Special issue on “Advances on model-based clustering and classification”

Advances in Data Analysis and Classification -     

Influence of interparticle interactions on the kinetics of self-assembly and mechanical strength of nanoparticulate aggregates

Computer simulations based on Discrete Element Method have been performed in order to investigate the influence of interparticle interactions on the kinetics of self-assembly and the mechanical strength of nanoparticle aggregates.Three different systems have been considered.In the first system the interaction between particles has been simulated using the JKR (Johnson,Kendall and Roberts) contact theory,while in the second and third systems the interaction between particles has been simulated using van der Waals and electrostatic forces respectively.In order to compare the mechanical behaviour of the three systems,the magnitude of the maximum attractive force between particles has been kept the same in all cases.However,the relationship between force and separation distance differs from case to case and thus,the range of the interparticle force.The results clearly indicate that as the range of the interparticle force increases,the self-assembly process is faster and the work required to produce the mechanical failure of the assemblies increases by more than one order of magnitude.     

Synthesis of the first stable phosphonamide transition state analogue

Three methods were selected for the one-pot synthesis of the fully protected beta-fluoroaminophosphonic acids, using the readily accessible N-protected beta-fluoroaminals. These were activated by acylation leading, by beta-elimination, to a transient N-acylimine immediately trapped by reactive forms of dialkyl phosphites. Avoiding basic conditions, the complete or partial deprotection of these N-protected beta-fluoroaminophosphonic esters allowed the synthesis of the free amino acids, their esters, and a racemic beta-trifluorophosphonamidic acid. The latter, which represents a transition state analogue formed by the bacterial transpeptidase, is perfectly stable at pH 4.7, contrary to the nonfluorinated compounds.     

An Improved Model for Predicting Proton NMR Shielding by Alkenes Based on Ab Initio GIAO Calculations

In a strong magnetic field, nuclei located over a carbon-carbon double bond experience NMR shielding effects that are the net result of the magnetic anisotropy of the nearby double bond and various other intramolecular shielding effects. We have used GIAO, a subroutine in Gaussian 4, to calculate isotropic shielding values and to predict the proton NMR shielding increment for a simple model system: methane held in various orientations and positions over ethene. The average proton NMR shielding increments of several orientations of methane have been plotted versus the Cartesian coordinates of the methane protons relative to the center of ethene. A single empirical equation for predicting the NMR shielding experienced by protons over a carbon-carbon double bond has been developed from these data. The predictive capability of this equation has been validated by comparing the shielding increments for several alkenes calculated using our equation to the experimentally observed shielding increments. This equation predicts the NMR shielding effects more accurately than a previous model that was based on only one orientation of methane over ethene. Deshielding is predicted by this equation for protons over the center and within about 3 Å of a carbon-carbon double bond. This result is in contrast to predictions made by the long-held shielding cone model based on the McConnell equation found in nearly every textbook on NMR, but is consistent with experimental observations.     

Clustering bivariate mixed-type data via the cluster-weighted model

The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered.     

Performance of slotted pores in particle manufacture using rotating membrane emulsification

This paper addresses the use of different slotted pores in rotating membrane emulsification technology.Pores of square and rectangular shapes were studied to understand the effect of aspect ratio (1-3.5) and their orientation on oil droplet formation.Increasing the membrane rotation speed decreased the droplet size,and the oil droplets produced were more uniform using slotted pores as compared to circular geometry.At a given rotation speed,the droplet size was mainly determined by the pore size and the fluid velocity of oil through the pore (pore fluid velocity).The ratio of droplet diameter to the equivalent diameter of the slotted pore increased with the pore fluid velocity.At a given pore fluid velocity and rotation speed,pore orientation significantly influences the droplet formation rate: horizontally disposed pores (with their longer side perpendicular to the membrane axis) generate droplets at double the rate of vertically disposed pores.This work indicates practical benefits in the use of slotted membranes over conventional methods.     

Special issue on “Innovations on model based clustering and classification”

Advances in Data Analysis and Classification -     

Special issue on “Models and learning for clustering and Classification”

Advances in Data Analysis and Classification -     

Predicting thermal conductivity of liquid suspensions of nanoparticles (nanofluids) based on rheology

A methodology is proposed for predicting the effective thermal conductivity of dilute suspensions of nanoparticles (nanofluids) based on rheology.The methodology uses the rheological data to infer microstructures of nanoparticles quantitatively,which is then incorporated into the conventional Hamilton-Crosser equation to predict the effective thermal conductivity of nanofluids.The methodology is experimentally validated using four types of nanofluids made of titania nanoparticles and titanate nanotubes dispersed in water and ethylene glycol.And the modified Hamilton-Crosser equation successfully predicted the effective thermal conductivity of the nanofluids.