Linear Free Energy Relationship Model Research Articles

Evaluation of the Goss-modified solvation parameter model for the characterization of biphasic systems and descriptor assignments

The solvation parameter model uses six descriptors identified as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L to model the distribution of neutral compounds in biphasic systems. Abraham's version of this model uses all six descriptors with two separate linear free energy relationship models for the transfer of compounds from a gas phase to a condensed phase and between condensed phases. Goss proposed a modification to this model that uses a single calibration model regardless of the physical state for each phase and five of the descriptors employed in Abraham's model (E descriptor is eliminated). The capability of Abraham's model and the Goss-modified model to characterize the contribution of intermolecular interaction to retention for gas and reversed-phase liquid chromatographic systems and distribution in liquid-liquid partition systems is evaluated using the WSU compound descriptor database. These more accurate values for the Abraham descriptors have not been utilized previously for the evaluation of the Goss-modified model and should be more capable of discerning subtle differences in model performance. It is shown that model quality defined by statistical parameters favors Abraham's model over the Goss-modified model with differences in model quality greater for systems in which Abraham's model indicates a significant contribution from electron lone pair interactions and for systems in which one phase is a solvent containing perfluoroalkyl substituents. There is a small systematic difference for the terms describing the combined contributions of cavity formation and dispersion interactions and for interactions of a dipole-type. The contribution of hydrogen-bonding interactions is virtually identical for the two models. The model intercepts are generally different and potentially assigned to a larger contribution from lack-of-fit for the Goss-modified model. Although the Abraham model descriptors have been routinely employed for applications using the Goss-modified model the possibility that Goss-model specific descriptors should be employed was evaluated. Using the Solver method and Goss-model specific calibration models for chromatographic and liquid-liquid partition systems a new set of Goss-specific descriptors was calculated for 28 varied compounds. These descriptors show good statistical agreement with the Abraham descriptor values with an average deviation of 0.009, -0.003, -0.004, and -0.023, respectively, for the S, A, B, and L descriptors, corresponding to a relative absolute deviation in percent of 2.2 %, 3.9 %, 4.3 %, and 1.2 %, respectively.

First insights into the bioaccumulation, biotransformation and trophic transfer of typical tetrabromobisphenol A (TBBPA) analogues along a simulated aquatic food chain

Tetrabromobisphenol A (TBBPA) analogues have been investigated for their prevalent occurrence in environments and potential hazardous effects to humans and wildlife; however, there is still limited knowledge regarding their toxicokinetics and trophic transfer in aquatic food chains. Using a developed toxicokinetic model framework, we quantified the bioaccumulation, biotransformation and trophic transfer of tetrabromobisphenol S (TBBPS) and tetrabromobisphenol A di(allyl ether) (TBBPA-DAE) during trophic transfer from brine shrimp (Artemia salina) to zebrafish (Danio rerio). The results showed that the two TBBPA analogues could be readily accumulated by brine shrimp, and the estimated bioconcentration factor (BCF) value of TBBPS (5.68 L kg-1 ww) was higher than that of TBBPA-DAE (1.04 L kg-1 ww). The assimilation efficiency (AE) of TBBPA-DAE in zebrafish fed brine shrimp was calculated to be 16.3%, resulting in a low whole-body biomagnification factor (BMF) in fish (0.684 g g-1 ww). Based on the transformation products screened using ultra-high-performance liquid chromatograph-high resolution mass spectrometry (UPLC-HRMS), oxidative debromination and hydrolysis were identified as the major transformation pathways of TBBPS, while the biotransformation of TBBPA-DAE mainly took place through ether bond breaking and phase-II metabolism. Lower accumulation of TBBPA as a metabolite than its parent chemical was observed in both brine shrimp and zebrafish, with metabolite parent concentration factors (MPCFs) < 1. The investigated BCFs for shrimp of the two TBBPA analogues were only 3.77 × 10-10 - 5.59 × 10-3 times of the theoretical Kshrimp-water based on the polyparameter linear free energy relationships (pp-LFERs) model, and the BMF of TBBPA-DAE for fish was 0.299 times of the predicted Kshrimp-fish. Overall, these results indicated the potential of the trophic transfer in bioaccumulation of specific TBBPA analogues in higher trophic-level aquatic organisms and pointed out biotransformation as an important mechanism in regulating their bioaccumulation processes. Environmental ImplicationThe internal concentration of a pollutant in the body determines its toxicity to organisms, while bioaccumulation and trophic transfer play important roles in elucidating its risks to ecosystems. Tetrabromobisphenol A (TBBPA) analogues have been extensively investigated for their adverse effects on humans and wildlife; however, there is still limited knowledge regarding their toxicokinetics and trophic transfer in aquatic food chains. This study investigated the bioaccumulation, biotransformation and trophic transfer of TBBPS and TBBPA-DAE in a simulated di-trophic food chain. This state-of-art study will provide a reference for further research on this kind of emerging pollutant in aquatic environments.

Predictive modeling for VOC adsorption on TiO2 filters: Implications for enhanced photocatalytic oxidation in indoor air quality management

Adsorption of volatile organic compounds (VOCs) onto the surface of a TiO2 filter is essential for initiating photocatalytic oxidation (PCO). This study investigated the adsorption coefficients (K) of 14 VOCs under different relative humidities (RH) and explored the influence of different air velocities on the adsorption of Methyl isobutyl ketone (MIBK). Consequently, a polyparameter linear free energy relationship (pp-LFER) model was established to predict lnK at 35 % RH (lnK35%). Moreover, the study found that lnK has a linear relationship with RH, represented as lnK=αRH+β. The parameters ′α′ and ′β′ can be accurately predicted using the Henry’s Law constant (K°OH) and lnK35%, respectively. By combining the pp-LFER model with regression equations for ′α′ and ′β′, it is possible to predict lnK for VOCs across various RH levels. The study also revealed that the adsorption coefficient of increases with escalating air velocities (0.04 – 0.34 m/s), primarily due to mass transfer. The observed low adsorption performance for aldehydes, commonly seen as by-products in the PCO process, suggests cautious usage of the PCO filtration system in practice before the by-products are eliminated downstream. This study offers valuable insights for the practical application of PCO technologies in the comprehensive removal of indoor VOCs.

Comparison of System Constants of Thermostable Polysiloxane Stationary Liquid Phases of Different Origins

The expansion of possible areas of use of modern gas chromatography is largely determined by the thermal stability of stationary liquid phases (SLP), which allows the separation of complex mixtures of high-boiling compounds. A way to increase the thermal stability of nonpolar and weakly polar polysiloxane SLPs is to introduce rigid fragments into the polysiloxane chain. Currently, there is an extensive set of columns with thermostable SLPs, which, in some cases, have the index “ms”. However, in most cases, the nature of these phases and the methods for preparing the columns are not disclosed. In this work, using the linear free energy relationship (LFER) model, we compared the selectivity of a number of proprietary columns with in-house columns in which the siloxane–silarylene phase and the column preparation methods themselves are known. Various types of intermolecular interactions are considered within the framework of the LFER model.

Predicting adsorption of organic compounds onto graphene and black phosphorus by molecular dynamics and machine learning.

With an increase in production and application of various engineering nanomaterials (ENMs), they will inevitably be released into the environment. Adsorption of various organic chemicals onto ENMs will impact on their environmental behavior and toxicology. It is unrealistic to experimentally determine adsorption equilibrium constants (K) for the vast number of organics and ENMs due to high cost in expenditure and time. Herein, appropriate molecular dynamics (MD) methods were evaluated and selected by comparing experimental K values of seven organics adsorbed onto graphene with the MD-calculated ones. Machine learning (ML) models on K of organics adsorption onto graphene and black phosphorus nanomaterials were constructed based on a benchmark data set from the MD simulations. Lasso models based on Mordred descriptors outperformed ML models built by support vector machine, random forest, k-nearest neighbor, and gradient boosting decision tree, in terms of cross-validation coefficients (Q2 > 0.90). The Lasso models also outperformed conventional poly-parameter linear free energy relationship models for predicting logK. Compared with previous models, the Lasso models considered more compounds with different functional groups and thus have broader applicability domains. This study provides a promising way to fill the data gap in logK for chemicals adsorbed onto the ENMs.

Linear Free Energy Relationship for Predicting the Rate Constants of Munition Compound Reduction by the Fe(II)-Hematite and Fe(II)-Goethite Redox Couples.

Abiotic reduction by iron minerals is arguably the most important fate process for munition compounds (MCs) in subsurface environments. No model currently exists that can predict the abiotic reduction rates of structurally diverse MCs by iron (oxyhydr)oxides. We performed batch experiments to measure the rate constants for the reduction of three classes of MCs (poly-nitroaromatics, nitramines, and azoles) by hematite or goethite in the presence of aqueous Fe2+. The surface area-normalized reduction rate constant (kSA) depended on the aqueous-phase one-electron reduction potential (EH1) of the MC and the thermodynamic state (i.e., pe and pH) of the iron oxide-Feaq2+ system. A linear free energy relationship (LFER), similar to that reported previously for nitrobenzene, successfully captures all MC reduction rate constants that span 6 orders of magnitude: . The finding that the rate constants of all the different classes of MCs can be described by a single LFER suggests that these structurally diverse nitro compounds are reduced by iron oxide-Feaq2+ couples through a common mechanism up to the rate-limiting step. Multiple mechanistic implications of the results are discussed. This study expands the applicability of the LFER model for predicting the reduction rates of legacy and emerging MCs and potentially other nitro compounds.

Molecular Docking and QSAR Studies of Indole Derivatives as Antifungal Agents

Introduction: Fungal infection and resistance to existing antibiotics is a major problem across the globe, which is associated with the inappropriate use of antibiotics in humans, animals farming, and agriculture. Unfortunately, addressing these difficulties is becoming extremely critical as they arise. In this paper, we used Hansch and Fujita's linear free energy relationship (LFER) model to perform 2D QSAR (quantitative structure activity relationships) on 28 known Indole derivatives. Objective: Molecular docking and 2D QSAR studies of Indole derivatives as antifungal agents. Methods: 2D QSAR was performed on the reported antifungal activity data of 28 selected compounds based on theoretical chemical descriptors to construct statistical models that link structural properties of Indole derivatives to antifungal activity. To represent the structural properties of compounds, a collection of molecular descriptors such as geometric, topological, hydrophobic and electronic characters were determined. Biological activity data was first translated into pMIC values (i.e. -log MIC) and used as a predictor variable in the QSAR analysis. We have also performed molecular docking studies of same compounds by using Maestro module of Schrodinger. Result: All docked compounds had better binding scores with reference to fluconazole. Conclusion: Compounds with high values of electronic energy and dipole moment will be effective against fungi, and Indole derivatives having lowered κ2 values will be effective antifungal agents.

Electron Transfer Energy and Hydrogen Atom Transfer Energy-Based Linear Free Energy Relationships for Predicting the Rate Constants of Munition Constituent Reduction by Hydroquinones.

No single linear free energy relationship (LFER) exists that can predict reduction rate constants of all munition constituents (MCs). To address this knowledge gap, we measured the reduction rates of MCs and their surrogates including nitroaromatics [NACs; 2,4,6-trinitrotoluene (TNT), 2,4-dinitroanisole (DNAN), 2-amino-4,6-dinitrotoluene (2-A-DNT), 4-amino-2,6-dinitrotoluene (4-A-DNT), and 2,4-dinitrotoluene (DNT)], nitramines [hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and nitroguanidine (NQ)], and azoles [3-nitro-1,2,4-triazol-5-one (NTO) and 3,4-dinitropyrazole (DNP)] by three dithionite-reduced quinones (lawsone, AQDS, and AQS). All MCs/NACs were reduced by the hydroquinones except NQ. Hydroquinone and MC speciations were varied by controlling pH, permitting the application of a speciation model to determine second-order rate constants (k) from observed pseudo-first-order rate constants. The intrinsic reactivity of MCs (oxidants) decreased upon deprotonation, while the opposite was true for hydroquinones (reductants). The rate constants spanned ∼6 orders of magnitude in the order NTO ≈ TNT > DNP > DNT ≈ DNAN ≈ 2-A-DNT > DNP- > 4-A-DNT > NTO- > RDX. LFERs developed using density functional theory-calculated electron transfer and hydrogen atom transfer energies and reported one-electron reduction potentials successfully predicted k, suggesting that these structurally diverse MCs/NACs are all reduced by hydroquinones through the same mechanism and rate-limiting step. These results increase the applicability of LFER models for predicting the fate and half-lives of MCs and related nitro compounds in reducing environments.

Adsorption of ionic and neutral pharmaceuticals and endocrine-disrupting chemicals on activated carbon fiber: batch isotherm and modeling studies

Activated carbon fiber (ACF) has received increasing attention as an adsorbent due to its excellent surface properties. However, the adsorption mechanism of ACF for micropollutants, especially those in ionic forms, has not been sufficiently characterized to date. Therefore, the adsorption property of ACF was characterized using isotherm experiments and linear free energy relationship (LFER). For the experiments, adsorption affinities of thirty-five chemicals, i.e., pharmaceuticals and endocrine-disrupting chemicals, on ACF were estimated. Afterward, the adsorption affinities were used as dependent variables to build the LFER modeling. Finally, three isolated models for each chemical species, i.e., cations, anions, and neutrals, and a comprehensive model for the whole dataset were developed. The LFER results revealed that the models for anionic and neutral compounds have high predictabilities in R2 of 0.97 and 0.96, respectively, while that for cations has a slightly lower R2 of 0.72. In the comprehensive model including cationic, anionic, and neutral compounds, the accuracy of it is 0.81. From the developed LFER model based on the whole dataset, the adsorption mechanisms of ACF for the selected substances could be interpreted, in which the terms of hydrophobic interaction, hydrogen bonding basicity, and anionic Coulombic force of the compounds were identified as the predominant interactions with ACF.

Pollution profiles, influencing factors, and source apportionment of target and suspect organophosphate esters in ambient air: A case study in a typical city of Northern China.

Organophosphate esters (OPEs) are attracting attention because they pose risks to biota, including humans. Little research has been performed into the environmental fates of OPEs in the atmosphere. Here, target/suspect OPEs were determined in 122 atmosphere samples (gas phase (n=31), PM2.5 (n=30), PM10 (n=30), and total suspended particles (n=31)) from a city in Northern China. Pollution profiles were established, influencing factors identified, and sources apportioned. We found 12 target OPEs and 29 suspect OPEs. The target and suspect OPE concentrations in the ambient air samples were 2.2-172.5 and 0.7-53.9ng/m3, respectively. Tris(chloroethyl) phosphate, tris(1-chloro-2-propyl) phosphate, and tris(2,4-di-t-butylphenyl) phosphate were the dominant OPEs in all samples. The OPEs were not in equilibrium, indicated by a multi-parameter linear free energy relationship model. The air quality index and OPE concentrations significantly correlated, indicating that OPE pollution is often more serious during weather with worse air quality. The target and suspect screening strategy and a positive matrix factorization model allowed OPE sources to be apportioned, improving our understanding of OPE sources. The four dominant sources were (1) construction, (2) indoor emissions, (3) the plastic industry and industrial activities, and (4) traffic emissions, textiles, and foam products.

Measurements and correlation of activity coefficients at infinite dilution for organic solutes in 1-heptyl-3-methylimidazolium chloride

This work is a continuation of our studies on the determination of activity coefficients at infinite dilution (γi∞) for organic solutes (i) in ionic liquids (ILs) and their use in separation processes. The values of γi∞ for 31 organic solutes including alkanes, cycloalkanes, 1,4-dioxane, aromatic hydrocarbons, cyclohexene, chloromethanes, acetonitrile, acetone, tetrahydrofuran, ethyl acetate and alcohols in the IL 1-heptyl-3-methylimidazolium chloride ([C7MIm]Cl) at six temperatures in range of (313.15–363.15) K were determined by gas-liquid chromatography (GLC). The density of [C7MIm]Cl as a function of temperature at pressure p= 101.33 kPa was measured and the values of gas-liquid partition coefficients (KL) were obtained. The linear relationship between lnγi∞ and 1/T was fitted by the Gibbs-Helmholtz equation. The molar excess thermodynamic functions including the infinite dilution partial molar excess Gibbs free energies (G¯iE,∞), partial molar excess enthalpies (H¯iE,∞) and entropies (TrefS¯iE,∞) at Tref = 298.15 K were calculated. In addition, the solubility parameters (δi) of [C7MIm]Cl were obtained from the regular solution theory combined with Flory “combinatorial” equation. The interaction between the solute and the IL was quantified based on the linear free energy relationship (LFER) model. The infinite dilution selectivity coefficients (Sij∞) of n-hexane (i)/benzene (j) and cyclohexane (i)/benzene (j) were calculated at T = 323.15 K from γi∞ and compared to literature values for [Cl]-based ILs for the same separation problems.

Performance evaluation and prediction of activated carbon for VOCs via experiments and LFER methods

Due to the diversity and fluidity of volatile organic compounds (VOCs), it is difficult to conduct experimental studies on activated carbon (AC) used to capture VOCs in industry. Therefore, predicting the adsorption capacity of AC for VOCs is of great significance for evaluating the adsorption performance of AC and developing its promising applications. Herein, based on the experimental data of dynamic adsorption, the poly parameter linear free energy relationship (pp-LFER) model was developed to quantify the interactions between VOCs and AC. Through the dynamic adsorption curves of 25 VOCs on a fixed bed packed with the AC, the partition coefficient (PC) and adsorption capacity (q) of AC for VOCs were calculated, and the pp-LFER equations used to predict them were established. For the PC values, the pp-LFER model consists of the excess molar refractive index (E), molar polarizability (S), hydrogen-bond acidity (A), and logarithmic hexadecane-air partition coefficient (L), and the R2 of it was 0.86. For the ln q values, the developed model can predict it in R2 of 0.83, and the increased adsorption capacity was predominantly attributed to the dispersive interaction (lL). Finally, the internal and external validation results confirmed that the developed models have reliability, robustness, and predictability.

Solute–Solvent Interactions in Hydrophilic Interaction Liquid Chromatography: Characterization of the Retention in a Silica Column by the Abraham Linear Free Energy Relationship Model

The Abraham linear free energy relationship model has been used to characterize a hydrophilic interaction liquid chromatography (HILIC) silica column with acetonitrile/water and methanol/water mobile phases. Analysis by the model for acetonitrile/water mobile phases points to solute volume and hydrogen bond basicity as the main properties affecting retention, whereas solute hydrogen bond acidity, dipolarity and polarizability practically do not affect it. Formation of a cavity is easier in acetonitrile-rich mobile phases than in the aqueous stationary phase, and hence increase of solute volume decreases retention. Conversely, hydrogen bond acidity is stronger in the aqueous stationary phase than in the acetonitrile-rich mobile phase and thus an increase of solute hydrogen bond basicity increases retention. Results are similar for methanol/water mobile phases with the difference that solute hydrogen bond acidity is significant too. Increase in hydrogen bond acidity of the solute decreases retention showing that methanol mobile phases must be better hydrogen bond acceptors than acetonitrile ones, and even than water-rich stationary phases. The results are like the ones obtained in zwitterionic HILIC columns bonded to silica or polymer supports for acetonitrile/water mobile phases, but different for solute hydrogen bond acidity for a polymer bonded zwitterionic column with methanol/water mobile phases, indicating that bonding support plays an important role in HILIC retention. Comparison to RPLC characterized systems confirms the complementarity of HILIC systems to RPLC ones because the main properties affecting retention are the same but with reversed coefficients. The least retained solutes in RPLC are the most retained in HILIC.

Applicability Domain of Polyparameter Linear Free Energy Relationship Models Evaluated by Leverage and Prediction Interval Calculation.

Polyparameter linear free energy relationships (PP-LFERs) are accurate and robust models employed to predict equilibrium partition coefficients (K) of organic chemicals. The accuracy of predictions by a PP-LFER depends on the composition of the respective calibration data set. Generally, extrapolation outside the domain defined by the calibration data is likely to be less accurate than interpolation. In this study, the applicability domain (AD) of PP-LFERs was systematically evaluated by calculating the leverage (h) and prediction interval (PI). Repeated simulations with experimental data showed that the root mean squared error of predictions increased with h. However, the analysis also showed that PP-LFERs calibrated with a large number (e.g., 100) of training data were highly robust against extrapolation error. For such PP-LFERs, the common definition of extrapolation (h > 3 hmean, where hmean is the mean h of all training compounds) may be excessively strict. Alternatively, the PI is proposed as a metric to define the AD of PP-LFERs, as it provides a concrete estimate of the error range that agrees well with the observed errors, even for extreme extrapolations. Additionally, published PP-LFERs were evaluated in terms of their AD using the new concept of AD probes, which indicated the varying predictive performance of PP-LFERs in the existing literature for environmentally relevant compounds.

Simulating and Predicting Adsorption of Organic Pollutants onto Black Phosphorus Nanomaterials.

Layered black phosphorus (BP) has exhibited exciting application prospects in diverse fields. Adsorption of organics onto BP may influence environmental behavior and toxicities of both organic pollutants and BP nanomaterials. However, contributions of various intermolecular interactions to the adsorption remain unclear, and values of adsorption parameters such as adsorption energies (Ead) and adsorption equilibrium constants (K) are lacking. Herein, molecular dynamic (MD) and density functional theory (DFT) was adopted to calculate Ead and K values. The calculated Ead and K values for organics adsorbed onto graphene were compared with experimental ones, so as to confirm the reliability of the calculation methods. Polyparameter linear free energy relationship (pp-LFER) models on Ead and logK were developed to estimate contributions of different intermolecular interactions to the adsorption. The adsorption in the gaseous phase was found to be more favorable than in the aqueous phase, as the adsorbates need to overcome cohesive energies of water molecules onto BP. The affinity of the aromatics to BP was comparable to that of graphene. The pp-LFER models performed well for predicting the Ead and K values, with external explained variance ranging from 0.90 to 0.97, and can serve as effective tools to rank adsorption capacities of organics onto BP.

Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin

Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R2 are 0.940 and 0.976) than the published NN-LFER model (R2 are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log Ce, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

Investigation of partition coefficients and fingerprints of atmospheric gas- and particle-phase intermediate volatility and semi-volatile organic compounds using pixel-based approaches

Ambient gas- and particle-phase intermediate volatility and semi-volatile organic compounds (I/SVOCs) of Beijing were analyzed by a thermal desorption comprehensive two-dimensional gas chromatography quadrupole mass spectrometry (TD-GC × GC-qMS). A pixel-based scheme combing the integration-based approach was applied for partition coefficients estimation and fingerprints identification. Blob-by-blob recognition was firstly utilized to characterize I/SVOCs from the molecular level. 412 blobs in gas-phase and 460 blobs in particle-phase were resolved, covering a total response of 47.5% and 43.5%. A large pool of I/SVOCs was found with a large diversity of chemical classes in both gas- and particle-phase. Acids (8.5%), b-alkanes (5.8%), n-alkanes (C8-C25, 5.3%), and aromatics (4.4%) were dominant in gas-phase while esters (7.0%, including volatile chemical product compounds, VCPs), n-alkanes (C9-C34, 5.7%), acids (4.6%), and siloxanes (3.6%) were abundant in particle-phase. Air pollutants were then evaluated by a two-parameter linear free energy relationship (LFER) model, which could be further implemented in the two-dimensional volatility basis set (2D-VBS) model. Multiway principal component analysis (MPCA) and partial least squares-discriminant analysis (PLS-DA) implied that naphthalenes, phenol, propyl-benzene isomers, and oxygenated volatile organic compounds (OVOCs) were key components in the gas-phase under different pollution levels. This work gives more insight into property estimation and fingerprints identification for complex ambient samples.

Adsorption modeling of microcrystalline cellulose for pharmaceutical-based micropollutants

Cellulose can be considered as a raw material for the production of filters and adsorbents for the removal of micropollutants, particularly in pharmaceutical-based products. To study its applications, it is important to estimate the adsorptive interaction of cellulose with the targeted chemicals, and develop predictive models for the expandable estimation into various types of micropollutants. Therefore, the adsorption affinity between cellulose and micropollutants was measured through isotherm experiments, and a quantitative structure-adsorption relationship model was developed using the linear free energy relationship (LFER) equation. The results indicate that microcrystalline cellulose has a remarkably high adsorption affinity with cationic micropollutants. Moreover, it has interactions with neutral and anionic micropollutants, although they have relatively lower affinities than those of cations. Through a modeling study, an LFER model - comprising of excess molar refraction, polar interaction, molecular volume, and charge-related terms - was developed, which could be used to predict the adsorption affinity values with an R2 of 0.895. To verify the robustness and predictability of the model, internal and external validation studies were performed. The results proved that the model was reasonable and acceptable, with an SE = 0.207 log unit.

Evaluation of Drug Sorption on Laboratory Materials with Abraham Solvation Parameters of Drugs and its Prevention.

Undesired drug sorption on laboratory material surfaces reduces the performance of analytical methods and results in the generation of unreliable data. Hence, we characterized the sorption of drugs and evaluated the sorption extent using a linear free energy relationship (LFER) model with Abraham solvation parameters of drugs. Furthermore, to prevent sorption, the effects of additives, such as organic solvents and salts, were evaluated. The sorption of fifteen model drugs (concentration: 2μM), with various physicochemical properties, on materials in 0.2% dimethyl sulfoxide aqueous solutions was evaluated. Drug sorption extent on the materials was determined using high-performance liquid chromatography. The obtained results were analyzed using an LFER model with Abraham solvation parameters of the drugs. The effect of additives on the sorption of itraconazole, one of the most hydrophobic drugs among those tested in this study, was investigated. Sorption was dependent on the physicochemical properties of drugs, rather than the type of materials used, and additives altered the rate of drug sorption. Equations were developed to evaluate the sorption extent (nmol) of drugs to glass and polypropylene using the Abraham solvation parameters of the drugs. LFER modeling with Abraham solvation parameters of drugs enabled us to evaluate drug sorption on materials. All the additives altered the rate of drug sorption, and some organic solvents effectively prevented sorption. The developed LFER model would be useful for assessment of the sorption properties of compounds in in vitro evaluations in drug discovery research and various other biochemical fields.

Organic acid-based linear free energy relationship models for green leaching of strategic metals from spent lithium-ion batteries and improvement of leaching performance

Water-soluble organic acids (e.g., acetic acid, acrylic acid, and ascorbic acid), as green leachants, have been applied to leach strategic metals (Ni, Li, Mn, and Co) from spent lithium-ion batteries (LIBs). Organic acid-based linear free energy relationship models were developed and simulated to predict the feasibility of the leaching efficiency for each of the strategic metals based on in silico calculated descriptors. The developed models, with accuracy (R2) of 0.747–0.831, reveal that hydrogen bond acidity of organic acids promotes the leaching efficiency, whereas molecular volume or excess molar refraction inhibits the efficiency. Furthermore, toxicity (lethal dose 50%) of organic acids was discussed along with the predicted leaching efficiency to explore more green and efficient organic acids. Considering both toxicity and leaching efficiency, citric acid was selected as a green and efficient leachant. To more improve the leaching performance (rate and efficiency) of citric acid, glucose as a green reductant and microwave treatment were additionally applied. Under the selected conditions, the leaching efficiencies after 1 h for Ni, Li, Mn, and Co were enhanced up to 98.3%, 99.1%, 98.7%, and 97.7%, respectively.