LSER model for organic compounds adsorption by single-walled carbon nanotubes: Comparison with multi-walled carbon nanotubes and activated carbon

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.

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.

Universal machine-learning algorithm for predicting adsorption performance of organic molecules based on limited data set: Importance of feature description

Adsorption of organic molecules from aqueous solution offers a simple and effective method for their removal. Recently, there have been several attempts to apply machine learning (ML) for this problem. To this end, polyparameter linear free energy relationships (pp-LFERs) were employed, and poor prediction results were observed outside model applicability domain of pp-LFERs. In this study, we improved the applicability of ML methods by adopting a chemical-structure (CS) based approach. We used the prediction of adsorption of organic molecules on carbon-based adsorbents as an example. Our results show that this approach can fully differentiate the structural differences between any organic molecules, while providing significant information that is relevant to their interaction with the adsorbents. We compared two CS feature descriptors: 3D-coordination and simplified molecular-input line-entry system (SMILES). We then built CS-ML models based on neural networks (NN) and extreme gradient boosting (XGB). They all outperformed pp-LFERs based models and are capable to accurately predict adsorption isotherm of isomers with similar physiochemical properties such as chiral molecules, even though they are trained with achiral molecules and racemates. We found for predicting adsorption isotherm, XGB shows better performance than NN, and 3D-coordinations allow effective differentiation between organic molecules.

Evaluation of the permeability and potential toxicity of polycyclic aromatic hydrocarbons to pulmonary surfactant membrane by the parallel artificial membrane permeability assay model

Polycyclic aromatic hydrocarbons (PAHs) can penetrate and accumulate in the pulmonary surfactant (PS) membranes, leading to abnormalities of biological macromolecules and the destruction of membrane structure and properties. In the present study, the bioavailability, apparent permeability, effective permeability and residual coefficient of 10 PAHs on PS membrane was assessed by the parallel artificial membrane permeability assay (PAMPA). The influence of various forces on permeability is obtained by analyzing the correlation between parameters and physicochemical properties. Research shows that octanol-water partition coefficient (K_ow) cannot directly predict permeability, and permeability has no significant relationship with polarity. Dispersion, induction, coupling/polarization promote permeation, while hydrogen bonded acid and n-n electron pair inhibit permeation. Further surface pressure-area (π-A) isotherms test and Brewster angle microscope observation manifested that there are huge differences in the transmembrane ability and effects on the membrane of PAHs with different structures. This work has considerable significance that will help to evaluate the bioavailability and human health risk of PAHs.

Adsorption modeling of organic compounds (OCs) by carbon nanotubes (CNTs): role of OC and CNT properties on the linear solvation energy relationship

This study evaluated a comprehensive database for the adsorption of polar and nonpolar organic compounds (OCs) by carbon nanotubes (CNTs) and to use the linear solvation energy relationship (LSER) technique for developing predictive adsorption models of OCs by multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs). The results showed that coefficient of determinations (R²) values for all compounds are higher variability in the 200 g/mol molecular weight cutoff (74-99%). When the molecular weight cutoff of all OCs is higher than 200 g/mol, the trend of their R² values is decreased (less than 70%). Among all adsorbate descriptor coefficients, V and B terms are the most significant descriptors (p-values ≤ 0.05) in LSER equations for adsorption of low molecular weight polar and nonpolar OCs by both CNTs. Besides, K_OW normalization of all K_d values did not have significant impact on the regression of the LSER model, indicating that hydrophobic interactions are not sole mechanism for the adsorption of OCs on CNTs. Lastly, SWCNTs exhibited higher polar OCs uptake than MWCNTs, which was attributed to more polar surface of SWCNTs as suggested by its high oxygen content (%10).

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 (γ_s^t) of HPR due to the occupation of active sites by water molecules, resulting in the decrease of adsorption partition coefficients (K). Moreover, the γ_s^t 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 γ_s^t. 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.

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.

Magnetron Sputtering Thin Films as Tool to Detect Triclosan in Infant Formula Powder: Electronic Tongue Approach

Triclosan (TCS) is being detected in breast milk and in infants of puerperal women. The harmful effects caused by this compound on living beings are now critical and thus it is pivotal find new tools to TCS monitoring. In the present study, an electronic tongue (e-tongue) device comprising an array of sputtered thin films based on Multi-Walled Carbon Nanotubes and titanium dioxide was developed to identify TCS concentrations, from 10−15 to 10−5 M, in both water and milk-based solutions. Impedance spectroscopy was used for device signal transducing and data was analyzed by principal component analysis (PCA). The e-tongue revealed to be able to distinguish water from milk-based matrices through the two Principal Components (PC1 and PC2), which represented 67.3% of the total variance. The PC1 values of infant formula milk powder prepared with tap water (MT) or mineral water (MMW) follows a similar exponential decay curve when plotted with the logarithm of concentration. Therefore, considering the TCS concentration range between 10−15 and 10−9 M, the PC1 values are fitted by a straight line and values of −1.9 ± 0.2 and of 7.6 × 10−16 M were calculated for the sensor sensitivity and sensor resolution, respectively. Additionally, a strong correlation (R = 0.96) between MT and MMW PC1 data was found. These results have shown that the proposed device corresponds to a promisor method for the detection of TCS in milk-based solutions.

Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning

Predictive models are useful tools for aqueous adsorption research; existing models such as multilinear regression (MLR), however, can only predict adsorption under specific equilibrium concentrations or for certain adsorption isotherm models. Also, few studies have discussed data processing beyond applying different modeling algorithms to improve the prediction accuracy. In this research, we employed a cosine similarity approach that focused on mining the available data before developing models; this approach can mine the most relevant data concerning the prediction target to build models and was found to considerably improve the prediction accuracy. We then built a machine-learning modeling process based on neural networks (NN), a group-selection data-splitting strategy for grouped adsorption data for adsorbent-adsorbate pairs under different equilibrium concentrations, and polyparameter linear free energy relationships (pp-LFERs) for aqueous adsorption of 165 organic compounds onto 50 biochars, 34 carbon nanotubes, 35 GACs, and 30 polymeric resins. The final NN-LFER models were successfully applied to various equilibrium concentrations regardless of the adsorption isotherm models and showed less prediction deviations than the published models with the root-mean-square errors 0.23-0.31 versus 0.23-0.97 log unit, and the predictions were improved by adding two key descriptors (BET surface area and pore volume) for the adsorbents. Finally, interpreting the NN-LFER models based on the Shapley values suggested that not considering equilibrium concentration and properties of the adsorbents in the existing MLR models is a possible reason for their higher prediction deviations.

Predicting adsorption coefficients of VOCs using polyparameter linear free energy relationship based on the evaluation of dispersive and specific interactions

Predicting adsorption of volatile organic compounds (VOCs) on activated carbons is of major importance to understand activated carbons' adsorption properties and explore their potential applications. In this study, adsorption of 38 VOCs on a commercial granular activated carbon (GAC) was examined using inverse gas chromatography (IGC) at infinite dilution, and the adsorption coefficients (K), dispersive and specific components of adsorption free energy were calculated. We found that the dispersive interaction was well described by adsorbate's molar polarizability (P), and the specific interactions well by dipolarity/polarizability (S), hydrogen-bond acidity (A) and hydrogen-bond basicity (B). Based on the result, a polyparameter linear free energy relationship (PP-LFER) was established: logK = (0.96 ± 0.23) S + (2.23 ± 0.34) A + (0.84 ± 0.25) B + (0.69 ± 0.050) P + (0.13 ± 0.35); (n = 38, R² = 0.859, root mean square error (RMSE) = 0.25), which exhibited a more accurate prediction compared to the classical PP-LFER (E, S, A, B and L as descriptors, R² = 0.765, RMSE = 0.33). Moreover, it overcame the drawbacks of indistinguishable dispersive interaction and unavailable relative contribution of each interaction for classical PP-LFER in explaining adsorption mechanism. As suggested by the developed model, the dispersive interaction was the dominant contribution to the adsorption of VOCs on GAC (42-100%), following by dipole-type interactions (0-30%) and hydrogen bonding (hydrogen-bond acidity 0-32%, hydrogen-bond basicity 0-11%). Additionally, it also accurately predicted the K values of VOCs on other three activated carbons.

Externally predictive quantum-mechanical models for the adsorption of aromatic organic compounds by graphene-oxide nanomaterials

Graphene oxide is most often chosen as an alternative to graphene in the applications of carbon-based nanomaterials where adsorption is the primary process. However, its adsorption properties are poorly understood. The existing reports on the adsorption mechanism of graphene oxide rely on the linear free-energy/solvation-energy relationship (LFER/LSER) models. This computational work explores the role of quantum mechanical descriptors in the adsorption of aromatic organic compounds by graphene-oxide. For this, externally predictive quantitative models based on quantum-mechanical descriptors are developed and compared with the existing LSERs for the prediction of adsorption coefficients of organic compounds at three different adsorbate concentrations. The predictivity of the models is assessed using an external prediction set of compounds not used for developing the models. Notably, the mean polarizability, but originating from the quantum mechanical exchange interactions (between electrons of parallel spin), is found to be the most significant factor in driving the adsorption on graphene oxide. The present work also proposes quantum-mechanical-LSER models based on a combination of quantum-mechanical and LSER descriptors, which are in fact found to be equally predictive as the existing LSERs. The quantum-mechanical models proposed in this work are further utilized for the prediction of adsorption coefficients of aliphatic compounds.

Insights into the roles of the morphological carbon structure and ash in the sorption of aromatic compounds to wood-derived biochars

Currently, it is still lack of systematic and in-depth knowledge regarding the co-effect of carbon-based fractions and ash in the sorption behavior of biochars. Therefore, pristine wood-derived biochars (PBCs) produced at different temperatures and their corresponding de-ashed versions (DBCs) were used to determine the roles of carbon's morphological structure and ash in sorption of aromatic compounds (toluene, m-toluidine, and m-nitrotoluene) to biochars. The results showed that biochars produced at 300-400 °C (mainly uncarbonized organic matter, UCOM) and 900 °C (turbostratic carbon, TC) may have stronger partition effect and pore filling effect with π-π interaction, respectively, and thus have greater sorption coefficients (Lg K_d) than biochars produced at 600 °C (pyrogenic amorphous carbon, PAC), which are probably dominated by surface hydrophobic effect. Meanwhile, TC had a greater Lg K_d than UCOM at low adsorbate concentrations (C_e), but exhibited an opposite trend at high C_e. The Lg K_d values of DBCs are always greater than those of PBCs, indicating ash has an inhibitory effect on sorption of aromatic compounds to biochars. Furthermore, the role of ash in sorption behavior of PBCs would vary with solution pH. At a neutral pH, PBCs have the maximum sorption quantity for aromatic compounds due to the formed cation-π bond between cations of ash and aromatic compounds. However, the acidic pH enhanced the dissolution of cations in ash and the basic pH enhanced the hydroxylation of cations in ash. Therefore, both acidic and basic pH weakened the cation-π bond between ash and aromatic compounds and decreased the sorption of aromatic compounds on PBCs. The results suggest that de-ashed biochars with more UCOM or TC are effective sorbents for sequestration of aromatic compounds, and provide a well-designed method for improving the sorption efficiency of biochars.

Alkali promoted the adsorption of toluene by adjusting the surface properties of lignin-derived carbon fibers

The lignin-based carbon fibers were prepared by electrospinning followed by stabilization, carbonization, and activation (i.e., steam activation, one-step KOH activation, and metal activation). The effect of carbonization temperature on prepared carbon fibers (CFs) was investigated. As a result, 800 °C is the most suitable carbonization temperature because the prepared carbon fibers possess greater specific surface area and pore volume. With the help of various characterization methods, the structural characteristics of the activated carbon fibers (ACFs) prepared by the three activation methods and the adsorption performance of toluene were compared. It was observed that the activated carbon fibers prepared by KOH one-step activation method (ACFK) exhibited higher specific surface area (1147.16 m²/g) and greater toluene adsorption (463 mg/g). Particularly, abundant microporous structures and surface functional groups play a vital role in the adsorption process. Further, the adsorption performance of toluene onto ACFK was further investigated in a gas-phase dynamic adsorption system and the results showed that ACFK has great potential application in adsorption of volatile organic compounds.

Micron-Size White Bamboo Fibril-Based Silane Cellulose Aerogel: Fabrication and Oil Absorbent Characteristics

Micron-size white bamboo fibrils were fabricated from white bamboo and used as a source for the production of highly porous and very lightweight cellulose aerogels for use as a potential oil absorbent. The aerogels were fabricated through gelation from an aqueous alkali hydroxide/urea solution, followed by a conventional freeze-drying process. The morphology and physical properties of the aerogels were characterized by field emission scanning electron microscopy and Brunauer–Emmett–Teller surface area analysis, respectively. Successful silanization of the cellulose aerogel was confirmed by energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, and water contact angle measurements. The fabricated silane cellulose aerogel exhibited excellent absorption performance for various oil and organic solvents with typical weight gains ranging from 400% to 1200% of their own dry weight, making them promising versatile absorbents for a range of applications, including water purification.

Predictive models for adsorption of organic compounds by Graphene nanosheets: comparison with carbon nanotubes

The Linear Solvation Energy Relationships (LSER) technique was applied in the present study for predicting models of organic compounds (OCs) adsorption by Graphene and Graphene oxide (GO), and the results were compared with those of multi-walled carbon nanotube (MWCNT) and single-walled carbon nanotube (SWCNT). Adsorption database of 38 OCs (28 aromatic and 10 aliphatic) for Graphene and 69 OCs (59 aromatic and 10 aliphatic) for GO were collected from the literature and our laboratory. The r² of the LSER models on the adsorption of aromatic OCs by Graphene and GO at three different equilibrium concentrations gradually increased up to OC molecular weight of 400 g/mol, after which a declining trend was observed for GO, while there was no visible change for Graphene. Among descriptors for all LSER models, V (molecular volume) and B (hydrogen bond accepting) for Graphene nanosheets (GNS) and carbon nanotubes (CNT) were the most significant descriptors (p values ≤ 0.05). B term had high value and was negatively correlated with adsorption of all OCs by Graphene (-1.24 to -9.45), GO (-0.55 to -9.31), SWCNT (-0.10 to -5.38) and MWCNT (-1.24 to -1.85). LSER successfully trained models for adsorption of OCs by GNS, and model coefficients were dependent on adsorbent type and OC properties.

Quantum-mechanical LSERs for the concentration-dependent adsorption of aromatic organic compounds by activated carbon: Applications and comparison with carbon nanotubes

Carbon nanotubes (CNTs) have taken precedence over activated carbon in various applications where adsorption is the primary process. The adsorption of chemical compounds by CNTs and activated carbon is most often predicted through linear free energy/solvation energy relationships (LFERs/LSERs). This work proposes quantum-mechanical LSER models based on a combination of quantum-mechanical descriptors and solvatochromic descriptors of LSERs for predicting the adsorption of aromatic organic compounds by activated carbon at varying adsorbate concentrations. The models are validated using state-of-the-art procedures employing an external prediction set of compounds. This work reveals the hydrogen bond donating and accepting ability of compounds to be the most influencing - but a negative - factor in the adsorption process of activated carbon. The quantum-mechanical LSERs proposed in this work are analysed and found to be equally reliable as the existing LSERs. These were further used to predict the adsorption of nucleobases, steroid hormones, agrochemicals, endocrine disruptors and pharmaceutical drugs. Notably, agrochemicals and endocrine disruptors are predicted to be adsorbed more strongly by activated carbon when compared with their adsorption by CNTs. However, quantum-mechanical LSERs predict the adsorption strength of biomolecules on activated carbon to be similar to that on the CNTs, which can be used to assess the risk associated with using carbon materials.

Modeling adsorption of organic pollutants onto single-walled carbon nanotubes with theoretical molecular descriptors using MLR and SVM algorithms

Prediction of adsorption equilibrium coefficients (K) of organic compounds onto single walled carbon nanotubes (SWNTs) from in silico molecular descriptors is of importance for probing potential applications of SWNTs as well as for evaluating environmental behavior and ecological risks of organic pollutants and SWNTs. In this study, two models for predicting logK were developed with multiple linear regression (MLR) and support vector machine (SVM) algorithms. The two models have satisfactory goodness-of-fit, robustness and predictive ability, and the SVM model performs slightly better than the MLR model. The two models are based on the up-to-date experimental dataset consisting of 61 logK values, and the applicability domains cover diverse organic compounds with functional groups > CC<, CC, C₆H₅, >CO, COOH, C(O)O, OH, O, F, Cl, Br, NH₂, NH, >N, >NN<, NO₂, >NC(O)NH₂, >NC(O)NH, S and S(O)(O). The adsorption of organic compounds toward SWNTs is mainly determined by van der Waals forces and hydrophobic interactions. Since only in silico molecular descriptors were employed for the modeling, the developed models are beneficial for prediction purposes.

Experimental and QSAR studies on adsorptive interaction of anionic nonsteroidal anti-inflammatory drugs with activated charcoal

Adsorptive interactions, namely adsorption capacity (q_m) and affinity (b), between nonsteroidal anti-inflammatory drugs (NSAIDs) in anionic forms and commercial activated charcoal (AC), were estimated by isotherm experiment in a batch, and the properties were modeled based on the concept of quantitative structure-activity relationship (QSAR). Experimental results showed that AC had a high q_m (0.38-0.67 mmol g^-1) and b (14.03-930.8 L mmol^-1) for the selected NSAIDs. In QSAR modeling, linear free energy relationship (LFER) descriptors of excess molar refraction (E), dipolarity/polarizability (S), and Coulombic interactions of anions (J^-) were highly related to log q_m, and the combination of the three terms could predict log q_m in R² of 0.97 and SE of 0.015 log unit. In the case of b, only single B term showed a good correlation with log b in R² of 0.81. Additionally, the combination of hydrogen-bonding acceptors (HBAs) and molar volume (MV), which are easily calculable parameters, could also derive good predictability in R² = 0.81 and SE = 0.26 log unit. Afterwards, validation of the QSAR models based on the leave-one-out cross-validation (Q²_LOO) method showed that the models were acceptable.

Sorption of chlorinated hydrocarbons to biochars in aqueous environment: Effects of the amorphous carbon structure of biochars and the molecular properties of adsorbates

Currently, the role of amorphous carbon structure (ACS) in sorption of chlorinated hydrocarbons (CHs) to biochars remains little known. Therefore, three CHs (1,1,2,2-tetrachloroethane, 1,3,5-trichlorobenzene and γ-hexachlorocyclohexane) with different molecular properties were selected as model adsorbates to investigate the effect of ACS on sorption of CHs to biochars produced at seven different pyrolysis temperatures (300-900 °C). There were two main mechanisms for ACS controlling the sorption of CHs. First, the polar sites on ACS are hydrophilic, CHs with greater polarity could strongly compete with the water molecule for the hydrophilic sites. Second, ACS of low temperature (300-400 °C) produced biochars possessing the natural organic matter (NOM)-like structure occupied some hydrophobic sites on condensed graphitic structure (CGS) of biochars. CHs with great hydrophobicity possibly seized the hydrophobic sorption sites on CGS from the NOM-like structure. Therefore, ACS of biochar was more benefit for sorption of strong polar CHs (1,1,2,2-tetrachloroethane: π^∗ = 0.95; LogK_ow = 2.39) or strong hydrophobic CHs (1,3,5-trichlorobenzene: π^∗ = 0.70; LogK_ow = 4.19) than CHs (γ-hexachlorocyclohexane: π^∗ = 0.68; LogK_ow = 3.72) with relatively low polarity and hydrophobicity. The result reflects that the interaction between NOM and natural black carbon/biochars in soil and water environment possibly plays the similar role in controlling the environmental behavior of various polar or hydrophobic organic pollutants. Moreover, with increasing concentration of adsorbate (C_e), the first mechanism enhanced, while the second mechanism weakened. This study gives a deep insight into the roles of ACS of biochars in controlling the fate and availability of CHs with different molecular properties in environment.

Concentration dependent adsorption of aromatic organic compounds by SWCNTs: Quantum-mechanical descriptors for nano-toxicological studies of biomolecules and agrochemicals

For evaluating the environmental risk associated using carbon nanotubes (CNTs), a successful prediction is desired for the adsorption of organic compounds (OCs) by CNTs at different adsorbate concentrations. This is most often achieved through poly-parameter linear solvation energy relationships (LSERs) based on solvatochromic descriptors. This study examines the real predictivity of the existing LSERs for predicting the adsorption of OCs by single-walled CNTs (SWCNTs) while comparing it with that of the models developed in the present work using quantum-mechanical descriptors. The real predictivity of the quantum-mechanical models and existing LSERs is compared using state-of-the-art statistical procedures employing an external prediction set of compounds not used in the model development. The quantum-mechanically computed mean polarizability, but originating from the interactions between electrons of parallel spin, is found to play an essential role in the adsorption of OCs by SWCNTs. Besides the solvatochromic descriptors (McGowan volume and molar excess refractivity), the instantaneous inter-electronic interactions, captured through electron-correlation based quantum-mechanical descriptors, are found to significantly affect the adsorption at varying adsorbate concentration. The models developed using a combination of quantum-mechanical and solvatochromic descriptors are found to be quite reliable. The models proposed were further employed to predict the adsorption of agrochemicals such as insecticides, pesticides, herbicides, as well as adsorption of endocrine disruptors and biomolecules such as nucleobases and steroid hormones. These are predicted to be strongly adsorbed by SWCNTs with Progesterone and Guanine exhibiting maximal interaction with the SWCNTs among biomolecules. The quantum-mechanical descriptors proposed in this work can be used for the risk assessment of SWCNTs in systems where adsorption is the primary process.

Unveiling Adsorption Mechanisms of Organic Pollutants onto Carbon Nanomaterials by Density Functional Theory Computations and Linear Free Energy Relationship Modeling

Predicting adsorption of organic pollutants onto carbon nanomaterials (CNMs) and understanding the adsorption mechanisms are of great importance to assess the environmental behavior and ecological risks of organic pollutants and CNMs. By means of density functional theory (DFT) computations, we investigated the adsorption of 38 organic molecules (aliphatic hydrocarbons, benzene and its derivatives, and polycyclic aromatic hydrocarbons) onto pristine graphene in both gaseous and aqueous phases. Polyparameter linear free energy relationships (pp-LFERs) were developed, which can be employed to predict adsorption energies of aliphatic and aromatic hydrocarbons on graphene. Based on the pp-LFERs, contributions of different interactions to the overall adsorption were estimated. As suggested by the pp-LFERs, the gaseous adsorption energies are mainly governed by dispersion and electrostatic interactions, while the aqueous adsorption energies are mainly determined by dispersion and hydrophobic interactions. It was also revealed that curvature of single-walled carbon nanotubes (SWNTs) exhibits more significant effects than the electronic properties (metallic or semiconducting) on gaseous adsorption energies, and graphene has stronger adsorption abilities than SWNTs. The developed models may pave a promising way for predicting adsorption of environmental chemicals onto CNMs with in silico techniques.

Linear solvation energy relationship to predict the adsorption of aromatic contaminants on graphene oxide

In this study, adsorption capability of aromatic contaminants on graphene oxide (GO) was predicted using linear solvation energy relationship (LSER) model for the first time. Adsorption data of 44 aromatic compounds collected from literature and our experimental results were used to establish LSER models with multiple linear regression. High value of R² (0.919), strong robustness (Q_Loo² = 0.862), and desirable predictability (Q_ext² = 0.834) demonstrated the model worked well for predicting the adsorption of small aromatic contaminants (descriptor V＜3.099) on GO. The adsorption process was governed by the ability of cavity formation and dispersion forces captured by vV and hydrogen-bond interactions captured by bB. Effect of equilibrium concentrations and properties of GO on the model were explored; and the results indicated that upon an increase of equilibrium concentration, the values of regression coefficients (a, b, v, e, and s) changed at different levels. The oxygen content normalization of logK_0.001 decreased the value of b dramatically; however, no obvious changes of the model deduced by the surface area normalization of logK_0.001 were witnessed. Overall, our study showed that LSER model provided a potential approach for exploring the adsorption of organic compounds on GO.

Different effects of surface heterogeneous atoms of porous and non-porous carbonaceous materials on adsorption of 1,1,2,2-tetrachloroethane in aqueous environment

The surface heterogeneous atoms of carbonaceous materials (CMs) play an important role in adsorption of organic pollutants. However, little is known about the surface heterogeneous atoms of CMs might generate different effect on adsorption of hydrophobic organic compounds by porous carbonaceous materials - activated carbons (ACs) and non-porous carbonaceous materials (NPCMs). In this study, we observed that the surface oxygen and nitrogen atoms could decrease the adsorption affinity of both ACs and NPCMs for 1,1,2,2-tetrachloroethane (TeCA), but the degree of decreasing effects were very different. The increasing content of surface oxygen and nitrogen ([O + N]) caused a sharper decrease in adsorption affinity of ACs (slope of lg (k_d/SA) vs [O + N]: -0.098∼-0.16) than that of NPCMs (slope of lg (k_d/SA) vs [O + N]: -0.025∼-0.059) for TeCA. It was due to the water cluster formed by the surface hydrophilic atoms that could block the micropores and generate massive invalid adsorption sites in the micropores of ACs, while the water cluster only occupied the surface adsorption sites of NPCMs. Furthermore, with the increasing concentration of dissolved TeCA, the effect of surface area on adsorption affinity of NPCMs for TeCA kept constant while the effect of [O + N] decreased due to the competitive adsorption between water molecule and TeCA on the surface of NPCMs, meanwhile, both the effects of micropore volume and [O + N] on adsorption affinity of ACs for TeCA were decreased due to the mechanism of micropore volume filling. These findings are valuable for providing a deep insight into the adsorption mechanisms of CMs for TeCA.

Quantum-mechanical parameters for the risk assessment of multi-walled carbon-nanotubes: A study using adsorption of probe compounds and its application to biomolecules

This work forwards new insights into the risk-assessment of multi-walled carbon-nanotubes (MWCNTs) while analysing the role of quantum-mechanical interactions between the electrons in the adsorption of probe compounds and biomolecules by MWCNTs. For this, the quantitative models are developed using quantum-chemical descriptors and their electron-correlation contribution. The major quantum-chemical factors contributing to the adsorption are found to be mean polarizability, electron-correlation energy, and electron-correlation contribution to the absolute electronegativity and LUMO energy. The proposed models, based on only three quantum-chemical factors, are found to be even more robust and predictive than the previously known five or four factors based linear free-energy and solvation-energy relationships. The proposed models are employed to predict the adsorption of biomolecules including steroid hormones and DNA bases. The steroid hormones are predicted to be strongly adsorbed by the MWCNTs, with the order: hydrocortisone > aldosterone > progesterone > ethinyl-oestradiol > testosterone > oestradiol, whereas the DNA bases are found to be relatively less adsorbed but follow the order as: guanine > adenine > thymine > cytosine > uracil. Besides these, the developed electron-correlation based models predict several insecticides, pesticides, herbicides, fungicides, plasticizers and antimicrobial agents in cosmetics, to be strongly adsorbed by the carbon-nanotubes. The present study proposes that the instantaneous inter-electronic interactions may be quite significant in various physico-chemical processes involving MWCNTs, and can be used as a reliable predictor for their risk assessment.

Linear solvation energy relationships (LSER) for adsorption of organic compounds by carbon nanotubes

The objective of this paper was to create a comprehensive database for the adsorption of organic compounds by carbon nanotubes (CNTs) and to use the Linear Solvation Energy Relationship (LSER) technique for developing predictive adsorption models of organic compounds (OCs) by multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs). Adsorption data for 123 OCs by MWCNTs and 48 OCs by SWCNTs were compiled from the literature, including some experimental results obtained in our laboratory. The roles of selected OCs properties and CNT types were examined with LSER models. The results showed that the r(2) values of the LSER models displayed small variability for aromatic compounds smaller than 220 g/mol, after which a decreasing trend was observed. The data available for aliphatics was mainly for molecular weights smaller than 250 g/mol, which showed a similar trend to that of aromatics. The r(2) values for the LSER model on the adsorption of aromatic and aliphatic OCs by SWCNTs and MWCNTs were relatively similar indicating the linearity of LSER models did not depend on the CNT types. Among all LSER model descriptors, V term (molecular volume) for aromatic OCs and B term (basicity) for aliphatic OCs were the most predominant descriptors on both type of CNTs. The presence of R term (excess molar refractivity) in LSER model equations resulted in decreases for both V and P (polarizability) parameters without affecting the r(2) values. Overall, the results demonstrate that successful predictive models can be developed for the adsorption of OCs by MWCNTs and SWCNTs with LSER techniques.

Linear solvation energy relationship for the adsorption of synthetic organic compounds on single-walled carbon nanotubes in water

The linear solvation energy relationship (LSER) was applied to predict the adsorption coefficient (K) of synthetic organic compounds (SOCs) on single-walled carbon nanotubes (SWCNTs). A total of 40 log K values were used to develop and validate the LSER model. The adsorption data for 34 SOCs were collected from 13 published articles and the other six were obtained in our experiment. The optimal model composed of four descriptors was developed by a stepwise multiple linear regression (MLR) method. The adjusted r(2) (r(2)adj) and root mean square error (RMSE) were 0.84 and 0.49, respectively, indicating good fitness. The leave-one-out cross-validation Q(2) ([Formula: see text]) was 0.79, suggesting the robustness of the model was satisfactory. The external Q(2) ([Formula: see text]) and RMSE (RMSEext) were 0.72 and 0.50, respectively, showing the model's strong predictive ability. Hydrogen bond donating interaction (bB) and cavity formation and dispersion interactions (vV) stood out as the two most influential factors controlling the adsorption of SOCs onto SWCNTs. The equilibrium concentration would affect the fitness and predictive ability of the model, while the coefficients varied slightly.