Partition Coefficients Of Organic Compounds Research Articles

The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients

A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible measured data of PUF-air partition coefficients for 170 chemicals are used to develop and test the novel model. In comparison to available quantitative structure-property relationship (QSPR) methods for the prediction of PUF-air partition coefficients that need complex descriptors, the here used descriptors are simpler. The assessed various statistical factors of the simple method containing 147 (training) and 23 (test) organic compounds can verify the external and internal cross-validations. Various statistical parameters confirm the high reliability of the novel model as compared with the outputs of complex multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) methods. The values of R-squared (R2), and root mean square error (RMSE) of the new model are for training/test sets are 0.924/0.894 and 0.374/0.318, respectively. Meanwhile, R2 and RMSE values for three comparative models training/test sets are (i) MLR: 0.848/0.670 (R2) and 0.531/0.573 (RMSE); (ii) ANN: 0.902/0.664 (R2) and 0.425/0.560 (RMSE); (iii) SVM: 0.935/0.794 (R2) and 0.351/0.419 (RMSE). Thus, the new model the simplest approach with higher reliability in comparison to the best available methods.

Infinite Dilution Activity Coefficients and Gas–Liquid Partition Coefficients of Organic Compounds in Ionic Liquid 1-Butyl-2,3-dimethylimidazolium Thiocyanate: Measurements and LFER Calculations

Infinite dilution activity coefficients (γi∞) of 31 organic solutes (alkanes, cycloalkanes, olefins, aromatics, chlorinated alkanes, alcohols, and other polar organic solutes) in the ionic liquid (IL) 1-butyl-2,3-dimethylimidazole thiocyanate ([BMMIM][SCN]) were determined by gas–liquid chromatographic (GLC) method in the temperature range of 313.15–353.15 K. The infinite dilution partial molar excess enthalpies (H̅iE, ∞), entropies (TrefS̅iE, ∞), and Gibbs free energies (G̅iE, ∞) of the organic solutes at the reference temperature T = 298.15 K and the solubility parameters (δ3) of [BMMIM][SCN] were derived from γi∞. The gas–liquid partition coefficients (KL) of solutes at different temperatures were calculated by referring to the densities of [BMMIM][SCN]. The linear free energy relationship solvation model was used to fit the KL values, and the KL prediction model was obtained. The selectivity (Sij∞) and capacity (kj∞) of n-hexane (i)/benzene (j) and cyclohexane (i)/benzene (j) at infinite dilution at 323.15 K were calculated. The results were compared with the values of ILs with the thiocyanate anion in the literature.

Novel quantitative structure activity relationship models for predicting hexadecane/air partition coefficients of organic compounds

Predicting the logarithm of hexadecane/air partition coefficient (L) for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships. Herein, two quantitative structure activity relationship (QSAR) models were developed with 1272 L values for the organic compounds by using multiple linear regression (MLR) and support vector machine (SVM) algorithms. On the basis of the OECD principles, the goodness of fit, robustness and predictive ability for the developed models were evaluated. The SVM model was first developed, and the predictive capability for the SVM model is slightly better than that for the MLR model. The applicability domain (AD) of these two models has been extended to include more kinds of emerging pollutants, i.e., oraganosilicon compounds. The developed QSAR models can be used for predicting L values of various organic compounds. The van der Waals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound. These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic compounds.

The quantitative structure-property relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids

Ionic liquids (ILs) have unique properties as solvents and electrolytes, which need to be studied using innovative machine learning (ML) approaches and which allow the identification of a chemical environment that can be adapted to different applications. The gas-ionic liquid partition coefficients of organic compounds is one such application-oriented parameter for selecting both ionic liquids and organic compounds as quickly, cost-effectively, and as accurately as possible. Therefore, multiple linear regression (MLR) and random forest (RF) quantitative structure–property relationships (QSPRs) were developed for predicting the gas-ionic liquid partition coefficient (log K) of structurally variable organic solutes in the ionic liquids N-butyl-N-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate ([BMPyrr]+[FAP]−), N-butyl-N-methylpyrrolidinium tricyanomethanide ([BMPyrr]+[C(CN)3]−) and 1-(2-methoxyethyl)-1-methylpyrrolidinium tris(pentafluoroethyl)trifluorophosphate ([MeoeMPyrr]+[FAP]−). All derived models have excellent prediction capability evidenced by high 5-fold cross-validated coefficients of determination in the range 0.88 – 0.94, complemented with other high statistical parameters. Compared to the MLR approach, the non-linear RF models statistics improved in two of three data series. Analysis of the molecular descriptors selected into MLR models revealed major solvent–solute interactions, with primary contributions from Coulomb and dipolar or hydrogen bonding interactions and followed by the descriptors that expose dispersion force related interactions. Relations to all the aforementioned solvent–solute interactions were also found in RF models descriptor interpretation. Comparison of models demonstrated that a common anion in different ILs produces a significant correlation between the data series log K values, while that of ILs with a common cation are less but still significantly correlated. The lower correlation could be attributed to varying structural differences in the corresponding ions, or the anion might have a more substantial role in determining partition properties with the organic solutes in the series.

Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches

Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (KPUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the KPUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (KPDMS-w), PDMS-air (KPDMS-a) and PDMS-seawater partition coefficients (KPDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R2adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

Automated Femtoliter Droplet-Based Determination of Oil–Water Partition Coefficient

The oil-water partition coefficient of organic compounds is an essential parameter for the determination of their behaviors in environments, food, drug delivery, and biomedical systems, just to name a few. In this work, we establish a highly efficient approach to quantify the partition/distribution coefficient using surface femtoliter droplets. In our approach, droplets of 1-octanol were produced on the surface of a solid substrate in contact with the flow of an aqueous solution of the analyte. The analyte was rapidly enriched in the droplets from the flow and reached the partition equilibrium in a few seconds. The entire procedure was automated by continuous solvent exchange, and the analyte partition in the droplets was quantified from the in situ UV-vis spectrum collected by a microspectrophotometer. Our approach was validated for several substances with the octanol-water partition/distribution coefficient ranging from -1.5 to 4, where our results were in good agreement with the values reported in the literature. This method took ∼3 min to detect one analyte with the volume of the organic solvent at ∼50 μL. Thus, our surface droplet platform can greatly minimize the consumption of both solvent and analytes and can shorten the time for the determination of the partition of new compounds, which overcomes the drawbacks of the traditional shake-flask method and presents excellent reproducibility, high accuracy, cost-effectiveness, and labor-saving operation. The highly efficient micro/nanoextraction, partition, and real-time detection enabled by the surface droplets has the potential for many other high-throughput applications.

A combined quantitative property-property relationship (QPPR) for estimating packaging-food and solid material-water partition coefficients of organic compounds

The packaging-food partition coefficient (Kpf) is a key parameter to estimate the chemical migration from packaging to food and resulting ingestion exposures. As a particular case of Kpf, the solid material-water partition coefficient (Kmw) is also important in relating the material to the water phase-based skin permeation coefficient to further assess dermal contact exposure to chemicals in solid consumer products. Existing correlations to estimate Kpf or Kmw are applicable for a limited number of chemical-food-packaging or chemical-material combinations without considering the temperature effect. The present study develops a combined quantitative property-property relationship (QPPR) to predict Kpf and Kmw with a wide applicability. We compiled a dataset of 1846 measured Kpf or Kmw for 232 chemicals in 19 consolidated material types. A regression model predicts Kpf or Kmw as a function of chemical's Kow, food or water's ethanol equivalency, temperature and material type, which shows good fitting performance with R2adj of 0.93, and has been verified by internal and external validations to be robust, stable and has good predicting ability (R2ext > 0.80). A generic QPPR is also developed to predict Kpf or Kmw from chemical's Kow, food or water's ethanol equivalency, and temperature only (R2adj = 0.90), without the need to assign a specific material type. These QPPRs provide a comprehensive correlation method to estimate Kpf for diverse chemical-food-packaging combinations or to estimate Kmw for materials other than food packaging, which will facilitate high-throughput assessments of consumer exposures to chemicals in food packaging and in other solid materials such as building materials, furniture and toys.

A quantitative structure-property relationship (QSPR) for estimating solid material-air partition coefficients of organic compounds.

The material-air partition coefficient (Kma ) is a key parameter to estimate the release of chemicals incorporated in solid materials and resulting human exposures. Existing correlations to estimate Kma are applicable for a limited number of chemical-material combinations without considering the effect of temperature. The present study develops a quantitative structure-property relationship (QSPR) to predict Kma for a large number of chemical-material combinations. We compiled a dataset of 991 measured Kma for 179 chemicals in 22 consolidated material types. A multiple linear regression model predicts Kma as a function of chemical's Koa , enthalpy of vaporization (∆Hv ), temperature, and material type. The model shows good fitting of the experimental dataset with adjusted R2 of 0.93 and has been verified by internal and external validations to be robust, stable and has good predicting ability ( >0.78). A generic QSPR is also developed to predict Kma from chemical properties and temperature only (adjusted R2 =0.84), without the need to assign a specific material type. These QSPRs provide correlation methods to estimate Kma for a wide range of organic chemicals and materials, which will facilitate high-throughput estimates of human exposures for chemicals in solid materials, particularly building materials and furniture.

QSAR studies on partition coefficients of organic compounds for polydimethylsiloxane of solid-phase microextraction devices

The solid-phase microextraction (SPME) technique has been widely applied to sample the environmental matrices for organic compounds. The success of employing SPME requires knowledge of the coating-matrix partition coefficients of analytes. Polydimethylsiloxane (PDMS) is the most widely used polymeric coating for the SPME device. In this study, the quantitative structure activity relationships (QSAR) for the PDMS-water (K fw ) and PDMS-gas partition coefficients (K fg ) of organic compounds were established using E-Dragon software and multiple linear regression analysis. K fw was significantly correlated to the BLTA96 descriptor, implying that the PDMS-water partition coefficients were primarily determined by the polarity of analyte molecules. In addition, K fg was significantly dependent on the Harary H index, i.e., molecular connectivity index or polarizability, of the organic compounds. If the organic compounds were grouped in alkanes and aromatic hydrocarbons, K fw and K fg were well proportional to their octanol–water partition coefficients. The statistical results of internal and external validation, determined by the square of the coefficient of multiple correlation (R 2 ≥ 0.865) and the leave-one-out cross-validation (Q 2 ≥ 0.751), showed that the QSAR models developed herein have good stability and great predictive power among the molecular descriptors and SPME/PDMS partition coefficients. The results of this study will facilitate the practical applications of SPME as a greener methodology.

QSPR models for prediction of bovine serum albumin-water partition coefficients of organic compounds and drugs based on enhanced replacement method and support vector machine

A quantitative structure property relationship (QSPR) study based on enhanced replacement method (ERM) and support vector machine (SVM) was used to correlate molecular structures to their bovine serum albumin water partition coefficients (KBSA/W). A wide variety of natural organic compounds and drugs were selected as a dataset and suitable sets of molecular descriptors were calculated using Dragon package. ERM was used as variable selection method. The nonlinear-support vector machine models were applied to correlate the ERM-selected molecular descriptors with the experimental values of KBSA/W. Results obtained demonstrate the reliability and good predictability of support vector machine model to predict KBSA/W of organic compounds and drugs. Satisfactory results demonstrate that the ERM approach is a very powerful method for variable selection and the predictive ability of the SVM model is superior to those acquired by ERM.

Prediction of Partition Coefficients of Organic Compounds for SPME/PDMS

The partition coefficients of 51 organic compounds between SPME/PDMS and gas were compiled from the literature sources in this study. The effect of physicochemical properties and descriptors on the partitioning process of partition coefficients was explicated by the correlation analysis. The PDMS-gas partition coefficients were well correlated to the molecular weight of organic compounds (r = 0.832, p < 0.05). An empirical model, consisting of the molecular weight and the polarizability, was developed to appropriately predict the partition coefficients of organic compounds. The empirical model for estimating the PDMS-gas partition coefficient will contribute to the practical applications of the SPME technique.

High-Throughput Determination of Octanol-Water Partition Coefficients by Ultrasound-Assisted Liquid-Phase Microextraction.

A method of high-throughput determination, which is based on ultrasound-assisted liquid-phase microextraction, was developed to measure directly the partition coefficients of n-octanol-water. In ultrasound-assisted liquid-phase microextraction, ultrasonic energy can facilitate the mass transfer process of six or more microextractors simultaneously. Therefore, high-throughput determination of n-octanol-water partition coefficients can be performed favorably, and the equilibrium time of each microextractor can be decreased effectively. Several experimental parameters including ultrasonic power and frequency, centrifugation conditions, extractant volume and sample concentration were analyzed and optimized at 25°C. Under the optimum conditions, it only takes 2 min to reach extraction equilibrium, and the solutions of sample can be separated by centrifugation in 4 min. After centrifugation, the concentrations in n-octanol phases are analyzed with gas chromatography. The method was further evaluated with eight reference compounds and the findings demonstrated that this method is suitable to determine the partition coefficients of organic compounds accurately and quickly. Next, the method was exploited to measure the partition coefficients of n-octanol-water containing 20 organic compounds, which cover the [Formula: see text] values from 0.05 to 4.36, with comparatively low relative standard deviation (RSD) directly. The results of this study illustrated that the RSD (n = 6) was under 3%.

Partition coefficients of organic compounds between water and imidazolium-, pyridinium-, and phosphonium-based ionic liquids.

The partition coefficients, P IL/w, of several compounds, some of them of biological and pharmacological interest, between water and room-temperature ionic liquids based on the imidazolium, pyridinium, and phosphonium cations, namely 1-octyl-3-methylimidazolium hexafluorophosphate, N-octylpyridinium tetrafluorophosphate, trihexyl(tetradecyl)phosphonium chloride, trihexyl(tetradecyl)phosphonium bromide, trihexyl(tetradecyl)phosphonium bis(trifluoromethylsulfonyl)imide, and trihexyl(tetradecyl)phosphonium dicyanamide, were accurately measured. In this way, we extended our database of partition coefficients in room-temperature ionic liquids previously reported. We employed the solvation parameter model with different probe molecules (the training set) to elucidate the chemical interactions involved in the partition process and discussed the most relevant differences among the three types of ionic liquids. The multiparametric equations obtained with the aforementioned model were used to predict the partition coefficients for compounds (the test set) not present in the training set, most being of biological and pharmacological interest. An excellent agreement between calculated and experimental log P IL/w values was obtained. Thus, the obtained equations can be used to predict, a priori, the extraction efficiency for any compound using these ionic liquids as extraction solvents in liquid-liquid extractions.

Prediction of Partition Coefficients of Organic Compounds between SPME/PDMS and Aqueous Solution

Polydimethylsiloxane (PDMS) is commonly used as the coated polymer in the solid phase microextraction (SPME) technique. In this study, the partition coefficients of organic compounds between SPME/PDMS and the aqueous solution were compiled from the literature sources. The correlation analysis for partition coefficients was conducted to interpret the effect of their physicochemical properties and descriptors on the partitioning process. The PDMS-water partition coefficients were significantly correlated to the polarizability of organic compounds (r = 0.977, p < 0.05). An empirical model, consisting of the polarizability, the molecular connectivity index, and an indicator variable, was developed to appropriately predict the partition coefficients of 61 organic compounds for the training set. The predictive ability of the empirical model was demonstrated by using it on a test set of 26 chemicals not included in the training set. The empirical model, applying the straightforward calculated molecular descriptors, for estimating the PDMS-water partition coefficient will contribute to the practical applications of the SPME technique.

Prediction of Bovine Serum Albumin-Water Partition Coefficients of a Wide Variety of Neutral Organic Compounds by Means of Support Vector Machine.

Support vector machine (SVM) was used to develop a quantitative structure property relationship (QSPR) model that correlates molecular structures to their bovine serum albumin water partition coefficients (KBSA/W ). The performance and predictive aptitude of SVM are considered and compared with other methods such as multiple linear regression (MLR) and artificial neural network (ANN) methods. A set of 83 natural organic compounds and drugs were selected and suitable sets of molecular descriptors were calculated. Genetic algorithm (GA) was used to select important molecular descriptors, and linear and nonlinear models were applied to correlate the selected descriptors with the experimental values of log KBSA/W . The correlation coefficients, R, between experimental and predicted log KBSA/W for the validation set by MLR, ANN and SVM are 0.951, 0.986 and 0.991, respectively. Results obtained document the reliability and good predictability of the nonlinear QSPR model to predict partition coefficients of organic compounds. Comparison between the values of statistical parameters demonstrates that the predictive ability of the SVM model is comparable or superior to those obtained by MLR and ANN.

Prediction of gas to water partition coefficient of some organic compounds using theoretically derived molecular descriptors

An artificial neural network (ANN) is constructed and trained for the prediction of gas to water partition coefficients of various organic compounds. The inputs of this neural network are theoretically derived from molecular descriptors that were chosen by the genetic algorithm-partial least squares (GA-PLS) feature selection technique. These descriptors are: area-weighted surface charge of hydrogen bonding donor atoms (HDCA-2), average bond order of a C atom (PC), Kier flexibility index (Φ), atomic charge weighted partial positively charged surface area (PPSA-3), and difference between atomic charge weighted partial positive and negative surface areas (DPSA-3). By comparing the results obtained from PLS and ANN models, one can see that statistical parameters (Fisher ratio, correlation coefficient, and standard error) of the ANN model are better than those of the PLS model, which indicates that a nonlinear model can simulate more accurately the relationship between the structural descriptors and the partition coefficients of the investigated molecules.

Prediction of Partition Coefficients of Organic Compounds in Ionic Liquids Using a Temperature-Dependent Linear Solvation Energy Relationship with Parameters Calculated through a Group Contribution Method

A temperature-dependent linear solvation energy relationship (LSER) is proposed for estimating the gas-to-ionic liquid partition coefficients. The model calculates the LSER parameters using a group contribution method. Large sets of partition coefficients were analyzed using the Abraham solvation parameter model to determine the contributions of 21 groups: 12 groups characterizing the cations and 9 groups for the anions. The derived equations correlate the experimental gas-to-ionic liquid coefficient data to within 0.13 log units. The 21 group parameters are used to predict the partition coefficients of solutes in alkyl or functionalized ionic liquids with good accuracy.

Estimation of Partition Coefficients of Benzene, Toluene, Ethylbenzene, and p-Xylene by Consecutive Extraction with Solid Phase Microextraction

It is very important to use partition coefficients of organic pollutants to predict their fate in the environment. A liquidliquid extraction technique was used to determine the partition coefficients of organic compounds between water and organic solvent. The concentration of the target compounds must be determined after equilibrium is established between the two phases. The partition coefficients can be estimated using the capacity factors of HPLC and GC. The solid phase microextraction (SPME) technique has been widely used for the analysis of volatile organic compounds (VOCs) in water and air; fragrances in perfume, food, and cosmetics; and many other emerging pollutants. In addition, the fact that SPME extraction is based on equilibrium drove scientists to develop a new approach to determine partition coefficient (Kd) values. Vaes et al. used the kinetics of partitioning to SPME fibers to estimate the Kd values for 19 organic compounds. Nardi used the “depletion technique” and a capillary tubing coated with siloxane to estimate the Kow values of BTEX. 6 The Kd values of hydrophobic compounds are generally known to have a close relationship with Kow. 7 Dean and colleagues showed a close correlation between water-fiber coefficients (Kfs) and octanol-water partition coefficients (Kow). 8 All these approaches use an absolute amount of analyte extracted from the SPME fiber. The liquid injection of a known amount of standard solution is a common technique to calibrate GC for quantitative analysis with the SPME fiber. This technique assumes that the sample transfer efficiency by the SPME fiber is the same as that of liquid injection. However, this study shows that several factors need to be considered in order to achieve higher sample transfer efficiency in the case of liquid injection, i.e., the liner, glass wool, and the temperature of the injector. The difference in sample transfer efficiency between liquid injection and the SPME fiber may result in errors in the estimation of Kfs values, i.e., Kow values. In this study, water-fiber partition coefficients (Kfs) were determined from consecutive extraction data by a SPME fiber. Instead of using the absolute mass of the analyte, the relative GC signal ratio was used to estimate the partition coefficients. An SPME fiber extracts target compounds multiple times from the same sample vial and inserts the fiber into a hot GC injector. The extracted amount on the fiber coating decreases exponentially after each extraction; Kfs of an analyte can be derived from the resulting GC responses. Theory. The following is the equation showing the relationship among the extracted amount (n, the number of moles of the analyte with an initial concentration of Co), the extraction phase volume, (Vf, typically 0.6 μL ), and the sample volume (Vs) when an SPME fiber has reached equilibrium with the analyte. Eq. (1) shows that the extracted amount is governed by the partition coefficient (Kfs) of the target analyte:

Partition Coefficients of Organic Contaminants with Carbohydrates

In view of the current lack of reliable partition coefficients for organic compounds with carbohydrates (K(ch)), carefully measured values with cellulose and starch, the two major forms of carbohydrates, are provided for a wide range of compounds: short-chain chlorinated hydrocarbons, halogenated benzenes, alkyl benzenes, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls, and organochlorine pesticides. To ensure the accuracy of the K(ch) data, solute concentrations in both water and carbohydrate phases are measured by direct solvent extraction of the samples. For a given compound, the observed partition coefficient with cellulose (K(cl)) is virtually the same as that with starch (K(st)). This finding expedites the evaluation of organic contamination with different forms of carbohydrates. The presently determined K(ch) values of 13 PAHs are substantially lower (by 3-66 times) than the literature data; the latter are suspect as they were obtained with (i) presumably impure carbohydrate samples or (ii) indirectly measured equilibrium solute concentrations in carbohydrate and water phases. Although the K(ch) values are generally considerably lower than the respective K(ow) (octanol-water) or K(lipid) (lipid-water), accurate K(ch) data are duly required to accurately estimate the contamination of carbohydrates by organic compounds because of the abundance of carbohydrates over lipids in crops and plants. To overcome the current lack of reliable K(ch) data for organic compounds, a close correlation of log K(ch) with log K(ow) has been established for predicting the unavailable K(ch) data for low-polarity compounds.