Adsorptive removal of micropollutants by natural and faujasite zeolites: Structural effect of micropollutants on adsorption.

To effectively characterize natural zeolite powder (ZP) and faujasite zeolite (FAU) as adsorbents to remove a wide variety of organic micropollutants, quantitative structure-activity relationship (QSAR) models for the adsorption of zeolites were developed. For this purpose, batch isotherms were performed to measure the adsorption affinity (Kd) between zeolite and organic micropollutants, and the measured Kd values were used as a dependent variable in the QSAR modeling. In the modeling, the concept of a linear free energy relationship (LFER) was employed and used either empirically measured or in silico calculated descriptors. Modeling results based on empirical descriptors showed that log Kd values for ZP could be predicted with R2 = 0.949 and standard error (SE) = 0.137 log units, and for FAU, R2 = 0.895 and SE = 0.144 log units. A test set was used to validate the models developed by the training set. The predictabilities of the models for the test set were R2 = 0.907 and SE = 0.209 log units for ZP and R2 = 0.784 and SE = 0.236 log units for FAU, indicating that the models have reasonable robustness and predictability. Also, we showed that in silico-based descriptors could be applied to the prediction. These findings may help determine the general coverage of ZP and FAU zeolites and identify suitable applications.

Investigation of the adsorption affinity of organic micropollutants on seaweed and its QSAR study.

Seaweed, one of the most abundant biomaterials, can be used as a biosorbent to remove organic micropollutants. In order to effectively use seaweed to remove a variety of micropollutants, it is vital to rapidly estimate the adsorption affinity according to the types of micropollutants. Thus, the isothermal adsorption affinities of 31 organic micropollutants in neutral or ionic form on seaweed were measured, and a predictive model using quantitative structure-adsorption relationship (QSAR) modeling was developed. As a result, it was found that the types of micropollutants had a significant effect on the adsorption of seaweed, as expected, and QSAR modeling with a predictability (R2) of 0.854 and a standard error (SE) of 0.27 log units using a training set could be developed. The model's predictability was internally and externally validated using leave-one-out cross validation and a test set. Its predictability for the external validation set was R2 = 0.864, SE = 0.171 log units. Using the developed model, we identified the most important driving forces of the adsorption at the molecular level: Coulomb interaction of the anion, molecular volume, and H-bond acceptor and donor, which significantly affect the basic momentum of molecules on the surface of seaweed. Moreover, in silico calculated descriptors were applied to the prediction, and the results revealed reasonable predictability (R2 of 0.944 and SE of 0.17 log units). Our approach provides an understanding of the adsorption process of seaweed for organic micropollutants and an efficient prediction method to estimate the adsorption affinities of seaweed and micropollutants in neutral and ionic forms.

Modeling for the estimating the adsorption property of fruit waste-based biosorbents for the removal of organic micropollutants.

The enormous production of fruit waste and the generation of countless organic micropollutants are serious environmental problems. To solve the problems, the biowastes, i.e., orange, mandarin, and banana peels, were used as biosorbents to remove the organic pollutants. In this application, the difficult challenge is knowing the degree of adsorption affinity of biomass for each type of micropollutant. However, since there are numerous micropollutants, it requires enormous material consumption and labor to physically estimate the adsorbability of biomass. To address this limitation, quantitative structure-adsorption relationship (QSAR) models for the adsorption assessment were established. In this process, the surface properties of each adsorbent were measured with instrumental analyzers, their adsorption affinity values for several organic micropollutants were determined through isotherm experiments, and QSAR models for each adsorbent were developed. The results showed that the tested adsorbents had significant adsorption affinity for cationic and neutral micropollutants, while the anionic one had low adsorption. As a result of the modeling, it was found that the adsorption could be predicted for a modeling set with an R2 of 0.90-0.915, and the models were validated via the prediction of a test set that was not included in the modeling set. Also, using the models, the adsorption mechanisms were identified. It is speculated that these developed models can be used to rapidly estimate adsorption affinity values for other micropollutants.

Adsorption of organic micropollutants on yeast: Batch experiment and modeling.

Yeast is ubiquitous and may act as a solid phase in natural aquatic systems, which may affect the distribution of organic micropollutants (OMs). Therefore, it is important to understand the adsorption of OMs on yeast. Therefore, in this study, a predictive model for the adsorption values of OMs on the yeast was developed. For that, an isotherm experiment was performed to estimate the adsorption affinity of OMs on yeast (i.e., Saccharomyces cerevisiae). Afterwards, quantitative structure-activity relationship (QSAR) modeling was performed for the purpose of developing a prediction model and explaining the adsorption mechanism. For the modeling, empirical and in silico linear free energy relationship (LFER) descriptors were applied. The isotherm results showed that yeast adsorbs a wide range of OMs, but the magnitude of Kd strongly depends on the types of OMs. The measured log Kd values of the tested OMs ranged from -1.91 to 1.1. Additionally, it was confirmed that the Kd measured in distilled water is comparable to that measured in real anaerobic or aerobic wastewater (R2 = 0.79). In QSAR modeling, the Kd value could be predicted by the LFER concept with an R2 of 0.867 by empirical descriptors and an R2 of 0.796 by in silico descriptors. The adsorption mechanisms of yeast for OMs were identified in individual correlations between log Kd and each descriptor: Dispersive interaction, hydrophobicity, hydrogen-bond donor, and cationic Coulombic interaction of OMs attract the adsorption, while the hydrogen-bond acceptor and anionic Coulombic interaction of OMs act as repulsive forces. The developed model can be used as an efficient method to estimate OM adsorption to yeast at a low level of concentration.

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.

Quantitative analysis of molecular interaction potentials of ionic liquid anions using multi-functionalized stationary phases in HPLC.

The molecular interaction potentials, including S (dipolarity/polarizability), A (hydrogen bonding acidity), and B (hydrogen bonding basicity), of anions are experimentally determined using multi-functionalized stationary phases in high-performance liquid chromatography (HPLC) systems. We employ three different multi-functionalized stationary phase columns (Obelisc R, Obelisc N, and Acclaim Trinity-P1) combined with two ingredients, namely, acetonitrile (ACN) and methanol (MeOH). These conditions can cause neutral, cationic, and anionic compounds to be retained. By using the retention characteristics of calibration compounds, including cations, anions, and neutral compounds, system parameters including the ionic interaction terms (zc Zc , za Za ) are evaluated using multiple linear regression, resulting in a standard deviation (SD) of 0.090-0.158 log units. Based on the system parameters and retention characteristics of the anions of interest, their molecular interaction potentials are characterized on the same scale for neutral and cationic molecules. Furthermore, to verify the determined molecular interaction potentials, we predict anion hydrophobicity. The results show that the determined S, A, and B, together with the computable descriptors E (excess molar refraction) and V (McGowan volume), can predict anion hydrophobicity with R(2) =0.982 and SD=0.167 (dimensionless).

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.

Determination of LFER descriptors of 30 cations of ionic liquids--progress in understanding their molecular interaction potentials.

In order to understand molecular interaction potentials of 30 cations of ionic liquids (ILs), the well-known linear free energy relationship concept (LFER) was applied. The LFER descriptors for the excess molar refractivity and the molar volume were calculated in silico and for hydrogen-bonding acidity and basicity, and the polarizability/dipolarity of IL cations were experimentally determined through high performance liquid chromatography (HPLC) measurements. For the study, three different columns (RP-select B, Cyan, and Diol) and buffered mobile phases, based on two organic solvents acetonitrile (ACN) and methanol (MeOH), were selectively combined to the HPLC separation systems RP-select B-ACN, RP-select B-MeOH, Cyan-MeOH, Diol-ACN, and Diol-MeOH. By measuring the retention factors of 45 neutral calibration compounds and calculating LFER descriptors of three cations in the HPLC systems, the system parameters, including an ionic z coefficient, were determined. Conversely, the LFER descriptors of 30 ionic liquid cations were determined, based on the parameters of five systems and their retention factors in the HPLC systems. The results showed that the type of head group, alkyl chain length and further substituents of the cation have a significant influence on the dipolarity/polarizability and the hydrogen-bonding acidity, and functionalized groups (hydroxyl, ether, and dimethylamino) lead to hydrogen-bonding basicity of the cation. The characterization of cationic LFER descriptors opens up the chance for a more quantitative understanding of molecular interaction potentials and physicochemical properties of ILs.

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.

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.

Review of the toxic effects of ionic liquids.

Interest in ionic liquids (ILs), called green or designer solvents, has been increasing because of their excellent properties such as thermal stability and low vapor pressure; thus, they can replace harmful organic chemicals and help several industrial fields e.g., energy-storage materials production and biomaterial pretreatment. However, the claim that ILs are green solvents should be carefully considered from an environmental perspective. ILs, given their minimal vapor pressure, may not directly cause atmospheric pollution. However, they have the potential to cause adverse effects if leaked into the environment, for instance if they are spilled due to human mistakes or technical errors. To estimate the risks of ILs, numerous ILs have had their toxicity assessed toward several micro- and macro-organisms over the past few decades. Since the toxic effects of ILs depend on the method of estimating toxicity, it is necessary to briefly summarize and comprehensively discuss the biological effects of ILs according to their structure and toxicity testing levels. This can help simplify our understanding of the toxicity of ILs. Therefore, in this review, we discuss the key findings of toxicological information of ILs, collect some toxicity data of ILs to different species, and explain the influence of IL structure on their toxic properties. In the discussion, we estimated two different sensitivity values of toxicity testing levels depending on the experiment condition, which are theoretical magnitudes of the inherent sensitivity of toxicity testing levels in various conditions and their changes in biological response according to the change in IL structure. Finally, some perspectives, future research directions, and limitations to toxicological research of ILs, presented so far, are discussed.

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.

Effective adsorption of mercury by Zr(IV)-based metal-organic frameworks of UiO-66-NH2 from aqueous solution.

In this study, Zr-based metal-organic frameworks (MOFs) of UiO-66 and UiO-66-NH2 were synthesized and applied to removal of mercury from aqueous solution. The characterizations of UiO-66 and UiO-66-NH2 were examined by X-ray diffraction (XRD), scanning electron microscope (SEM), and thermogravimetric analysis (TGA). To investigate the adsorption properties of UiO-66-NH2 for mercury, the experiments of kinetics, isotherm, pH, temperature, and salt concentration were conducted, and the results were compared with those by UiO-66. The result showed that UiO-66-NH2 has a higher adsorption capacity for mercury than UiO-66. The maximum adsorption capacity of UiO-66-NH2 was 223.8 ± 17.8 mg g-1 at 313 K. The salt concentration of NaCl has a significant effect on the adsorption of mercury on UiO-66, while UiO-66-NH2 can maintain the stable adsorption capacity for mercury in the concentration range of 0.1-0.5 M NaCl. Adsorption thermodynamics result indicated that the adsorption process of mercury on UiO-66-NH2 was spontaneous and endothermic. Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) analyses showed that the mercury was successfully adsorbed on the surface of UiO-66-NH2 and amino functional group as a soft base played a major role to react with mercury during the adsorption process. Graphical abstract.

Biodegradation of polystyrene by bacteria from the soil in common environments.

Polystyrene (PS), a major plastic waste, is difficult to biodegrade due to its unique chemical structure that comprises phenyl moieties attached to long linear alkanes. In this study, we investigated the biodegradation of PS by mesophilic bacterial cultures obtained from various soils in common environments. Two new strains, Pseudomonas lini JNU01 and Acinetobacter johnsonii JNU01, were specifically enriched in non-carbonaceous nutrient medium, with PS as the only source of carbon. Their growth after culturing in basal media increased more than 3-fold in the presence of PS. Fourier transform infrared spectroscopy analysis, used to confirm the formation of hydroxyl groups and potentially additional chemical bond groups, showed an increase in the amount of oxidized PS samples. Moreover, field emission scanning electron microcopy analysis confirmed PS biodegradation by biofilms of the screened microbes. Water contact angle measurement additionally offered insights into the increased hydrophilic characteristics of PS films. Bioinformatics and transcriptional analysis of A. johnsonii JNU01 revealed alkane-1-monooxygenase (AlkB) to be involved in PS biodegradation, which was confirmed by the hydroxylation of PS using recombinant AlkB. These results provide significant insights into the discovery of novel functions of Pseudomonas sp. and Acinetobacter sp., as well as their potential as PS decomposers.

Simultaneous scavenging of persistent pharmaceuticals with different charges by activated carbon fiber from aqueous environments.

The adsorptive removal possibility of persistent pharmaceuticals with different charges by activated carbon fiber (ACF) was examined. The pharmaceuticals tested included carbamazepine (CBZ), propranolol (PRO), and diclofenac (DCF), in neutral, cationic, and anionic forms, respectively, which were frequently detected in sewage. The adsorption characteristics of ACF were identified according to the kinetics, isotherm, pH, and ionic strength experiments. The results revealed that ACF can effectively remove these pharmaceuticals, and the adsorption capacities of CBZ, PRO, and DCF by ACF were 1.27 ± 0.06, 1.07 ± 0.08, and 0.95 ± 0.08 mmol g-1, respectively. Moreover, the adsorption of ACF for CBZ was independent of pH and ionic strength, whereas that of anionic diclofenac decreased at alkaline pHs and high concentrations of NaCl. Using a syringe system packed with ACF mat, the scavenging ability of intermittently generated secondary sewage was evaluated. As a result, the residual concentration of PRO and CBZ could not be even detected after consecutive 10 runs in secondary sewage mixture solution. This indicates ACF has the powerful potential for removing pharmaceutical micropollutants in the actual aqueous solutions. FTIR and XPS analyses showed that hydrophobic and π-π interactions and hydrogen bonding contributed to the adsorption process.

Adsorptive removal of cationic tricyclic antidepressants using cation-exchange resin.

This study aimed to select a high-performance cation-exchange resin (CER) and estimate its uptake of positively ionized tricyclic antidepressants (TCAs), i.e., amitriptyline (AMI), imipramine (IMI), clomipramine (CLO), and desipramine (DES), which are frequently used, and detected in wastewater treatment systems. For the selection of the resin, the one-point check test of AMI in distilled water was examined using several CERs. As a result, the strong-acid polystyrene CER, Dowex 50WX4-200, was selected on the basis of its outstanding uptake of AMI. The maximum adsorption capacities of Dowex 50WX4-200 for removal of the TCAs ranged from 2.53 ± 0.20 mmol/g to 3.76 ± 0.12 mmol/g, which are significantly higher when compared with those of previously reported adsorbents. This is likely because the combination of electrostatic and π-π interactions between the TCAs and Dowex 50WX4-200 may lead to high uptakes of the TCAs. Additionally, the removal efficiency of DES as a representative of the TCAs was tested in actual wastewater system containing activated sludge and miscellaneous cations. Consequently, the removal efficiencies of the DES in distilled water, aerobic wastewater, and filtered wastewater were 95.68%, 77.99%, and 56.66%, respectively. It is interesting to note that the activated sludge could also contribute to adsorption of the DES, leading to increased removability, while the cations present in the wastewater acted as competing ions, decreasing the removal efficiency.

Recovery of gold via adsorption-incineration techniques using banana peel and its derivatives: Selectivity and mechanisms.

In this study, banana peel (BP) and its derivatives after sequential extraction of biochemical components were evaluated for selective recovery of gold. In-depth instrumental characterizations including XPS, FTIR, XRD and HR-TEM were performed to understand the adsorption mechanisms. The biomass after lipid extraction, BP-L, demonstrated very good affinity and selectivity towards gold. In multi-metal systems containing 100 mg/L of Pt(IV), Au(III), Pd(II), Zn(II), Co(II), Ni(II) and Li(I), the selectivity coefficient increased from 978.45 in BP to 2034.70 in BP-L. Moreover, the equilibrium gold uptake was improved and reached 475.48 ± 3.08 mg/g owing to reduction-coupled adsorption mechanisms. The BP-L also showed improved gold nanoparticle formation properties that were pH-dependent. In a strategic adsorption-combined incineration process, metallic gold reaching 99.96% in purity was obtained. The BP and its derivative, BP-L have thus shown potentials for multiple applications in the areas of precious metal recovery and nanoscience.

Evaluation of orange peel-derived activated carbons for treatment of dye-contaminated wastewater tailings.

Dyes are colored compounds which are visible even at trace concentrations. Due to their recalcitrance and esthetic persistence, certain methods are unable to effectively eliminate them. So far, adsorptive treatment using activated carbons (ACs) is one of the most successful methods. In this study, we have employed orange peel (OP) as a cost-effective alternative to the expensive coal- and coir-based precursors to synthesize ACs for cationic methylene blue (MB) and anionic methyl orange (MO) dye adsorption. The pre-carbonized OP was activated via H2SO4, NaOH, KOH, ZnCl2, and H3PO4 to study the effects of activation reagents on dye removal efficiencies and mechanisms. Among several isotherm models employed to fit the adsorption data, the Langmuir and Sips models sufficiently estimated the maximum equilibrium uptakes close to the experimental values of 1012.10 ± 29.13, 339.82 ± 6.98, and 382.15 ± 8.62 mg/g, for ZnCl2-AC (MO), ZnCl2-AC (MB), and KOH-AC (MB), respectively. The adsorption mechanisms were suggested to involve electrostatic binding, pi-pi interactions, hydrogen bonding, and electron donor-acceptor reactions. Consequently, more than 99% removal efficiency was achieved from a laboratory organic wastewater sample bearing ~ 35 mg/L of MB. The results thus suggest that the synthesized ACs from agricultural waste have the tendencies to be applied to real dye wastewater treatment.

Application of general toxic effects of ionic liquids to predict toxicities of ionic liquids to Spodoptera frugiperda 9, Eisenia fetida, Caenorhabditis elegans, and Danio rerio.

Modeling for the toxicity of ionic liquids (ILs) is necessary to fill data gaps for untested chemicals and to understand the relevant mechanisms at the molecular level. In order for many researchers to easily predict toxicity and/or develop some prediction model, simple method(s) based on a single parameter should be proposed. Therefore, previously our group developed a comprehensive toxicity prediction model with unified linear free-energy relationship descriptors to address the single parameter for predicting the toxicities, as follows (Cho et al., 2016b). Log 1/toxicity in the unit of mM= (2.254 Ec - 2.545 Sc + 0.646 Ac - 1.471 Bc + 1.650 Vc + 2.917 J+ - 0.201 Ea + 0.418 Va + 0.131 J-) - 0.709. It is considered that the model can calculate the general toxicological effect of ILs in parenthesis, as it was developed on the basis of numerous toxic effects i.e., 58 toxicity testing methods and about 1600 data points. In order to check the hypothesis, the values calculated by the model were correlated with four different datasets from insect cell line (Spodoptera frugiperda 9), earthworm (Eisenia fetida), nematode (Caenorhabditis elegans), and fish (Danio rerio). The results clearly showed that the calculated values are in good agreement with each dataset. In the case of S. frugiperda 9 cells, the calculated parameters were correlated with log1/LC50 values, measured after 24 h and 48 h incubation, in R2 of 0.67 and 0.88, respectively. The R2 values for the earthworm, nematode, and fish were 0.88, 0.96, and 0.94-0.95, respectively. This study confirmed that the comprehensive model can be simply and accurately used to predict toxicity of ILs.

QSAR modelling for predicting adsorption of neutral, cationic, and anionic pharmaceuticals and other neutral compounds to microalgae Chlorella vulgaris in aquatic environment.

Environmental fate or transport of pharmaceutical waste depends on the adsorptive interactions of pharmaceuticals with various environmental phases e.g. soil, sediment, microalgae, and bacteria etc. Therefore, it is important to understand these adsorptive interactions. As part of the study, we studied the adsorptive interaction of 30 chemicals with microalgae, i.e. Chlorella vulgaris, because it is ubiquitous and its surface area occupies a high proportion in aquatic environments. For this study, isotherms between C. vulgaris and 30 micropollutants in neutral and ionic forms (i.e. 15 cations, 5 anions, and 10 neutrals) were experimentally measured, and their adsorptive affinities were then theoretically predicted based on the concept of the linear free energy relationship. For modeling, the dataset was divided into a training set and a test set, where the training set was used for model development and the test set was performed for model validation. This process was repeated ten times. Finally, we suggested one model which has high predictability in R2 of 0.96 and standard error (SE) of 0.17 log unit for the training set, R2 of 0.818 and SE = 0.217 log unit for the test set, and R2 of 0.926 and SE of 0.169 log unit for the total dataset. Moreover, it was found that dispersive force, H-bond basicity, molecular volume, and electrostatic interaction of anion significantly contribute to the model developed based on the entire dataset. Here, dispersive and hydrophobic interactions (proportional to the magnitude of molecular size) are main attractive forces, while the rest cases are repulsive. In addition, it was found that the adsorption property of the surface of C. vulgaris differs from those of Gram negative bacteria Escherichia coli and dissolved organic matters in an aquatic environment.