Linear Free Energy Relationship Model Research Articles

Mechanistic insights into and modeling the effects of relative humidity on low-concentration VOCs adsorption on hyper-cross-linked polymeric resin by inverse gas chromatography

Water vapor is very common in contaminated streams, which has a great influence on the adsorption of low-concentration volatile organic compounds (VOCs) due to the competition between water and VOCs. Understanding adsorption mechanisms and predicting adsorption of VOCs under different relative humidity (RH) are of great importance to design effective adsorption unit. In this study, we comprehensively investigated the effects of RH on the surface properties of hyper-cross-linked polymeric resin (HPR) and adsorption of 18 VOCs at low concentration on HPR under five levels of RH using inverse gas chromatography (IGC). Further, a promising RH-dependent poly-parameter linear free energy relationships (PP-LFERs) model was developed. It was found that water vapor caused the decrease of surface free energy (γst) of HPR due to the occupation of active sites by water molecules, resulting in the decrease of adsorption partition coefficients (K). Moreover, the γst could accurately quantify the effects of RH on the surface properties of HPR. Therefore, the RH-dependent PP-LFERs model was established by correlating RH and γst. The developed model overcame the limited predictive ability of existing models only under a specific RH level, and excellently predicted the lnK values of VOCs (R2 = 0.944, RMSEt = 0.36 and RMSEv = 0.47) under various RH.

Seasonal variation and affecting factors of organophosphate esters in particulate matter in air: a comparison between measured data and model predictions

Six organophosphate esters (OPEs) with different octanol-air partition coefficient (KOA) were measured from gas and particle samples collected from an urban area of Central China between June 2018 and May 2019. The highest concentration of total OPEs (∑6OPEs) in total suspended particle (TSP) were found in winter, followed by spring, autumn, and summer. ∑6OPEs showed significant positive correlation with TSP concentration (p < 0.01), significant negative correlation with temperature (p < 0.01), weak negative linear correlations with relative humility (p < 0.05), and no significant correlations with wind speed (p > 0.05). Although OPEs were not detected in polyurethane foams (PUFs), the poly-parameter linear free energy relationship model (pp-LFER) estimated particle fractions of OPEs (fpart pp-LFERg/p) were below 2.73% for Tris (2-chloroisopropyl) phosphate (TCIPP), Tris (chloroethyl) phosphate (TCEP), and Tributyl phosphate (TnBP); 67.8% for Triphenyl phosphate (TPhP); and above 99.6% for TBOEP and TCrP. The concentrations of particle-bound TPhP, TCrP, and TBOEP estimated by pp-LFER (Cpart pp-LFERg/p) were 0.19, 0.09, and 0.05 ng/m3, which were in good agreement with measurements. However, the estimated Cpart pp-LFERg/p of TCEP, TCIPP, and TnBP showed obvious differences with measured data. According to filter-air partitioning estimated by pp-LFER, the strong adsorption of gaseous OPEs to glass fiber filter (GFF) may be responsible to the underestimation. Besides, the weak adsorption of gaseous OPEs to PUF is likely to be another important reason for the discrepancy between measured and modeled results. Therefore, the risk assessment of gaseous OPEs should be of concern in the further research.

Predicting adsorption of micropollutants on non-functionalized and functionalized multi-walled carbon nanotubes: Experimental study and LFER modeling

It is of great importance to predict the adsorption of micropollutants onto CNTs, which is not only useful for exploring their potential adsorbent applications, but also helpful for better understanding their fate and risks in aquatic environments. This study experimentally examined the adsorption affinities of thirty-one micropollutants on four multi-walled CNTs (MWCNTs) with different functional groups (non-functionalized, -COOH, -OH, and -NH2). The properties of each adsorbent were predicted based on the linear free energy relationship (LFER) model. The experimental results showed that MWCNTs-COOH has remarkable adsorption affinities for positively charged compounds (1.996–3.203 log unit), whereas MWCNTs-NH2 has high adsorption affinities for negatively charged compounds (1.360–3.073 log unit). Regarding neutral compounds, there was no significant difference in adsorption affinities of all types of CNTs. According to modeling results, the adsorption affinity can be accurately predicted using LFER models with R2 in the range of 0.81–0.91. Based on the developed models, the adsorption mechanism and contribution of individual intermolecular interactions to the overall adsorption were interpreted. For non-functionalized MWCNTs, molecular interactions induced by molecular volume and H-bonding basicity predominantly contribute to adsorption, whereas for functionalized MWCNTs, the Coulombic interaction due to the charges is an important factor.

Linear free energy relationship models for the retention of partially ionized acid-base compounds in reversed-phase liquid chromatography

The LFER model of Abraham is applied to the retention of the neutral and ionic forms of 94 solutes in a C18 column and 40% v/v acetonitrile/water mobile phase. The results show that polarizability and cavity formation interactions increase retention, whereas dipole and hydrogen bonding interactions favours partition to the mobile phase and thus, they decrease retention. The coefficients of the ionic descriptors measure the effect of the electrostatic interactions and their contribution to partition of the cation or anion between the two mobile and stationary chromatographic phases.A new LFER model for application to the retention of partially dissociated acids and bases is derived averaging the descriptors of the neutral and ionic forms according to their degrees of ionization in the mobile phase. This new LFER model is satisfactorily compared to other literature modified Abraham models for a set of 498 retention data of partially dissociated acids and bases.All tested models require the calculation of the ionization degrees of the compounds at the measuring pH. Calculation of the ionization degrees in the chromatographic mobile phase (i.e. from pH and pKa in the eluent) give good correlations for all tested models. However, estimation of these ionization degrees from pH – pKa data in pure water gives biased estimations of the retention of the partially ionized solutes.

Prediction of organic pollutant removal using Corynebacterium glutamicum fermentation waste

The disposal of bio-waste (e.g., Corynebacterium glutamicum) produced by the fermentation industry is a serious problem and has a negative impact on economic returns. Some fermentation waste can be recycled as livestock feed, but much cannot be used. Therefore, other recycling methods must be developed to increase its applications, for example, as an environmentally friendly adsorbent for the removal or recovery of chemicals. To broaden its application as an adsorbent, we carried out comprehensive experimental and theoretical analysis. From the experiments, adsorption affinity values between C. glutamicum and micropollutants were measured, and, based on the experimental values, we developed a predictive model. The experimental results reveal that the degree of adsorption is dependent on the structural properties of the micropollutants. In particular, the adsorbent has remarkable adsorption ability toward cations, whereas anionic and neutral compounds interact weakly with the adsorbent. In addition, we found that adsorption is affected by the sodium chloride concentration. Briefly, an increase in salt concentration increases the adsorption of anions, whereas the opposite behavior is observed for cations. In contrast, the adsorption of neutral compounds was not affected by the presence of salt. The modeling studies revealed that a linear free energy relationship model can be used to predict the adsorption affinity. Based on the developed model, we found that hydrogen-bond basicity, anionic coulombic interactions, and molecular volume are the main contributing factors to the adsorption model. However, to achieve the best predictability (a coefficient of determination (R2) of 0.902), additional parameters, such as the dipolarity/polarizability and dispersive interaction, should be included. This indicates that adsorption is a product of complex interactions.

Two-Phase System Model to Assess Hydrophobic Organic Compound Sorption to Dissolved Organic Matter.

The equilibrium partition of organic compounds to dissolved organic matter (DOM) is an essential process that affects their environmental risks. Traditional models cannot accurately assess this process as the variability of DOM is not properly accounted for. The two-phase system (TPS) model was developed with the consideration of the variability that stems from both organic compounds and DOM. In this study, we examined the applicability of the TPS model for the prediction of the organic carbon-water partition coefficient (KOC) of hydrophobic organic compound (HOC) sorption to aquatic and sediment DOM using a diverse set of 17 organic compounds and 53 DOM samples. The TPS model showed good predictive power (RMSE < 0.20) without calibration, outperforming currently used linear free energy relationship models (RMSE > 0.28). The significance of DOM properties in the sorption behavior was quantitatively analyzed based on the TPS model. The spatial pattern of KOC for HOC sorption to aquatic DOM in Lake Taihu, the third largest freshwater lake in China, was assessed using the TPS model. Our results suggest that the TPS model has great potential to facilitate the routine assessment of the partition behavior of HOCs in aquatic systems for environmental risk assessment.

The effect of particle and ligand types on retention and peak shape in liquid chromatography

A well performed, detailed characterization of stationary phase properties can serve as a useful tool for choice of suitable separation environment for a given purpose. This work shows effects of particle type/technology and ligand chemistry on retention and peak shape of neutral, acidic, basic and zwitterionic compounds. Eight columns differing in endcapping technology, particle type and ligand bonded were evaluated, namely XSelect CSH C18, XSelect CSH Phenyl-Hexyl, XSelect CSH Fluoro-Phenyl, XSelect HSS C18, XSelect HSS C18 SB, XSelect HSS PFP, XBridge C18 and XBridge Shield RP18. A simple Walters test for evaluation of hydrophobicity and silanol activity was performed. Mobile phases composed of acetonitrile and aqueous part (pH 2.1; pH 4.7 and pH 8.0) were used. More detailed description of interactions participating in the retention mechanism was provided by the linear free energy relationship model. For a more detailed characterization of possible electrostatic interactions, compounds with permanent charge across selected pH range were used. A good agreement between the Walters test results, the expanded linear free energy relationship model, and the test of ionic-interactions was observed, suggesting that each testing method provides value for characterization of the solute-stationary phase interactions.

Yolk-Water Partitioning of Neutral Organic Compounds in the Model Organism Danio rerio.

Yolk is the most important temporary biological compartment of the early life stages of fish embryos. The sorption strength of a chemical to yolk components may significantly influence the distribution of that chemical in the fish embryo. We determined yolk-water partition coefficients (Kyolk/water , in liters of water per kilogram of yolk, normalized to dry wt) for 70 neutral organic chemicals. The log Kyolk/water values range from 0.76 to 6.56. On the basis of these values, we developed polyparameter linear free energy relationship models to predict yolk-water partitioning for a broad range of neutral organic chemicals with a root mean squared error of 0.37 and r2 of 0.919. These models can be applied for the prediction of internal concentrations at equilibrium (neglecting biotransformation and active transport) in the zebrafish embryo test system. Environ Toxicol Chem 2020;39:1506-1516. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

New Dicationic Quinolinium Ionic Liquids for Capillary Gas Chromatography

Highly polar capillary columns with liquid stationary phases (LSPs) based on dicationic quinolinium ionic liquids (ILs) are studied. These ILs are characterized by high polarity and strong dipole–dipole and π–π interactions in a linear free energy relationship model, and can separate complex mixtures (containing aromatic hydrocarbons, phenols, catechols, and hydroquinones) that are problematic for columns with conventional LSPs. It is shown that the high thermal stability of dicationic quinolinium ILs allows to perform chromatographic separation up to a temperature of at least 300°C, which is impossible for any conventional polar and highly polar LSPs. The thermal stability of the studied LSPs is virtually independent of the structure of a dicationic quinolinium IL and the length of an alkyl bridge.

Versatile modelling of polyoxymethylene-water partition coefficients for hydrophobic organic contaminants using linear and nonlinear approaches

Environmental fate or transport of hydrophobic organic contaminants (HOCs) depends on the partitioning properties of compounds within various environmental phases. Due to the wide application of polyoxymethylene (POM) in the passive sampling technique, several in silico models were developed to predict POM-water partition coefficients (KPOM-w) in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). It is an attempt to combine conventional linear method (multiple linear regression, MLR) and popular nonlinear algorithm (artificial neural network, ANN) for estimating partition coefficients of HOCs. All models were performed on a dataset of 210 chemicals from 13 different classes. The polyparameter linear free energy relationship (pp-LFER) model included 5 molecular descriptors, namely, E, S, A, B and V, and predicted log KPOM-w with R2adj of 0.825. The values of statistical parameters including R2adj, Q2ext, RMSEtra and RMSEext for quantitative structure-property relationship (QSPR)-MLR and QSPR-ANN models with four descriptors (ALOGP, MeanDD, E1m and Mor24s) were: (0.928, 0.877, 0.498 and 0.649) and (0.943, 0.905, 0.443 and 0.571), with high similarity for both models, which confirmed the robustness, significance, and remarkable prediction accuracy of the QSPR models. Moreover, the mechanism interpretation revealed that the molecular volume and hydrophobicity had a major impact on distribution procedure of HOCs. The models developed herein, with the broad applicability domain (AD), provide suitable tools to fill the experimental data gap for untested chemicals and help researchers better understand the mechanistic basis of adsorption behavior of POM.

Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model

Predicting plant cuticle-water partition coefficients (Kcw) and understanding the partition mechanisms are crucial to assess environmental fate and risk of organic pollutants. Up to now, experimental Kcw values are determined for only hundreds of compounds because of high experimental cost. For this reason, computational models, which can predict Kcw values based on chemical structures, are promising approaches to evaluate new compounds. In this study, a large dataset consisting of 279 logKcw values for 125 unique compounds were collected and curated. A poly-parameter linear free energy relationship (pp-LFER) model was developed with stepwise multiple linear regression based on this dataset. The resulted pp-LFER model has good predictability and robustness as indicated by determination coefficient (R2adj,tra) of 0.93, bootstrapping coefficient (Q2BOOT) of 0.92, external validation coefficient (Q2ext) of 0.94 and root mean square error of 0.52 log units. Contribution analysis of different interactions indicated that dispersion and hydrophobic interactions have the highest positive contribution (56%) to increase the partition of pollutants onto plant cuticles. In addition, for organic pollutions containing benzene ring (13–31%), double bond (9–17%) or nitrogen-containing heterocycles (9–17%), π/n-electron pairs interactions exhibit obvious positive contributions to logKcw. In conclusion, the proposed pp-LFER model is beneficial for predicting logKcw of potential organic pollutants directly from their molecular structures.

Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases.

The permeation of the blood-brain barrier is a very important consideration for new drug candidate molecules. In this research, the reversed-phase liquid chromatography with different columns (Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester) was used to predict the penetration of the blood-brain barrier by 65 newly-synthesized drug-like compounds. The linear free energy relationships (LFERs) model (log BB = c + eE + sS + aA + bB + vV) was established for a training set of 23 congeneric biologically active azole compounds with known experimental log BB (BB = Cblood/Cbrain) values (R2 = 0.9039). The reliability and predictive potency of the model were confirmed by leave-one-out cross validation as well as leave-50%-out cross validation. Multiple linear regression (MLR) was used to develop the quantitative structure-activity relationships (QSARs) to predict the log BB values of compounds that were tested, taking into account the chromatographic lipophilicity (log kw), polarizability and topological polar surface area. The excellent statistics of the developed MLR equations (R2 > 0.8 for all columns) showed that it is possible to use the HPLC technique and retention data to produce reliable blood-brain barrier permeability models and to predict the log BB values of our pharmaceutically important molecules.

Development of pp-LFER and QSPR models for predicting the diffusion coefficients of hydrophobic organic compounds in LDPE.

Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R2) of 0.949 and cross-validation coefficient (QLoo2) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.

Predicting low density polyethylene-water partition coefficients based on pp-LFER and QSPR models using molecular descriptors

The partition coefficients of chemicals between sorbent material and water (KPE-w) are important to determine water phase chemical concentrations using passive sampling devices. Due to the wide application of low density polyethylene (LDPE) films, the polyparameter linear free energy relationship (pp-LFER) and quantitative structure property relationship (QSPR) models were established for predicting KPE-w values. With the pp-LFER model, V (McGowan's molar volume), B (hydrogen bond acceptor capacity) and A (hydrogen bond donor capacity) were screened as significant variables. However, this model had low correlation coefficient (R2) and cross-validation coefficient (Q2). To comprehensively study the mechanisms of the partition process, a QSPR model with 4 influential descriptors (CrippenLogP (Crippen octanol-water partition coefficient), CIC0 (neighborhood symmetry of 0-order), MATS3i (Moran autocorrelation-lag3/weighted by first ionization potential) and A (hydrogen bond donor capacity)) was developed. The values of R2 and Q2 ranged from 0.771 to 0.921 and 0.739 to 0.912, respectively, indicating that both models had good predictive capacity and robustness. Mechanism interpretation indicated that the elements influencing the distribution procedure between LDPE and water were the McGowan's molar volume and hydrophobic interactions. In the present study, a superb model was established to predict the log KPE-w values for a wide range of common hydrophobic organic pollutants.

Prediction of Apolar Compound Sorption to Aquatic Natural Organic Matter Accounting for Natural Organic Matter Hydrophobicity Using Aqueous Two-Phase Systems.

The equilibrium partitioning of organic compounds to natural organic matter (NOM) plays a key role in their environmental fate as well as bioavailability. In this study, a prediction model for organic compound sorption to NOM was theoretically derived based on two-phase systems. In this model, the hydrophobicity of NOM was scaled by their partition coefficients in an aqueous two-phase system (KATPS) and that of organics was scaled by their octanol-water partition coefficients (KOW). The model uses only KATPS and KOW as variables. Coefficients in the model were determined using a data set including the organic carbon-water partition coefficient (KOC) of four polycyclic aromatic hydrocarbons (PAHs) sorption to 10 NOM samples collected from surface waters. The resulting model was validated using additional NOM samples and reference NOM, which suggested good prediction power for PAH sorption to aquatic NOM. The model performance was compared with commonly used linear free energy relationship models, and its applicability was discussed. Sorption behavior unexpected by this model is attributed to additional sorption mechanisms other than partitioning. Overall, this approach allows prediction of KOC for apolar organic compound sorption to aquatic NOM simply using their respective partition coefficients in two-phase systems based on a specific model.

Experimental Validation of Hydrogen Atom Transfer Gibbs Free Energy as a Predictor of Nitroaromatic Reduction Rate Constants.

Nitroaromatic compounds (NACs) are a class of prevalent contaminants. Abiotic reduction is an important fate process that initiates NAC degradation in the environment. Many linear free energy relationship (LFER) models have been developed to predict NAC reduction rates. Almost all LFERs to date utilize experimental aqueous-phase one-electron reduction potential ( EH1) of NAC as a predictor, and thus, their utility is limited by the availability of EH1 data. A promising new approach that utilizes computed hydrogen atom transfer (HAT) Gibbs free energy instead of EH1 as a predictor was recently proposed. In this study, we evaluated the feasibility of HAT energy for predicting NAC reduction rate constants. Using dithionite-reduced quinones, we measured the second-order rate constants for the reduction of seven NACs by three hydroquinones of different protonation states. We computed the gas-phase energies for HAT and electron affinity (EA) of NACs and established HAT- and EA-based LFERs for six hydroquinone species. The results suggest that HAT energy is a reliable predictor of NAC reduction rate constants and is superior to EA. This is the first independent, experimental validation of HAT-based LFER, a new approach that enables rate prediction for a broad range of structurally diverse NACs based solely on molecular structures.

Prediction of adsorption properties for ionic and neutral pharmaceuticals and pharmaceutical intermediates on activated charcoal from aqueous solution via LFER model

In this study, we performed modeling analysis based on isothermal experimental data to develop linear free energy relationship (LFER) model, which is a very useful concept for predicting the molecular interaction of an adsorbate in a certain system. Thirty compounds containing cationic, anionic, and neutral were used, and the effect of diverse intermolecular interactions was captured by isotherm fitting of the Langmuir model. The predicted equation of distribution coefficient (Kd), adsorption capacity (qm), and adsorption affinity (Kad) of activated charcoal (AC) for compounds was developed. For the Kd values, an activity-dependent LFER model was developed, and could predict log Kd within R2 of 0.85. The determined system parameters and their statistical results revealed that the molecular volume (V) of compounds encouraged the adsorption from water to AC, whereas the hydrogen basicity (B) discouraged such adsorption. In the cases of qm and Kad, AC had relatively good capacities for neutrals, which were higher than those for cations and anions. Whereas, AC had stronger adsorption affinities with anions, compared to those for neutrals and cations. The phenomena were explained by the developed LFER models, in which the p−/n− electron interaction (E) made a great positive contribution to increase the adsorption capacity and affinity, while Coulombic interactions (J+ and J−) demonstrated a negative trend. Finally, the developed models can predict the qm and Kad in R2 of 0.86 and 0.76, respectively.

Skin Permeation of Solutes from Metalworking Fluids to Build Prediction Models and Test A Partition Theory.

Permeation of chemical solutes through skin can create major health issues. Using the membrane-coated fiber (MCF) as a solid phase membrane extraction (SPME) approach to simulate skin permeation, we obtained partition coefficients for 37 solutes under 90 treatment combinations that could broadly represent formulations that could be associated with occupational skin exposure. These formulations were designed to mimic fluids in the metalworking process, and they are defined in this manuscript using: one of mineral oil, polyethylene glycol-200, soluble oil, synthetic oil, or semi-synthetic oil; at a concentration of 0.05 or 0.5 or 5 percent; with solute concentration of 0.01, 0.05, 0.1, 0.5, 1, or 5 ppm. A single linear free-energy relationship (LFER) model was shown to be inadequate, but extensions that account for experimental conditions provide important improvements in estimating solute partitioning from selected formulations into the MCF. The benefit of the Expanded Nested-Solute-Concentration LFER model over the Expanded Crossed-Factors LFER model is only revealed through a careful leave-one-solute-out cross-validation that properly addresses the existence of replicates to avoid an overly optimistic view of predictive power. Finally, the partition theory that accompanies the MCF approach is thoroughly tested and found to not be supported under complex experimental settings that mimic occupational exposure in the metalworking industry.

Examining the Gas-Particle Partitioning of Organophosphate Esters: How Reliable Are Air Measurements?

Organophosphate esters (OPEs) in air have been found to be captured entirely on filters of typical active air samplers and thus designated as being in the particle phase. However, this particle fraction is unexpected, especially for more volatile tris(2-chloroethyl) phosphate (TCEP) and tris(chloroisopropyl) phosphate (TCIPP). We evaluated gas-particle partitioning in indoor and outdoor air for OPEs and polybrominated diphenyl ethers (PBDEs) using single-parameter models (Junge-Pankow, Harner-Bidleman) and poly-parameter linear free energy relationship (pp-LFER) models. We also used the pp-LFER to estimate filter-air partitioning in active air samplers. We found that all gas-particle partitioning models predicted that TCEP and TCIPP should be in the gas phase, contrary to measurements. The pp-LFER better accounted for OPE measurements than the single-parameter models, except for TCEP and TCIPP. Gas-particle partitioning of PBDEs was reasonably explained by all models. The pp-LFER for filter-air partitioning showed that gas-phase sorption to glass and especially quartz fiber filters used for active air samplers could account for up to 100% of filter capture and explain the high particle fractions reported for TCIPP, tris(1,3-dichloro-2-propyl) phosphate TDCIPP, and triphenyl phosphate TPhP, but not TCEP. The misclassification of gas-particle partitioning can result in erroneous estimates of the fraction of chemical subject to gas-phase reactions and atmospheric scavenging and, hence, atmospheric long-range transport.

Bonding of monocarboxylic acids, monophenols and nonpolar compounds onto goethite

Adsorption of a diverse set of chemicals onto goethite was evaluated by column chromatography. The pH of the effluents was 4.7–5.2. Van der Waals forces dominate the exothermic adsorption of 8 nonpolar compounds (e.g., PAHs and chlorobenzenes). H-bonding is responsible for the adsorption of 32 monocarboxylic acids (i.e., benzoic acids, naphthoic acids and acidic pharmaceuticals) and their adsorption tends to be endothermic. Steric effects significantly decreased the bonding of monocarboxylic acids with ortho-substitutions. Exothermic adsorption of 10 monophenols is controlled by weak H-bonding. Bonding of these 50 solutes onto goethite is totally reversible. In contrast, inner-sphere complexation of phthalic acid and chlortetracycline with goethite occurred according to their low desorption ratio (1.1%–54.4%). Polyparameter linear free energy relationship (PP-LFER) models were established to provide acceptable fitting results of the goethite-solute distribution coefficients (RMSE = 0.32 and 0.30 at 25 °C and 5 °C, respectively). It is worthy to note that steric effects must be considered to get a better prediction for compounds with ortho-substitutions.