Property Relationship Study Research Articles

A novel method for predicting the flash points of organosilicon compounds from molecular structures

ABSTRACTA quantitative structure–property relationship (QSPR) study is performed to develop mathematical models for the prediction of the flash point (FP) of organosilicon compounds from their molecular structures. Various kinds of molecular descriptors were calculated to represent the molecular structures of organosilicon compounds, such as topological, charge, and geometric descriptors. The genetic algorithm combined with multiple linear regression (GA‐MLR) is employed to a select optimal subset of descriptors that have a significant contribution to the overall FP property. The model with the best result is a five‐variable multilinear model, which showed high prediction ability when the obtained root mean square error and average absolute error for the external test set were 14.11 and 11.1 K, respectively. The model was further compared with other previously published methods. The results indicate the superiority of the presented model and reveal that it can be effectively used to predict the FP of organosilicon compounds with only the knowledge of molecular structures. This study can provide a new way for predicting the FP of organosilicon compounds for the engineering field. Copyright © 2012 John Wiley & Sons, Ltd.

Highlighting and trying to overcome a serious drawback with qspr studies; data collection in different experimental conditions (mixed-QSPR)

The experimental conditions in quantitative structure-property relationship (QSPR) studies need to be the same for each dataset in case one wishes to relate the property, only to the structure. This major drawback limits QSPR studies due to two reasons: (1) Gathering of physicochemical data obtained under the same experimental condition is difficult. (2) The obtained model is just useful to predict the physicochemical properties under the specific experimental condition. In this article, we report an attempt to highlight the shortcoming of QSPR studies for a property that was measured under different experimental conditions. In addition, we reveal inadequacies that correlating the fluorescence properties and the descriptor of the solvent has. These defects are eventually removed by taking into account the solvent-solute interactions in descriptor calculations. Quantum chemical calculations (HF/6-31G*) were carried out to optimize geometry and calculate the structural descriptors. The genetic algorithm combined with multiple linear regression method was utilized to construct the linear QSPR models. Because of the better nonlinear relationship between the quantum yield of fluorescence and structural descriptors in comparison with those of a linear relationship, support vector machine was used to construct the nonlinear QSPR model. Result analyses demonstrated that the proposed models meet our goal.

QSPR study of charge–transfer complexes of some organic donors with p-chloranil using PLSR and MLR

A quantitative structure–property relationship (QSPR) study was conducted to develop models to relate the structure of 46 donor compounds to their charge–transfer complex formation constants with chloranil for the first time. Donors were a diverse set of organic compounds having different functional groups. QSPR models were calculated using multiple linear regressions (MLR) and partial least squares regression (PLSR) to predict charge–transfer complex formation constants. Some important descriptors in charge–transfer complex formation were calculated and added to the descriptors taken from DRAGON software. After a reduction step using variable importance in projection, stepwise regression was employed to select the most relevant descriptors and develop the QSPR models. Results of the PLSR and MLR modeling by the selected descriptors were markedly similar. The results showed that descriptors related to nitrogen, carbonyl group, and polarizability strengthen the charge–transfer interactions.

Intramolecular excimer emission as a blue light source in fluorescent organic light emitting diodes: a promising molecular design

Intramolecular excimer emission arising from organic molecules as a blue light source in fluorescent Small Molecule Organic Light Emitting Diodes (SMOLEDs) is almost absent from the literature. In this work, three aryl-substituted DiSpiroFluorene–IndenoFluorenes (DSF–IFs 1–3) possessing different fluorescent properties due to their different main emitters have been investigated through a structure–property relationship study. Due to its particular geometry, the rigid DSF–IF platform 2 allows an ‘aryl/fluorene/aryl’ dimer to be preformed in the ground state leading, in the excited state, to a deep blue fluorescent emission through strong π–π intramolecular interactions between the two ‘aryl/fluorene/aryl’ arms. 2 has been successfully used as an emitting layer in a SMOLED with electroluminescence arising from electrogenerated intramolecular excimers and the properties of these excimer-based OLEDs have been compared to those of two model compounds (1 and 3). The simple and non-optimized double-layer device displays a deep blue colour (CIE coordinates: 0.19; 0.18) exhibiting a luminance of 510 Cd m−2 with a luminous efficiency of ca. 0.1 Cd A−1. This work is, to the best of our knowledge, the first rational and comparative study describing an intramolecular excimer based-SMOLED.

Symmetric vs. asymmetric squaraines as photosensitisers in mesoscopic injection solar cells: a structure–property relationship study

A symmetric squaraine and its related non-symmetric structure are shown to have comparable efficiencies in DSCs, but with undoubtedly advantages in the low cost and easiness of synthesis for the symmetrical structure.

Structure–property relationship studies in amine functionalized multiwall carbon nanotubes filled polypropylene composite fiber

AbstractAmine functionalized multiwalled carbon nanotubes (a‐MWNT) based polypropylene (PP) composite fibers were prepared in the presence of polypropylene‐g‐maleic anhydride (PP‐g‐MA) by melt‐mixing followed by melt‐spinning with subsequent post‐drawing of the as‐spun fibers of varying draw ratio (DR). In order to enhance the interfacial interaction, a‐MWNT were utilized in combination with PP‐g‐MA during melt‐mixing. Fourier transform infrared spectroscopic analysis revealed the formation of imide bonds between MA functionality of PP‐g‐MA and amine functional group of a‐MWNT. Higher tensile properties of PP/a‐MWNT/PP‐g‐MA composite fibers were registered with varying DR of the as‐spun fiber. Orientation factors of a‐MWNT and PP chains along the fiber axis were correlated with the higher tensile modulus and tensile strength of PP/a‐MWNT/PP‐g‐MA composite fiber of varying DR. Crystallization studies indicated the role of hetero‐nucleating action of a‐MWNT in PP/a‐MWNT/PP‐g‐MA composite fiber. POLYM. ENG. SCI., 2012. © 2011 Society of Plastics Engineers

Structure–Behavior–Property Relationship Study of Surfactants as Foam Stabilizers Explored by Experimental and Molecular Simulation Approaches

A multiscale stability study of foams stabilized by sodium dodecyl sulfate (SDS), sodium dodecylbenzene sulfonate (SDBS), and sodium polyoxyethylene alkylether sulfate (AES) was conducted, to investigate the relationship of surfactant molecular behavior and interfacial monolayer configuration of foam film to the foam film properties. Molecular dynamic (MD) simulations using a full-atom model was utilized to explore the microscopic features of the air/liquid interface layer. Several parameters such as the distribution of surfactant head groups and the order degree of surfactant hydrophobic tails were used to describe the molecular adsorption behavior. The effect of molecular structure on the nature of the foam film and the impact on the dynamic stability of wet foam is discussed. In the experimental evaluation, the SDBS foam films manifest strong stiffness and low viscoelasticity as shown by the interfacial shear rheology determination as well as texture analyzer (TA) measurement results, which agree very well with the array behavior of SDBS molecules at the air/water interface as described by the simulation results and is identified to be the reason for the poor dynamic stability. Comparing the molecular structure of SDS, SDBS, and AES, the special contributions of the linking groups such as the O atom, the phenyl group, and the EO (oxyethyl) chain to the interfacial array behavior of surfactants were characterized. It is concluded that microhardness of the foam film enhanced by rigid linking groups favors static foam stability but decreases the dynamic foam stability, while viscoelasticity of the foam film enhanced by soft linking groups increases the dynamic foam stability.

Prediction of the melting points of fatty acids from computed molecular descriptors: A quantitative structure–property relationship study

The aim of these works present in this paper consisted in the development and evaluation of quantitative structure property models (QSPR) for the prediction of the melting points of a series constituted by 62 fatty acids. The best multilinear regressions method (MLR) is used to develop models for the prediction of the melting points. The descriptors of the model are selected among an extended set of more than 500 descriptors (constitutional, topological, geometric, quantum chemical and thermodynamic descriptors). Applicability domains were defined and the predictive power was determined using a set of validations The QSPR models are established using the BMLR method implemented in CODESSA software, It turns out that the best QSPR model ( R 2 = 0.948, R a d j 2 = 0.936 , SD = 0.940 and F-test = 190.90) is obtained with five molecular descriptors.

A new quantitative structure–property relationship model to predict bioconcentration factors of polychlorinated biphenyls (PCBs) in fishes using E-state index and topological descriptors

A quantitative structure–property relationship (QSPR) study for predicting the logarithm of bioconcentration factors (Log BCF) of polychlorinated biphenyls (PCBs) is presented in this work. For this, the descriptors were obtained using only the Simplified Molecular Input Line Entry System (SMILES) strings in the free web server Parameter Client. The model was built using the Partial Least Squares (PLS) regression method. The best model presented five descriptors (one E-state index and four topological descriptors) and a high quality for fit, internal, and external predictions. The leave- N-out (LNO) cross validation and the y-randomization test showed the model is robust and has no shown chance correlation. With a second test set, the model was compared to other models and presented a root mean square error (RMSE) very close to the best model. The mechanistic interpretation was corroborated by other works in the literature and by the descriptors' theory. Thus, the results meet the five Organization for Economic Co-operation and Development (OECD) principles for validation of QSA(P)R models, and it is expected the model can effectively predict the BCF values in fishes of the PCB congeners without highly reliable experimental BCF.

Quantitative structure–property relationship study on heat of fusion for ionic liquids

Heat of fusion is an important property for the application of ionic liquids (ILs) as phase change materials. For 40 ionic liquids whose experimental data of fusion heat being available, the lowest energy structure of each ionic liquid is calculated by Gaussian 03 software with B3LYP functional and a 6-31G* basis set. The most effective descriptors were selected by means of stepwise method. Then quantitative structure–property relationship (QSPR) models for different types of ILs were developed and evaluated by external datasets. The developed QSPR models can give reasonable correlation and prediction accuracy.

Synthesis, characterization and structure–property relationship studies of cobaloximes with dithienylglyoxime as the equatorial ligand

The synthesis of cobaloximes, X/RCo(dThgH) 2Py (X = Cl, R = Me, Et, n-Pr, n-Bu and Bn) has been described. All the complexes have been characterized by elemental analyses and NMR spectral studies. The molecular structures of ClCo(dThgH) 2Py, MeCo(dThgH) 2Py, EtCo(dThgH) 2Py and BnCo(dThgH) 2Py complexes are determined by X-ray crystallography. The electron withdrawing nature of 2-thienyl ring affects the NMR as well as electrochemical behavior of these complexes. The electrochemical reduction from Co(III) to Co(II) and from Co(II) to Co(I) are much easier in ClCo(dThgH) 2Py as compared to chlorocobaloximes with the other dioximes (gH, dmgH, dpgH, dmestgH). The molecular oxygen insertion in the Co−C bond of benzyl complex ( 6) has been examined and a comparison of its reaction rate with other similar cobaloximes is discussed. The structural features of a dioxy complex Bn(O 2)Co(dThgH) 2Py ( 7) have also been reported.

A quantum mechanical quantitative structure–property relationship study of the melting point of a variety of organosilicons

We have developed quantitative structure–property relationship (QSPR) models that correlate the melting points of chain and cyclic silanes and siloxanes with their molecular structures. A comprehensive correlation was derived for a variety of molecules, but the quality of the comprehensive model was modest at best. This provided the impetus for the development of two additional models focused on silanes and siloxanes, respectively. Statistical analyses confirm the robustness of the refined models, and the chemical interpretation of the descriptors was consistent with effects expected for melting.

Prediction of Solubility of Fullerene C60 in Various Organic Solvents by Genetic Algorithm-Multiple Linear Regression

Quantitative structure property relationship (QSPR) study is presented for modeling and predicting of solubility of fullerene (C60) in various solvents. A data set consisting of 36 benzene derivatives is used in this study. Various kinds of molecular descriptors were calculated to represent the molecular structures of compounds and the best-fitting descriptors were selected by using stepwise multiple linear regressions (SW-MLR) and a genetic algorithm (GA-MLR) the selection of variables. The models were validated using leave-one-out (LOO), leave-multiple-out (LMO) cross-validation, external test set and Y-randomization test. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. Comparison of the results obtained indicated the superiority of the genetic algorithm over the stepwise. Also electronegativity, dispersion interaction in solution and volume of molecule were the main independent factors contributing to the solubility of fullerenes in the studied solvents.

QSPR probing of Na+ complexation with 15-crown-5 ethers derivatives using artificial neural network and multiple linear regression

A quantitative structure–property relationship (QSPR) study is performed to develop a model, relating to Na+ complex stability constant (log K) and the structure of 74 derivatives of 1,4,7,10,13-pentaoxacyclo-pentadecane ethers (15C5). Stepwise Multiple Linear Regression (SMLR) and Artificial Neural Network (ANN) methods have been exploited as linear and nonlinear methods, respectively to build the QSPR model. MOPAC software embedded in ChemOffice 2004 package was used for the minimizing energy using semi-empirical AM1 method. The optimum structures have been applied to generate more than 50 descriptors using available servers in ChemOffice 2004. The five most important constitutional, steric, electronic, thermodynamic and molecular descriptors were selected using the common preselection method combined by SMLR method. SMLR and ANN models were constructed based on the five selected descriptors. Both proposed models efficiently predict log K of 15C5 complexes. However, the results of ANN were more effective respect to SMLR model. This phenomenon reveals that log K of 15C5 complexes have a deviation from linear behavior related to the selected descriptors.

Modeling of 13C NMR chemical shifts of benzene derivatives using the RC–PC–ANN method: A comparative study of original molecular descriptors and multivariate image analysis descriptors

The primary goal of a quantitative structure–property relationship study is to identify a set of structurally based numerical descriptors that can be mathematically linked to a property of interest. In this work, two main groups of descriptors have been used to predict 13C NMR chemical shifts of ipso, ortho, meta, and para positions in a series of 113 monosubstituted benzenes. First, two groups of descriptors — original molecular descriptors (constitutional, topological, electronic, and geometrical) and multivariate image analysis (MIA) descriptors — were calculated. Then, calculated descriptors were subjected to principal component analysis and the most significant principal components were extracted. Finally, more correlated principal components were used as inputs of artificial neural networks. The results obtained using the rank correlation–principal component–artificial neural network (RC–PC–ANN) modeling method show high ability to predict 13C NMR chemical shifts. Also, comparison of the results indicates that MIA descriptors show better ability to predict 13C NMR chemical shifts than the original molecular descriptors.

Elucidation of specific aspects of dielectric constants of conjugated organic compounds: a QSPR approach

The characteristic aspects of dielectric constants of π-conjugated compounds are elucidated by a quantitative structure property relationship (QSPR) study. To develop a QSPR model, among 141 collected π-conjugated compounds, a subset of 116 compounds was used as the training set for the model building and the rest was used as the test set for the model validation. Statistical regression models using 396 molecular descriptors were generated based on the genetic function approximation algorithm. The predicted dielectric constants obtained by the best model are highly correlated with the experimental values (squared correlation coefficient R(2) of 0.93 and 0.97 for the training and test sets, respectively), while a previous prediction model for general organic molecules (Sild S, Karelson M (2002) J Chem Inf Comput Sci 42:360-367) is not valid for our collected π-conjugated organic compounds. It has been known that the dielectric constants of organic materials are largely influenced by orientational correlations of the constituent molecules. In general, hydrogen bonding is one of the most important intermolecular interactions affecting orientational correlation. In the case of π-conjugated compounds, however, π-π interaction could be another comparable interaction with the hydrogen bonding.

Prediction of Setschenow constants of organic compounds based on a 3D structure representation

Quantitative structure–property relationship (QSPR) studies were performed between three-dimensional (3D) descriptors representing the molecular structures and Setschenow constants ( K salt) by sodium chloride for a diverse set of organic compounds. The entire set of 101 compounds was divided into a training set of 71 compounds and a test set of 30 compounds according to Kennard and Stones algorithm. Multilinear regression (MLR) analysis was used to select the best subset of descriptors and to build linear models; while nonlinear models were developed by means of artificial neural network (ANN). The obtained models with five descriptors involved show a good predictive power: for the test set, a squared correlation coefficient ( R 2) of 0.8987 and a standard error of estimation ( s) of 0.021 were achieved by the MLR analysis; while by the ANN, R 2 and s were 0.9034 and 0.020, respectively.

CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals.

Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown.

Computational studies on tetrazole derivatives as potential high energy materials

The tetrazole is an important functionality of the most of energetic materials due to 80% nitrogen content, stability, and high enthalpy of formation. The present structure–property relationship study focuses on the optimized geometries of tetrazole derivatives obtained from density functional theory (DFT) calculations at B3LYP/6-31G* levels. The heat of formation (HOF) of tetrazole derivatives have been calculated by designing the appropriate isodesmic reactions. The increase in nitro groups on azole rings shows the remarkable increase in HOF. Density has been predicted by using CVFF force field. Increase in the nitro group increases the density. Detonation properties of the designed compounds were evaluated by using the Kamlet–Jacobs equation based on predicted densities and HOFs. Designed tetrazole derivatives show detonation velocity (D) over 8 km/s and detonation pressure (P) of about 32 GPa. Thermal stability was evaluated via bond dissociation energies (BDE) of the weakest C–NO2 bond at B3LYP/6-31G* level. Charge on the nitro group has been used to assess the sensitivity correlation. Overall, the study implies that designed compounds of this series are found to be stable and expected to be the novel candidates of high energy materials (HEMs).

Computational design and structure–property relationship studies on heptazines

This study aimed to design novel nitrogen-rich heptazine derivatives as high energy density materials (HEDM) by exploiting systematic structure-property relationships. Molecular structures with diverse energetic substituents at varying positions in the basic heptazine ring were designed. Density functional techniques were used for prediction of gas phase heat of formation by employing an isodesmic approach, while crystal density was assessed by packing calculations. The results reveal that nitro derivatives of heptazine possess a high heat of formation and further enhancement was achieved by the substitution of nitro heterocycles. The crystal packing density of the designed compounds varied from 1.8 to 2 g cm(-3), and hence, of all the designed molecules, nitro derivatives of heptazine exhibit better energetic performance characteristics in terms of detonation velocity and pressure. The calculated band gap of the designed molecules was analyzed to establish sensitivity correlations, and the results reveal that, in general, amino derivatives possess better insensitivity characteristics. The overall performance of the designed compounds was moderate, and such compounds may find potential applications in gas generators and smoke-free pyrotechnic fuels as they are rich in nitrogen content.