PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Influence of Fluid Concentration on the Elevation of Boiling Point of Blackberry Juice

The rise in boiling point of blackberry juice was experimentally measured at soluble solids concentrations in the range of 9.4 to 58.4Brix and pressures between 4.9 103 and 9.0 104 Pa (abs.). Different approaches to representing experimental data, including the Duhring`s rule, a model similar to Antoine equation and other empirical models proposed in the literature were tested. In the range of 9.4 to 33.6Brix, the rise in boiling point was nearly independent of pressure, varying only with juice concentration. Considerable deviations of this behavior began to occur at concentrations higher than 39.1Brix. Experimental data could be best predicted by adjusting an empirical model, which consists of a single equation that takes into account the dependence of rise in boiling point on pressure and concentration.

Improved Prediction of Hydrocarbon Flash Points from Boiling Point Data

Flash points (T(FP)) of hydrocarbons are calculated from their flash point numbers, N(FP), with the relationship T(FP) (K) = 23.369N(FP)(2/3) + 20.010N(FP)(1/3) + 31.901 In turn, the N(FP) values can be predicted from experimental boiling point numbers (Y(BP)) and molecular structure with the equation N(FP) = 0.987 Y(BP) + 0.176D + 0.687T + 0.712B - 0.176 where D is the number of olefinic double bonds in the structure, T is the number of triple bonds, and B is the number of aromatic rings. For a data set consisting of 300 diverse hydrocarbons, the average absolute deviation between the literature and predicted flash points was 2.9 K.

Influence of Fluid Concentration on the Elevation of Boiling Point of Blackberry Juice

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Elevation of boiling point of coffee extract

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Thermal evaporation: Representation of rise in boiling point of grapefruit juice

The rise in boiling point of grapefruit juice was experimentally measured at soluble solids concentrations in the range of 9.3-60.6 °Brix and pressures between °6.0 × 103 and 9.0 × 104 Pa. Different approaches to represent experimental data, including the Dhring's rule, the Antoine equation and empirical models proposed in the literature were tested. In the range of 9.3-29.0 °Brix, the rise in boiling point was nearly independent of pressure, varying only with juice concentration. Considerable deviations of this behavior began to occur at concentrations higher than 29.0 °Brix. Experimental data could be best predicted by adjusting an empirical model, which consisted of a single equation that takes into account the dependence of rise in boiling point on pressure and concentration. © SAGE Publications 2007.

Estimation of the normal boiling point of organic compounds via group contributions

The normal boiling point is a fundamental thermo-physical property, which is important in describing the transition between the vapor and liquid phases. Reliable method which can predict it is of great importance, especially for compounds where there are no experimental data available. In this work, an improved group contribution method, which is second order method, for determination of the normal boiling point of organic compounds based on the Joback functional first order groups with some changes and added some other functional groups was developed by using experimental data for 632 organic components. It could distinguish most of structural isomerism and stereoisomerism, which including the structural, cis- and trans- isomers of organic compounds. First and second order contributions for hydrocarbons and hydrocarbon derivatives containing carbon, hydrogen, oxygen, nitrogen, sulfur, fluorine, chlorine and bromine atoms, are given. The fminsearch mathematical approach from MATLAB software is used in this study to select an optimal collection of functional groups (65 functional groups) and subsequently to develop the model. This is a direct search method that uses the simplex search method of Lagarias et al. The results of the new method are compared to the several currently used methods and are shown to be far more accurate and reliable. The average absolute deviation of normal boiling point predictions for 632 organic compounds is 4.4350 K; and the average absolute relative deviation is 1.1047 %, which is of adequate accuracy for many practical applications.

Calculating Flash Point Numbers from Molecular Structure: An Improved Method for Predicting the Flash Points of Acyclic Alkanes

We report a novel method for calculating flash points of acyclic alkanes from flash point numbers, N(FP), which can be calculated from experimental or calculated boiling point numbers (Y(BP)) with the equation N(FP) = 1.020Y(BP) - 1.083 Flash points (FP) are then determined from the relationship FP(K) = 23.369N(FP)(2/3) + 20.010N(FP)(1/3) + 31.901 For it data set of 102 linear and branched alkanes, the correlation of literature and predicted flash points has R(2) = 0.985 and an average absolute deviation of 3.38 K. N(FP) values can also be estimated directly from molecular structure to produce an even closer correspondence of literature and predicted FP values. Furthermore, N(FP) values provide a new method to evaluate the reliability of literature flash point data.

A Simple Method to Evaluate, Correlate and Predict Boiling and Flash Points of Alkynes

Boiling points (T-B) of acyclic alkynes are predicted from their boiling point numbers (Y-BP) with the relationship T-B(K) = -16.802Y(BP)(2/3) + 337.377Y(BP)(1/3) - 437.883. In turn, Y-BP values are calculated from structure using the equation Y-BP = 1.726 + A(i) + 2.779C + 1.716M(3) + 1.564M + 4.204E(3) + 3.905E + 5.007P - 0.329D + 0.241G + 0.479V + 0.967T + 0.574S. Here A(i) depends on the substitution pattern of the alkyne and the remainder of the equation is the same as that reported earlier for alkanes. For a data set consisting of 76 acyclic alkynes, the correlation of predicted and literature T-B values had an average absolute deviation of 1.46 K, and the R-2 of the correlation was 0.999. In addition, the calculated Y-BP values can be used to predict the flash points of alkynes.

A simple method to evaluate, correlate and predict boiling and flash points of alkynes

Boiling points (T B) of acyclic alkynes are predicted from their boiling point numbers (Y BP) with the relationship T B(K) = -16.802Y BP2/3 + 337.377Y BP1/3 - 437.883. In turn, Y BP values are calculated from structure using the equation Y BP = 1.726 + Ai + 2.779C + 1.716M3 + 1.564M + 4.204E3 + 3.905E + 5.007P - 0.329D + 0.241G + 0.479V + 0.967T + 0.574S. Here Ai depends on the substitution pattern of the alkyne and the remainder of the equation is the same as that reported earlier for alkanes. For a data set consisting of 76 acyclic alkynes, the correlation of predicted and literature T B values had an average absolute deviation of 1.46 K, and the R² of the correlation was 0.999. In addition, the calculated Y BP values can be used to predict the flash points of alkynes.

Ionic liquids as recycling solvents for the synthesis of magnetic nanoparticles

This work describes an easy synthesis (one pot) of MFe(2)O(4) (M = Co, Fe, Mn, and Ni) magnetic nanoparticles MNPs by the thermal decomposition of Fe(Acac)(3)/M(Acac)(2) by using BMI center dot NTf(2) (1-n-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide) or BMI center dot PF(6) (1-n-butyl-3-methylimidazolium hexafluorophosphate) ionic liquids (ILs) as recycling solvents and oleylamine as the reducing and surface modifier agent. The effects of reaction temperature and reaction time on the features of the magnetic nanomaterials (size and magnetic properties) were investigated. The growth of the MNPs is easily controlled in the IL by adjusting the reaction temperature and time, as inferred from Fe(3)O(4) MNPs obtained at 150 degrees C, 200 degrees C and 250 degrees C with mean diameters of 8, 10 and 15 nm, respectively. However, the thermal decomposition of Fe(Acac)(3) performed in a conventional high boiling point solvent (diphenyl ether, bp 259 degrees C), under a similar Fe to oleylamine molar ratio used in the IL synthesis, does not follow the same growth mechanism and rendered only smaller NPs of 5 nm mean diameter. All MNPs are covered by at least one monolayer of oleylamine making them readily dispersible in non-polar solvents. Besides the influence on the nanoparticles growth, which is important for the preparation of highly crystalline MNPs, the IL was easily recycled and has been used in at least 20 successive syntheses.

Comparative study of amine solutions used in CO2 absorption/desorption cycles

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química

Role of a novel disulfide bridge within the all-beta fold of soluble Rieske proteins

Journal of Biological Inorganic Chemistry (2010)15: 271-281