1,720,963 research outputs found
Quantitative Structure–Property Relationship for Flash Points of Alcohols
No full textIn this paper, quantitative structure–property relationship (QSPR) models have been developed to predict flash points for some common alcohols based on a data set of 151 components. With the use of the genetic function approximation (GFA) approach, four descriptors have been selected from a set of more than 1000 molecular descriptors. These selected descriptors were used as inputs for the adaptive neuro-fuzzy inference system (ANFIS) model. The GFA model resulted in squared correlation coefficient values of 0.935 and 0.91 respectively for the training and test sets, whereas ANFIS resulted in the values of 0.959 and 0.951 for the training and test sets, respectively. However, the linear and nonlinear models can give satisfactory prediction results, but the ANFIS model is somewhat superior
Quantitative Structure–Property Relationship Prediction of Gas Heat Capacity for Organic Compounds
No full textIn the present work, a quantitative structure–property
relationship
study is performed to predict gas heat capacity for a structurally
wide variety of organic compounds using the genetic function approximation
(GFA) and the adaptive neuro-fuzzy inference system (ANFIS) methods.
The simple proposed models contain only three descriptors calculated
solely from the molecular structure of compounds which are 3D-independent
descriptors. The models were validated by an external prediction set.
Good results were obtained from both models which get the squared
correlation coefficients of 0.996 and 0.997 for GFA and ANFIS, respectively.
This study discloses enhanced correlations of the heat capacity of
gases with their molecular structures, wherein the influence of the
size of molecules is found to predominate
Quantitative Structure–Property Relationship Prediction of Liquid Heat Capacity at 298.15 K for Organic Compounds
No full textNovel QSPR models were developed and evaluated for the
prediction
of heat capacity of liquids at 298.15 K with only three descriptors.
Two linear and nonlinear models were produced using genetic function
approximation (GFA) and adaptive neurofuzzy inference system (ANFIS)
methods based on a data set of 706 compounds with a wide variety of
functional groups. The results showed that both GFA and ANFIS methods
could model the relationship between the liquid heat capacity of organic
compounds and their structures with high accuracy. The predictive
quality of the QSPR models were tested for an external test set, where
the squared correlation coefficients of prediction for the GFA and
ANFIS methods were 0.970 and 0.973, respectively
Prediction of Thermophysical Properties for Binary Mixtures of Common Ionic Liquids with Water or Alcohol at Several Temperatures and Atmospheric Pressure by Means of Artificial Neural Network
No full textIn this work, thermophysical properties
such as density, dynamic
viscosity, excess molar volume, refractive index and speed of sound
of binary mixtures of common ionic liquids (ILs) with water or alcohol
are predicted by the artificial neural network (ANN) technique. In
each ANN proposed models, the density and dynamic viscosity of pure
components IL, water or alcohol (including methanol, ethanol, 1-propanol
and 2-propanol) and pure IL and the temperature as well as mole fractions
of water or alcohol of studied binary mixtures were given as the inputs
and the desired properties were predicted as the outputs. The obtained
results revealed that the selected input parameters were appropriate
and the high statistical quality represented by various criteria and
the low prediction errors indicated that the presented models can
accurately predict the properties of IL + water/alcohol binary mixtures
QSPR Correlation of Melting Point for Drug Compounds Based on Different Sources of Molecular Descriptors
No full textFive linear QSPR models for melting points (MP) of drug-like compounds are developed based on three
different packages for molecular descriptor generation and a combined set of all descriptors. A data set of
323 gaseous, liquid, and solid compounds was used for this study. Two models from the combined set of
descriptors based on stepwise regression and genetic algorithm (GA) descriptor selection methods have
acceptable prediction abilities. The statistical results of these models are r2 = 0.673 and root-mean-square
error (RMSE) of 40.4 °C for stepwise regression-based quantitative structure−property relationships (QSPRs)
and r2 = 0.660 and RMSE of 41.1 °C for GA-based QSPRs. Interpretation of descriptors of all models
showed a strong correlation of hydrogen bonding and molecular complexity with melting points of drug-like compounds
QSPR Correlation of Melting Point for Drug Compounds Based on Different Sources of Molecular Descriptors
No full textFive linear QSPR models for melting points (MP) of drug-like compounds are developed based on three
different packages for molecular descriptor generation and a combined set of all descriptors. A data set of
323 gaseous, liquid, and solid compounds was used for this study. Two models from the combined set of
descriptors based on stepwise regression and genetic algorithm (GA) descriptor selection methods have
acceptable prediction abilities. The statistical results of these models are r2 = 0.673 and root-mean-square
error (RMSE) of 40.4 °C for stepwise regression-based quantitative structure−property relationships (QSPRs)
and r2 = 0.660 and RMSE of 41.1 °C for GA-based QSPRs. Interpretation of descriptors of all models
showed a strong correlation of hydrogen bonding and molecular complexity with melting points of drug-like compounds
QSPR Correlation of Melting Point for Drug Compounds Based on Different Sources of Molecular Descriptors
No full textFive linear QSPR models for melting points (MP) of drug-like compounds are developed based on three
different packages for molecular descriptor generation and a combined set of all descriptors. A data set of
323 gaseous, liquid, and solid compounds was used for this study. Two models from the combined set of
descriptors based on stepwise regression and genetic algorithm (GA) descriptor selection methods have
acceptable prediction abilities. The statistical results of these models are r2 = 0.673 and root-mean-square
error (RMSE) of 40.4 °C for stepwise regression-based quantitative structure−property relationships (QSPRs)
and r2 = 0.660 and RMSE of 41.1 °C for GA-based QSPRs. Interpretation of descriptors of all models
showed a strong correlation of hydrogen bonding and molecular complexity with melting points of drug-like compounds
Self-Accumulation of Uncharged Polyaromatic Surfactants at Crude Oil–Water Interface: A Mesoscopic DPD Study
No full textThe dissipative particle dynamics
(DPD) technique was applied to
study the behavior of several uncharged perylene bisimide-based polyaromatic
surfactant (PAS) molecules, with the same polyaromatic core but with
different terminal functional types (TP, C5Pe, PAP, and PCH) at the
crude oil–water interface. We considered the SARA crude oil
model with Persian Gulf oil field composition, which includes saturates,
59%; aromatics, 28.5%; resins, 9.7%; and asphaltenes, 2.8% at two
temperatures 298 and 363 K. The DPD interaction parameters for the
bead pairs needed in the DPD simulations were evaluated by using the
well-known correlation equation, where the required Flory–Huggins
interaction parameter in this equation was calculated by the blend
methodology model. The results indicated that the C5Pe terminal functional
type of PAS is absorbed more effectively on the water droplet interface
in the crude oil system and can reduce the interfacial tension (IFT)
to facilitate the oil–water separation. The results of this
simulation can be used to choose proper demulsifier surfactant for
application in various processes in the oil industry as well as enhanced
oil recovery (EOR)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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