Modelling predicts positive and negative interactions between three Australian tropical tree species in monoculture and binary mixture

Computer modelling promises to. be an important tool for analysing and predicting interactions between trees within mixed species forest plantations. This study explored the use of an individual-based mechanistic model as a predictive tool for designing mixed species plantations of Australian tropical trees. The 'spatially explicit individually based-forest simulator' (SeXI-FS) modelling system was used to describe the spatial interaction of individual tree crowns within a binary mixed-species experiment. The three-dimensional model was developed and verified with field data from three forest tree species grown in tropical Australia. The model predicted the interactions within monocultures and binary mixtures of Flindersia brayleyana, Eucalyptus pellita and Elaeocarpus grandis, accounting for an average of 42% of the growth variation exhibited by species in different treatments. The model requires only structural dimensions and shade tolerance as species parameters. By modelling interactions in existing tree mixtures, the model predicted both increases and reductions in the growth of mixtures (up to +/- 50% of stem volume at 7 years) compared to monocultures. This modelling approach may be useful for designing mixed tree plantations. (c) 2006 Published by Elsevier B.V.

Phase separation, porous structure, and cure kinetics in aliphatic epoxy resin containing hyperbranched polyester

Thermosetting blends of an aliphatic epoxy resin and a hydroxyl-functionalized hyperbranched polymer (HBP), aliphatic hyperbranched polyester Boltorn H40, were prepared using 4,4'-diaminodiphenylmethane (DDM) as the curing agent. The phase behavior and morphology of the DDM-cured epoxy/HBP blends with HBP content up to 40 wt% were investigated by differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM). The cured epoxy/HBP blends are immiscible and exhibit two separate glass transitions, as revealed by DMA. The SEM observation showed that there exist two phases in the cured blends, which is an epoxy-rich phase and an HBP-rich phase, which is responsible for the two separate glass transitions. The phase morphology was observed to be dependent on the blend composition. For the blends with HBP content up to 10 wt%, discrete HBP domains are dispersed in the continuous cured epoxy matrix, whereas the cured blend with 40 wt% HBP exhibits a combined morphology of connected globules and bicominuous phase structure. Porous epoxy thermosets with continuous open structures on the order of 100-300 nm were formed after the HBP-rich phase was extracted with solvent from the cured blend with 40 wt% HBP. The DSC study showed that the curing rate is not obviously affected in the epoxy/HBP blends with HBP content up to 40 wt %. The activation energy values obtained are not remarkably changed in the blends; the addition of HBP to epoxy resin thus does not change the mechanism of cure reaction of epoxy resin with DDM. (c) 2006 Wiley Periodicals, Inc.

Simulation of binary mixture adsorption of methane and CO2 at supercritical conditions in carbons

Knowledge of the adsorption behavior of coal-bed gases, mainly under supercritical high-pressure conditions, is important for optimum design of production processes to recover coal-bed methane and to sequester CO2 in coal-beds. Here, we compare the two most rigorous adsorption methods based on the statistical mechanics approach, which are Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulation, for single and binary mixtures of methane and carbon dioxide in slit-shaped pores ranging from around 0.75 to 7.5 nm in width, for pressure up to 300 bar, and temperature range of 308-348 K, as a preliminary study for the CO2 sequestration problem. For single component adsorption, the isotherms generated by DFT, especially for CO2, do not match well with GCMC calculation, and simulation is subsequently pursued here to investigate the binary mixture adsorption. For binary adsorption, upon increase of pressure, the selectivity of carbon dioxide relative to methane in a binary mixture initially increases to a maximum value, and subsequently drops before attaining a constant value at pressures higher than 300 bar. While the selectivity increases with temperature in the initial pressure-sensitive region, the constant high-pressure value is also temperature independent. Optimum selectivity at any temperature is attained at a pressure of 90-100 bar at low bulk mole fraction of CO2, decreasing to approximately 35 bar at high bulk mole fractions. (c) 2005 American Institute of Chemical Engineers.

Testing predictions of macroscopic binary diffusion coefficients using lattice models with site heterogeneity

Quantitatively predicting mass transport rates for chemical mixtures in porous materials is important in applications of materials such as adsorbents, membranes, and catalysts. Because directly assessing mixture transport experimentally is challenging, theoretical models that can predict mixture diffusion coefficients using Only single-component information would have many uses. One such model was proposed by Skoulidas, Sholl, and Krishna (Langmuir, 2003, 19, 7977), and applications of this model to a variety of chemical mixtures in nanoporous materials have yielded promising results. In this paper, the accuracy of this model for predicting mixture diffusion coefficients in materials that exhibit a heterogeneous distribution of local binding energies is examined. To examine this issue, single-component and binary mixture diffusion coefficients are computed using kinetic Monte Carlo for a two-dimensional lattice model over a wide range of lattice occupancies and compositions. The approach suggested by Skoulidas, Sholl, and Krishna is found to be accurate in situations where the spatial distribution of binding site energies is relatively homogeneous, but is considerably less accurate for strongly heterogeneous energy distributions.

Particle formation during soy protein precipitation

The current success of soy foods is driving soy ingredient manufacturers to develop innovative products for food manufacturers. One such innovation is separating the soy proteins glycinin and beta-conglycinin to take advantage of their individual functional and nutritional properties. Precipitation by acidification is a low-cost method for separating these two proteins. Separation is achieved by preferentially precipitating glycinin at pH ~ 6 while beta-conglycinin remains in solution. Understanding the particle formation during protein precipitation is important as it can influence the efficiency of the precipitation process as well as subsequent downstream processes such as the particle-liquid separation step, usually achieved by centrifugation. Most of the previous soy protein precipitation studies are limited to precipitation at pH 4 as this is the pH range most commonly used in the commercial manufacturing of soy protein isolates. To date, there have been no studies on the particle formation during precipitation at pH > 5.Precipitation of soy protein is generally thought to occur by the rapid formation of primary particles in the size range of 0.1 - 0.3 microns followed by aggregation of these particles via collision to aggregates of size about 1 - 50 microns. The formation of the primary particles occurs on a time scale much shorter than that of the overall precipitation process (Nelson and Glatz, 1985). This study shows that precipitation of soy protein is indeed rapid. At high pH levels, binary liquid-liquid separation occurs forming a protein-rich heavy phase. The protein-rich phase appears as droplets which can be coalesced to form a uniform bulk layer under centrifugation forces. Upon lowering the pH level by the addition of acid, further protein is precipitated as amorphous material which binds the droplets together to form aggregates of amorphous precipitates. Liquid-liquid separation has been observed in many protein solutions but this phenomenon has only scarcely been reported in the literature for soy proteins. It presents an exciting opportunity for an innovative product. Features of the liquid-phase protein such as protein yield and purity will be characterized.

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting of signed genomic data. Their algorithm solves the minimum number of reversals required for rearranging a genome to another when gene duplication is nonexisting. In this paper, we show how to extend the Hannenhalli-Pevzner approach to genomes with multigene families. We propose a new heuristic algorithm to compute the reversal distance between two genomes with multigene families via the concept of binary integer programming without removing gene duplicates. The experimental results on simulated and real biological data demonstrate that the proposed algorithm is able to find the reversal distance accurately. ©2005 IEEE

Efficient spatial clustering algorithm using binary tree

In this paper we present an efficient k-Means clustering algorithm for two dimensional data. The proposed algorithm re-organizes dataset into a form of nested binary tree*. Data items are compared at each node with only two nearest means with respect to each dimension and assigned to the one that has the closer mean. The main intuition of our research is as follows: We build the nested binary tree. Then we scan the data in raster order by in-order traversal of the tree. Lastly we compare data item at each node to the only two nearest means to assign the value to the intendant cluster. In this way we are able to save the computational cost significantly by reducing the number of comparisons with means and also by the least use to Euclidian distance formula. Our results showed that our method can perform clustering operation much faster than the classical ones. © Springer-Verlag Berlin Heidelberg 2005