Response surface and group additivity methodology for estimation of thermodynamic properties of organosilanes

Janbazi, H.; Hasemann, Olaf; Schulz, Christof; Kempf, Andreas; Wlokas, Irenäus; Peukert, Sebastian

doi:10.1002/kin.21192

Cited by 16 publications

(30 citation statements)

References 33 publications

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“…In an earlier work, 17 we examined the enthalpy of formation of Si–C–H organosilane species using the atomization energy method. In it, the enthalpy of formation at 0 K is calculated as the difference between the theoretical atomization energy of a target species and the experimentally determined enthalpies of formation of the constituent atoms.…”

Section: Methodsmentioning

confidence: 99%

“…13,14 In this method, group-additivity values (GAV) may be derived from experimental thermochemical data. Because experimental data can be scarce especially for certain reactive systems, the derivation of GAV often must rely on quantum chemical calculations (see, eg, Yamada et al, 15 Dellon et al, 16 and Janbazi et al 17 ). The benefit of the group-additivity method is that they are much easier to implement in codes for automatic mechanism generation, whereas it requires more programming effort to implement and more computational resources to conduct ab initio calculations for predicting thermochemical data.…”

Section: Introductionmentioning

confidence: 99%

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A group additivity methodology for predicting the thermochemistry of oxygen‐containing organosilanes

Janbazi

Schulz

Wlokas

et al. 2020

Int J of Chemical Kinetics

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A combinatorial approach was applied to devise a set of reference Si-CO -H species that is used to derive group-additivity values (GAVs) for this class of molecules. The reference species include 62 stable single-bonded, 19 cyclic, and nine double-bonded Si-CO -H species. The thermochemistry of these reference species, that is, the standard enthalpy of formation, entropy, and heat capacities covering the temperature range from 298 to 2000 K was obtained from quantum chemical calculations using several composite methods, including G4, G4MP2, and CBSQB3, and the isodesmic reaction approach. To calculate the GAVs from the ab initio based thermochemistry of the compounds in the training set, a multivariable linear regression analysis is performed. The sensitivity of GAVs to the different composite methods is discussed, and thermodynamics properties calculated via group additivity are compared with available ab initio calculated values from the literature.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A group additivity methodology for predicting the thermochemistry of oxygen‐containing organosilanes

Janbazi

Schulz

Wlokas

et al. 2020

Int J of Chemical Kinetics

Self Cite

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“…Besides, GAM requires the manual identification of substructures due to the detailed classification of chemical groups and substructures, thus it is not capable of automatic machine predicting when large quantities of compounds need to be marked. [25] In other words, the intelligent algorithms for molecular disassembly must be available before GAM being applied to computer programs, but to achieve these algorithms has been proved to be a challengeable work [31,[37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Corrections of Molecular Morphology and Hydrogen Bond for Improved Crystal Density Prediction

et al. 2019

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Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor η and a hydrogen-bond descriptor Hb are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for Δρ < 5%, are 92.7%, 91.8%, and 93.3%; for Δρ < 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for Δρ < 5%, the values are 92.3%, 91.4% and 92.9%; for Δρ < 2%, the values are 53.7%, 51.4% and 53.2%.

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“…Benson method has been widely used to estimate thermochemical properties such as enthalpy and Gibbs energy of formation and heat capacity, of different chemical compounds [16][17][18][19][20]. This methodology is based on hierarchy of additivities, starting from atomic, following by bond and finally the group additivity [21,22].…”

Section: Introductionmentioning

confidence: 99%

Improvements of Thermal and Thermochemical Properties of Rosin by Chemical Transformation for Its Use as Biofuel

García

Bustamante

Alarcón

et al. 2019

Waste Biomass Valor

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The use of raw materials from renewable sources has become an important topic for different industries. Pine oleoresin is one of the most important renewable sources. It is composed of a broad range of chemical substances from volatile molecules to complex compounds. The resinic fraction, known as rosin or colophony, comprises approximately 80% of oleoresin. This fraction has become the most attractive one from the economic standpoint. Rosin is a complex mixture of diterpenic acids and is typically used in formulation of adhesives, coating materials, rubbers, printing inks, among others. Although their transformations have been studied, scarce information on the thermal and thermochemical properties of rosin and rosinderived products has been reported. In this work some of these properties have been estimated to evaluate the influence of chemical transformations such as reduction, isomerization and esterification of rosin components. The estimations have been compared to the literature data and to some experimental values. The interest of some of these transformations is based on the reduction in melting and boiling temperatures observed, although such reductions are probably not enough to use these substances as fuel components.

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Response surface and group additivity methodology for estimation of thermodynamic properties of organosilanes

Cited by 16 publications

References 33 publications

A group additivity methodology for predicting the thermochemistry of oxygen‐containing organosilanes

A group additivity methodology for predicting the thermochemistry of oxygen‐containing organosilanes

Corrections of Molecular Morphology and Hydrogen Bond for Improved Crystal Density Prediction

Improvements of Thermal and Thermochemical Properties of Rosin by Chemical Transformation for Its Use as Biofuel

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