the pore space of the finite pore. Argon was used as a model for Lennard-Jones fluids, while the adsorbent was modelled as a finite carbon slit pore whose two walls were composed of three graphene layers with carbon atoms arranged in a hexagonal pattern. The Lennard-Jones (LJ) 12-6 potential model was used to compute the interaction energy between two fluid particles, and also between a fluid particle and a carbon atom. Argon adsorption isotherms were obtained at 87.3 K for pore widths of 1.0, 1.5 and 2.0 nm using both Canonical and Gibbs Ensembles. These results were compared with isotherms obtained with corresponding infinite pores using Grand Canonical Ensembles. The effects of the number of cycles necessary to reach equilibrium, the initial allocation of particles, the displacement step and the simulation box size were particularly investigated in the Monte Carlo simulation with Canonical Ensembles. Of these parameters, the displacement step had the most significant effect on the performance of the Monte Carlo simulation. The simulation box size was also important, especially at low pressures at which the size must be sufficiently large to have a statistically acceptable number of particles in the bulk phase. Finally, it was found that the Canonical Ensemble and the Gibbs Ensemble both yielded the same isotherm (within statistical error); however, the computation time for GEMC was shorter than that for canonical ensemble simulation. However, the latter method described the proper interface between the reservoir and the adsorbed phase (and hence the meniscus).

Comparative adsorption of spherical argon and flexible n-butane in carbon slit pores - a GCMC computer simulation study

In this paper we investigate the difference between the adsorption of spherical molecule argon (at 87.3 K) and the flexible normal butane (at an equivalent temperature of 150 K) in carbon slit pores. These temperatures are equivalent in the sense that they have the same relative distances between their respective triple points and critical points. Higher equivalent temperatures are also studied (122.67 K for argon and 303 K for n-butane) to investigate the effects of temperature on the 2D-transition in adsorbed density. The Grand Canonical Monte Carlo simulation is used to study the adsorption of these two model adsorbates. Beside the longer computation times involved in the computation of n-butane adsorption, n-butane exhibits many interesting behaviors such as: (i) the onset of adsorption occurs sooner (in terms of relative pressure), (ii) the hysteresis for 2D- and 3D-transitions is larger, (iii) liquid-solid transition is not possible, (iv) 2D-transition occurs for n-butane at 150 K while it does not happen for argon except for pores that accommodate two layers of molecules, (v) the maximum pore density is about four times less than that of argon and (vi) the sieving pore width is slightly larger than that for argon. Finally another feature obtained from the Grand Canonical Monte Carlo (GCMC) simulation is the configurational arrangement of molecules in pores. For spherical argon, the arrangement is rather well structured, while for n-butane the arrangement depends very much on the pore size. (C) 2004 Elsevier B.V. All rights reserved.

The applicability of recurrent neural networks for biological sequence analysis

Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.

Effect of pore constriction on adsorption behaviour in nanoporous carbon slit pore: a computer simulation

A Grand Canonical Monte Carlo simulation (GCMC) method is used to study the effects of pore constriction on the adsorption of argon at 87.3 K in carbon slit pores of infinite and finite lengths. It is shown that the pore constriction affects the pattern of adsorption isotherm. First, the isotherm of the composite pore is greater than that of the uniform pore having the same width as the larger cavity of the composite pore. Secondly, the hysteresis loop of the composite pore is smaller than and falls between those of uniform pores. Two types of hysteresis loops have been observed, irrespective of the absence or presence of constriction and their presence depend on pore width. One hysteresis loop is associated with the compression of adsorbed particles and this phenomenon occurs after pore has been filled with particles. The second hysteresis loop is the classical condensation-evaporation loop. The hysteresis loop of a composite pore depends on the sizes of the larger cavity and the constriction. Generally, it is found that the pore blocking effect is not manifested in composite slit pores, and this result does not support the traditional irkbottle pore hypothesis.

The effects of energy sites on adsorption of Lennard–Jones fluids and phase transition in carbon slit pore of finite length a computer simulation study

A Monte Carlo simulation method is Used 10 study the effects of adsorption strength and topology of sites on adsorption of simple Lennard-Jones fluids in a carbon slit pore of finite length. Argon is used as a model adsorbate, while the adsorbent is modeled as a finite carbon slit pore whose two walls composed of three graphene layers with carbon atoms arranged in a hexagonal pattern. Impurities having well depth of interaction greater than that of carbon atom are assumed to be grafted onto the surface. Different topologies of the impurities; corner, centre, shelf and random topologies are studied. Adsorption isotherms of argon at 87.3 K are obtained for pore having widths of 1, 1.5 and 3 11111 using a Grand Canonical Monte Carlo simulation (GCMC). These results are compared with isotherms obtained for infinite pores. It is shown that the Surface heterogeneity affects significantly the overall adsorption isotherm, particularly the phase transition. Basically it shifts the onset of adsorption to lower pressure and the adsorption isotherms for these four impurity models are generally greater than that for finite pore. The positions of impurities on solid Surface also affect the shape of the adsorption isotherm and the phase transition. We have found that the impurities allocated at the centre of pore walls provide the greatest isotherm at low pressures. However when the pressure increases the impurities allocated along the edges of the graphene layers show the most significant effect on the adsorption isotherm. We have investigated the effect of surface heterogeneity on adsorption hysteresis loops of three models of impurity topology, it shows that the adsorption branches of these isotherms are different, while the desorption branches are quite close to each other. This suggests that the desorption branch is either the thermodynamic equilibrium branch or closer to it than the adsorption branch. (c) 2005 Elsevier Inc. All rights reserved.

Dynamic rupture in a 3-D particle-based simulation of a rough planar fault

An appreciation of the physical mechanisms which cause observed seismicity complexity is fundamental to the understanding of the temporal behaviour of faults and single slip events. Numerical simulation of fault slip can provide insights into fault processes by allowing exploration of parameter spaces which influence microscopic and macroscopic physics of processes which may lead towards an answer to those questions. Particle-based models such as the Lattice Solid Model have been used previously for the simulation of stick-slip dynamics of faults, although mainly in two dimensions. Recent increases in the power of computers and the ability to use the power of parallel computer systems have made it possible to extend particle-based fault simulations to three dimensions. In this paper a particle-based numerical model of a rough planar fault embedded between two elastic blocks in three dimensions is presented. A very simple friction law without any rate dependency and no spatial heterogeneity in the intrinsic coefficient of friction is used in the model. To simulate earthquake dynamics the model is sheared in a direction parallel to the fault plane with a constant velocity at the driving edges. Spontaneous slip occurs on the fault when the shear stress is large enough to overcome the frictional forces on the fault. Slip events with a wide range of event sizes are observed. Investigation of the temporal evolution and spatial distribution of slip during each event shows a high degree of variability between the events. In some of the larger events highly complex slip patterns are observed.

Extreme learning machine for predicting HLA-peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and most importantly - prevention from over-fitting for prediction of peptide binding to HLA.

Molecular dynamics characterization of n-octyl-beta-D-glucopyranoside micelle structure in aqueous solution

n-Octyl-beta-D-glueopyranoside (OG) is a non-ionic glycolipid, which is used widely in biotechnical and biochemical applications. All-atom molecular dynamics simulations from two different initial coordinates and velocities in explicit solvent have been performed to characterize the structural behaviour of an OG aggregate at equilibrium conditions. Geometric packing properties determined from the simulations and small angle neutron scattering experiment state that OG micelles are more likely to exist in a non-spherical shape, even at the concentration range near to the critical micelle concentration (0.025 M). Despite few large deviations in the principal moment of inertia ratios, the average micelle shape calculated from both simulations is a prolate ellipsoid. The deviations at these time scales are presumably the temporary shape change of a micelle. However, the size of the micelle and the accessible surface areas were constant during the simulations with the micelle surface being rough and partially elongated. Radial distribution functions computed for the hydroxyl oxygen atoms of an OG show sharper peaks at a minimum van der Waals contact distance than the acetal oxygen, ring oxygen, and anomeric carbon atoms. This result indicates that these atoms are pointed outwards at the hydrophilic/hydrophobic interface, form hydrogen bonds with the water molecules, and thus hydrate the micelle surface effectively. (c) 2005 Elsevier Inc. All rights reserved.

Effects of graphene layer size on the adsorption of fluids on graphitized thermal carbon black. A computer simulation study

The adsorption of Lennard-Jones fluids (argon and nitrogen) onto a graphitized thermal carbon black surface was studied with a Grand Canonical Monte Carlo Simulation (GCMC). The surface was assumed to be finite in length and composed of three graphene layers. When the GCMC simulation was used to describe adsorption on a graphite surface, an over-prediction of the isotherm was consistently observed in the pressure regions where the first and second layers are formed. To remove this over-prediction, surface mediation was accounted for to reduce the fluid-fluid interaction. Do and co-workers have introduced the so-called surface-mediation damping factor to correct the over-prediction for the case of a graphite surface of infinite extent, and this approach has yielded a good description of the adsorption isotherm. In this paper, the effects of the finite size of the graphene layer on the adsorption isotherm and how these would affect the extent of the surface mediation were studied. It was found that this finite-surface model provides a better description of the experimental data for graphitized thermal carbon black of high surface area (i.e. small crystallite size) while the infinite- surface model describes data for carbon black of very low surface area (i.e. large crystallite size).

Numerical method for determination of the NMR frequency of the single-qubit operation in a silicon-based solid-state quantum computer

A numerical method is introduced to determine the nuclear magnetic resonance frequency of a donor (P-31) doped inside a silicon substrate under the influence of an applied electric field. This phosphorus donor has been suggested for operation as a qubit for the realization of a solid-state scalable quantum computer. The operation of the qubit is achieved by a combination of the rotation of the phosphorus nuclear spin through a globally applied magnetic field and the selection of the phosphorus nucleus through a locally applied electric field. To realize the selection function, it is required to know the relationship between the applied electric field and the change of the nuclear magnetic resonance frequency of phosphorus. In this study, based on the wave functions obtained by the effective-mass theory, we introduce an empirical correction factor to the wave functions at the donor nucleus. Using the corrected wave functions, we formulate a first-order perturbation theory for the perturbed system under the influence of an electric field. In order to calculate the potential distributions inside the silicon and the silicon dioxide layers due to the applied electric field, we use the multilayered Green's functions and solve an integral equation by the moment method. This enables us to consider more realistic, arbitrary shape, and three-dimensional qubit structures. With the calculation of the potential distributions, we have investigated the effects of the thicknesses of silicon and silicon dioxide layers, the relative position of the donor, and the applied electric field on the nuclear magnetic resonance frequency of the donor.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.