COMPLEX OF NICKEL (II) ION WITH TRYPTOPHAN AS A HOMOGENEOUS CATALYST IN THE DECOMPOSITION REACTION OF CUMENE HYDROPEROXIDE IN AQUEOUS SOLUTION

The formation of Ni2+:Tryptophan (Trp) 1:1 complex, which acts as a model catalyst for decomposition of cumene hydroperoxide (ROOH) in Ni2++Trp+ROOH+H2O system, has been confirmed via kinetic study in aqueous solution at pH>7. The kinetic expression of a single catalytic decomposition reaction of ROOH under the influence of [NiTrp]+ complex was brought out. The temperature dependence of the effective rate constant of ROOH decay (Keff=Kcat[Ni2+]0[Trp]0=const) in the temperature range from 323 to 343 K can be expressed by Arrhenius equation (Eeff is in kJ/mol): Keff=(1.87±0.02)·106exp[–(49.8±0.3)/RT], min –1.

Effect of particle trapping on frost heaving soils

A model of freezing soils is developed that accounts for the dependence of the frost heave rate on particle trapping. At sufficiently low cooling rates the soil experiences primary frost heave with a single growing ice lens that rejects all soil particles. At higher cooling rates ice lenses start to engulf the largest soil particles and the rate of segregation heave is reduced. At the highest freezing rates all particles are engulfed by the ice and the pore water freezes in situ. A new kinetic expression for the segregation potential of the soil is obtained that accounts for particle trapping. Using this expression a simple transient frost heave model is developed and compared with experimental data.

CATALYTIC ACTIVITY OF THE COBALT(II) ION WITH PHENYLALANINE COMPLEX IN THE DECOMPOSITION REACTION OF CUMENE HYDROPEROXIDE IN AQUEOUS SOLUTION

The formation of Cօ2+ : phenylalanine (Phe) 1:1 complex has been confirmed via kinetic study in aqueous solution at pH>7, which acts as a model catalyst for the decomposition of cumene hydroperoxide (ROOH) in Co2++Phe+ROOH+H2O system. The kinetic expression of a single catalytic decomposition reaction of ROOH under the influence of [CoPhe]+ complex, as well as the Arrhenius equation describing the temperature dependence of the effective constant Keff (=Kcat[Co2+]0[Phe]0) of ROOH decay rate in the temperature range from 323 to 343 K are brought out.

Data Mining Crystallization Kinetics

<p>Population balance model is a valuable modelling tool which facilitates the optimization and understanding of crystallization processes. However, in order to use this tool, it is necessary to have previous knowledge of the crystallization kinetics, specifically crystal growth and nucleation. The majority of approaches to achieve proper estimations of kinetic parameters required experimental data. Across time, a vast literature about the estimation of kinetic parameters and population balances have been published. Considering the availability of data, this work built a database with information on solute, solvent, kinetic expression, parameters, crystallization method and seeding. Correlations were assessed and clusters structures identified by hierarchical clustering analysis. The final database contains 336 data of kinetic parameters from 185 different sources. The data were analysed using kinetic parameters of the most common expressions. Subsequently, clusters were identified for each kinetic model. With these clusters, classification random forest models were made using solute descriptors, seeding, solvent, and crystallization methods as classifiers. Random forest models had an overall classification accuracy higher than 70% whereby they were useful to provide rough estimates of kinetic parameters, although these methods have some limitations.</p>

Data Mining Crystallization Kinetics

<p>Population balance model is a valuable modelling tool which facilitates the optimization and understanding of crystallization processes. However, in order to use this tool, it is necessary to have previous knowledge of the crystallization kinetics, specifically crystal growth and nucleation. The majority of approaches to achieve proper estimations of kinetic parameters required experimental data. Across time, a vast literature about the estimation of kinetic parameters and population balances have been published. Considering the availability of data, this work built a database with information on solute, solvent, kinetic expression, parameters, crystallization method and seeding. Correlations were assessed and clusters structures identified by hierarchical clustering analysis. The final database contains 336 data of kinetic parameters from 185 different sources. The data were analysed using kinetic parameters of the most common expressions. Subsequently, clusters were identified for each kinetic model. With these clusters, classification random forest models were made using solute descriptors, seeding, solvent, and crystallization methods as classifiers. Random forest models had an overall classification accuracy higher than 70% whereby they were useful to provide rough estimates of kinetic parameters, although these methods have some limitations.</p>

A Kinetic Description of How Interfaces Accelerate Reactions in Micro-Compartments

A kinetic expression is derived to explain how interfaces alter bulk chemical equilibria and accelerate reactions in micro-compartments. This description, aided by the development of a stochastic model, quantitatively predicts previous experimental observations of accelerated imine synthesis in micron-sized emulsions.

A Kinetic Description of How Interfaces Accelerate Reactions in Micro-Compartments

A kinetic expression is derived to explain how interfaces alter bulk chemical equilibria and accelerate reactions in micro-compartments. This description, aided by the development of a stochastic model, quantitatively predicts previous experimental observations of accelerated imine synthesis in micron-sized emulsions.

Improved parameterization of the weathering kinetics module in the PROFILE and ForSAFE models

Although the PROFILE and ForSAFE model can accurately reproduce the chemical and mineralogical evolution of the soil unsaturated zone, it overestimates weathering rates in deeper soil layers and in groundwater systems. This overestimation has been corrected by improving the kinetic expression describing mineral dissolution by adding or upgrading braking functions. The base cation and aluminium brakes have been strengthened, and an additional silicate brake has been developed, improving the ability to describe mineral- water reactions in deeper soils. These brakes are developed from a molecular-level model of the dissolution mechanisms. Equations, parameters and constants describing mineral dissolution kinetics have now been obtained for 113 minerals from 12 major structural groups, comprising all types of minerals encountered in most soils. The PROFILE and ForSAFE weathering sub-model was extended to cover two-dimensional catchments, both in the vertical and the horizontal direction, including the hydrology. Comparisons between this improved model and field observations are available in Erlandsson Lampa et al. (2019, This special issue). The results showed that the incorporation of a braking effect of silica concentrations was necessary and helps obtain more accurate descriptions of soil evolution rates at greater depths and within the saturated zone.

Data Mining Crystallization Kinetics

<p>Population balance model is a valuable modelling tool which facilitates the optimization and understanding of crystallization processes. However, in order to use this tool, it is necessary to have previous knowledge of the crystallization kinetics, specifically crystal growth and nucleation. The majority of approaches to achieve proper estimations of kinetic parameters required experimental data. Across time, a vast literature about the estimation of kinetic parameters and population balances have been published. Considering the availability of data, this work built a database with information on solute, solvent, kinetic expression, parameters, crystallization method and seeding. Correlations were assessed and clusters structures identified by hierarchical clustering analysis. The final database contains 336 data of kinetic parameters from 185 different sources. The data were analysed using kinetic parameters of the most common expressions. Subsequently, clusters were identified for each kinetic model. With these clusters, classification random forest models were made using solute descriptors, seeding, solvent, and crystallization methods as classifiers. Random forest models had an overall classification accuracy higher than 70% whereby they were useful to provide rough estimates of kinetic parameters, although these methods have some limitations.</p>