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

Enhancing the selective ciprofloxacin adsorption in urine matrices through the metal-doping of carbon sorbents

Pharmaceuticals excreted after administration can pollute water sources given their ineffective removal in conventional wastewater treatment plant. Among the techniques used during tertiary wastewater treatment, adsorption is an effective and cost-efficient method for removing antibiotics. This study aimed to investigate the adsorption of ciprofloxacin (CIP) on metal-doped granular activated carbon (GAC) and evaluate the impact of urine on CIP adsorption for pristine, pre-oxidized, and metal-doped GAC. The results showed that the uptake of CIP by iron (Fe)-doped GAC was higher than Ag-doped, pre-oxidized, and pristine GAC in single-solute isotherms (DI water). This higher uptake was attributed to the presence of Fe content (1.2%) on the carbon surface, which can strongly interact with zwitterionic CIP at a neutral pH. However, when synthetic human urine was introduced, the adsorption of CIP was negatively affected due to pore blockage and competition for available sorption sites on the GAC. Among the four types of GACs tested, the lowest reduction in CIP uptake in the urine solution was observed for Fe-doped GAC followed (%17) by pre-oxidized (64%), Ag-doped (%69), and pristine F400 (76%) carbon. These results suggested that the complexation between CIP and Fe-doped GAC in urine was stronger due to its higher functionalization compared to Ag-doped, pre-oxidized, and pristine GAC. As the equilibrium concentration of CIP increased, the competition between CIP and urine decreased on the surface of Fe-doped carbon, owing to the limited competition from urine for the available active sorption sites.

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.

Selectivity evaluation of extraction systems

Extraction is the most common sample preparation technique prior to chromatographic analysis for samples which are too complex, too dilute, or contain matrix components incompatible with the further use of the separation system or interfere in the detection step. The most important extraction techniques are biphasic systems involving the transfer of target compounds from the sample to a different phase ideally accompanied by no more than a tolerable burden of co-extracted matrix compounds. The solvation parameter model affords a general framework to characterize biphasic extraction systems in terms of their relative capability for solute-phase intermolecular interactions (dispersion, dipole-type, hydrogen bonding) and within phase solvent-solvent interactions for cavity formation (cohesion). The approach is general and allows the comparison of liquid and solid extraction phases using the same terms and is used to explain the features important for the selective enrichment of target compounds by a specific extraction phase using solvent extraction, liquid-liquid extraction, and solid-phase extraction for samples in a gas, liquid, or solid phase. Hierarchical cluster analysis with the system constants of the solvation parameter model as variables facilitates the selection of solvents for extraction, the identification of liquid-liquid distribution systems with non-redundant selectivity, and evaluation of different approaches using liquids and solids for the isolation of target compounds from different matrices.

Biochar for the Removal of Emerging Pollutants from Aquatic Systems: A Review

Water contaminated with emerging pollutants has become a serious environmental issue globally. Biochar is a porous and carbon-rich material produced from biomass pyrolysis and has the potential to be used as an integrated adsorptive material. Many studies have shown that biochar is capable to adsorb emerging pollutants from aquatic systems and could be used to solve the water pollution problem. Here, we provided a dual perspective on removing emerging pollutants from aquatic systems using biochar and analyzed the emerging pollutant removal efficiency from the aspects of biochar types, pollutant types and coexistence with heavy metals, as well as the associated mechanisms. The potential risks and future research directions of biochar utilization are also presented. This review aims to assist researchers interested in using biochar for emerging pollutants remediation in aquatic systems and facilitate research on emerging pollutants removal, thereby reducing their environmental risk.

Selective adsorption of antibiotics on manganese oxide-loaded biochar and mechanism based on quantitative structure-property relationship model

In this study, MnCl₂-impregnated biomass was oxygen-limited pyrolyzed to produce manganese oxide-loaded biochar (MBC), its adsorption behaviors and influencing factors on tetracycline (TTC), norfloxacin (NOR), and sulfamethoxazole (SMX) were systematically investigated. Three antibiotics exhibited enhanced adsorption behavior on MBC, with maximum adsorption capacity as accurately described by Sips isotherm: TTC (534 mg/g) > NOR (67 mg/g) > SMX (28 mg/g). Hydrogen bonding, n/π-π interactions, electrostatic interaction, surface coordination, and hydrophobic interaction are the major mechanisms for the improved adsorption. Manganese oxide particles on MBC promoted surface coordination and hydrogen bonding. Antibiotic molecules with more hydroxyl oxygen-containing functional groups are more susceptible to migrate to biochar surfaces and to be adhered. Moreover, the quantitative structure-property relationship (QSPR) model was constructed and revealed that hydrogen bonding and π-π interactions were crucial for tetracycline antibiotics selective adsorption. Hence, MBC was a prospective adsorbent with promising applications for antibiotic removal in sewage processing.

Biochar application for remediation of organic toxic pollutants in contaminated soils; An update

Bioremediation of organic contaminants has become a major environmental concern in the last few years, due to its bio-resistance and potential to accumulate in the environment. The use of diverse technologies, involving chemical and physical principles, and passive uptake utilizing sorption using ecofriendly substrates have drawn a lot of interest. Biochar has got attention mainly due to its simplicity of manufacturing, treatment, and disposal, as it is a less expensive and more efficient material, and has a lot of potential for the remediation of organic contaminants. This review highlighted the adverse impact of persistent organic pollutants on the environment and soil biota. The utilization of biochar to remediate soil and contaminated compounds i.e., pesticides, polycyclic aromatic hydrocarbons, antibiotics, and organic dyes has also been discussed. The soil application of biochar has a significant impact on the biodegradation, leaching, and sorption/desorption of organic contaminants. The sorption/desorption of organic contaminants is influenced by chemical composition and structure, porosity, surface area, pH, and elemental ratios, and surface functional groups of biochar. All the above biochar characteristics depend on the type of feedstock and pyrolysis conditions. However, the concentration and nature of organic pollutants significantly alters the sorption capability of biochar. Therefore, the physicochemical properties of biochar and soils/wastewater, and the nature of organic contaminants, should be evaluated before biochar application to soil and wastewater. Future initiatives, however, are needed to develop biochars with better adsorption capacity, and long-term sustainability for use in the xenobiotic/organic contaminant remediation strategy.

C-PCM study on the electronic and optical properties of Fe(CO)4B12N12 complexes

We explored solvent effect on the stability, dipole moment, polarizability and first hyperpolarizability of Fe(CO)4B12N12 complexes at MPW1PW91/6-311G(d,p) level of theory. These complexes were considered in the low spin states. The self-consistent reaction field theory (SCRF) based on conductor-like polarizable continuum model (PCM) was employed to illustration of the solvent influences. The relations between the parameters with solvent polarity functions (McRae and Suppan functions) were given. Also, relations of the wavenumbers values of the stretching of carbonyl ligands with the Kirkwood–Bauer–Magat equation (KBM) were provided.

Decipher the molecular descriptors and mechanisms controlling sulfonamide adsorption onto mesoporous carbon: Density functional theory calculation and partial least-squares path modeling

Mesoporous carbons (MCs) exhibit excellent removal efficiencies to various organic chemicals. However, how the properties of chemicals influence the adsorption mechanisms and further determine their adsorption onto MCs are poorly understood. We investigated the adsorption of 22 sulfonamides (SAs) onto four MCs, and further uncovered the major molecular descriptors and adsorption mechanisms influencing the adsorption by density functional theory (DFT) and partial least-squares path modeling (PLS-PM). The results revealed that the excess molar refraction (E), McGowan's molar volume (V), energy of the highest occupied molecular orbital (E_HOMO), hardness (H), and most positive net charge on carbon atom (Q_c⁺) were identified as the indirect factors affecting the distribution coefficient (logK_D), by influencing the BE(π-π), BE(H), and logK_ow. BE(π-π) and logK_ow displayed significant direct impacts on logK_D (p < 0.05), while BE(H) showed insignificant direct influences on logK_D (p > 0.05). The PLS-PM results indicate the main driving forces for SAs adsorption including π-π interactions, hydrophobic effects, and hydrogen bonding. This study provides a new perspective on revealing the adsorption mechanisms, and the identified factors can be used to develop the quantitative model to further predict the adsorption of SAs onto MCs.

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 (E_ad) and adsorption equilibrium constants (K) are lacking. Herein, molecular dynamic (MD) and density functional theory (DFT) was adopted to calculate E_ad and K values. The calculated E_ad 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 E_ad 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 E_ad 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.

Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor

Polydimethylsiloxane-air partition coefficient (K_PDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict K_PDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the R_adj², Q_BOOT² and Q_ext² are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental K_PDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.

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).

Predictive modeling of haloacetonitriles under uniform formation conditions

The objective of this study was to develop models to predict the formation of HANs under uniform formation conditions (UFC) in chlorinated, choraminated, and perchlorinated/chloraminated waters of different origins. Model equations were developed using multiple linear regression analysis to predict the formation of dichloroacetonitrile (DCAN), HAN4 (trichloroacetonitrile [TCAN], DCAN, bromochloroacetonitrile [BCAN], and dibromoacetonitrile [DBAN]) and HAN6 (HAN4 plus monochloroacetonitrile, monobromoacetonitrile). The independent variables covered a wide range of values, and included ultraviolet absorbance,(UV₂₅₄) dissolved organic carbon (DOC), dissolved organic nitrogen (DON), specific UV absorbance at 254 (SUVA₂₅₄), bromide (Br^-), pH, oxidant dose, contact time, and temperature. The regression coefficients (r²) of HAN4 and HAN6 models for natural organic matter (NOM), algal organic matter (AOM), and effluent organic matter (EfOM)  impacted waters were within the range of 60-88%, while the r² values of HAN4 and DCAN models for both groundwater and distribution systems were lower, in the range of 41-66%. The r² values for the DCAN model were mostly higher in the individual types as compared to the cumulative analysis of all source water data together. This was attributed to differences in HAN precursor characteristics. For chlorination, among all variables, pH was found to be the most significant descriptor in the model equations describing the formation of DCAN, HAN4, and HAN6, and it was negatively correlated with HAN formation in the distribution system, groundwater, AOM, and NOM samples, while it showed an inverse relationship with HAN6 formation in EfOM impacted waters. During chloramination, pH was the most influential model descriptor for DCAN formation in the NOM. Prechlorination dose was the most predominant parameter for prechlorination/chloramination, and it was positively correlated with HAN4 formation in AOM impacted waters.

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.

Fungi and biochar applications in bioremediation of organic micropollutants from aquatic media

The conventional wastewater treatment system such as bacteria, is not able to remove recalcitrant micropollutants effectively. While, fungi have shown high capacity in degradation of recalcitrant compounds. Biochar, on the other hand, has gained attention in water and wastewater treatment as a low cost and sustainable adsorbent. This paper aims to review the recent applications of three major fungal divisions including Basidiomycota, Ascomycota, and Mucoromycotina, in organic micropollutants removal from wastewater. Moreover, it presents an insight into fungal bioreactors, fungal biofilm and immobilization system. Biochar adsorption capacities for organic micropollutants removal under different operating conditions are summarized. Finally, few recommendations for further research are established in the context of the combination of fungal biofilm with the technologies relying on the adsorption by porous carbonaceous materials.

Adsorption of neutral organic compounds on polar and nonpolar microplastics: Prediction and insight into mechanisms based on pp-LFERs

Adsorption of 18 neutral organic compounds (OCs) on polar (polybutylene succinate (PBS) and polycaprolactone (PCL)) and nonpolar (low-density polyethylene (LDPE) and polystyrene (PS)) microplastics (MPs) were investigated. The adsorption coefficients (K_d) varied with ranges of 130-42,002, 124-27,768, 6.40-10,713, and 1.52-10,332 L kg^-1 for adsorption on PCL, PBS, LDPE, and PS MPs, respectively. The polar MPs showed greater adsorption capacities than nonpolar MPs. Non-specific interaction, i.e. hydrophobic partition played a crucial role in the adsorption of OCs on all MPs, while polar interactions also contributed significantly to the greater adsorption on polar MPs. Poly-parameter linear free energy relationships (pp-LFERs) with multiple linear regression (MLR) and feedforward network (FN) were then employed to model the adsorption of OCs on MPs so as to obtain deep insights into adsorption mechanisms. The MLR models achieved R_adj² of 0.90-0.97 and root mean square error (RMSE) of 0.13-0.38 log units, while the FN models achieved R_adj² of 0.85-0.90 and RMSE of 0.21-0.60 log units. The MLR models are more accurate under selected equilibrium concentrations while FN models are capable of making predictions under varying equilibrium concentrations. Lastly, both MLR and FN models showed good prediction on literature adsorption data on nonpolar MPs.

Competitive Adsorption of Polycyclic Aromatic Hydrocarbons to Carbon Nanotubes and the Impact on Bioavailability to Fathead Minnow (Pimephales promelas)

Recent studies investigating the influence of carbon nanotubes (CNTs) on the bioavailability of organic contaminants have mostly focused on single-solute systems; however, a more likely scenario in the natural environment is a multisolute system where chemical interactions at the surface of the CNT may alter the bioavailability of these chemicals. In the present study bisolute adsorption isotherms of pairs of chemically similar polycyclic aromatic hydrocarbons (PAHs) by multiwalled carbon nanotubes (MWCNTs) were established, in conjunction with quantifying the bioavailability of the 2 competing MWCNT-adsorbed PAHs to Pimephales promelas using bile analysis by high-performance liquid chromatography with fluorescence detection. The results showed that whereas adsorption and bioavailability of chemically similar PAHs (anthracene and phenanthrene, and fluoranthene and pyrene) were the same in a single-solute system, in bisolute systems, PAHs that could better align or flex with the MWCNT surface due to morphological characteristics would outcompete the more rigid or planar PAHs. The bioavailability of individual PAHs in bisolute solutions increased by as much as 50% compared with single-solute solutions. However, the relationship between adsorption (i.e., K_d ) and concentration of PAH in the fish bile was similar in single and bisolute systems. This finding indicates that competitive interactions at the surface of MWCNTs influence bioavailability by way of altering adsorption affinity in a moderately predictable manner. Environ Toxicol Chem 2020;39:1702-1711. © 2020 SETAC.

Adsorption of low-concentration VOCs on various adsorbents: Correlating partition coefficient with surface energy of adsorbent

Accurately evaluating the adsorption properties of various adsorbents by some parameter is of great significance to select an appropriate adsorbent and remove volatile organic compounds (VOCs) efficiently. In this study, we successfully found a new parameter as a common standard in selecting adsorbents. Six classical adsorbents containing three carbon materials and three porous polymeric resins were used, and their surface energy (γ_s^t) and corresponding gas-solid partition coefficients (K) of eleven VOCs were measured by inverse gas chromatography (IGC) at three different column temperatures of 343 K(or 353 K), 373 K and 403 K. Then, these values at 303 K were calculated according to the linear relationship between lnK and 1/T. It was found that surface energy was significantly correlated with K values for a specific VOC, and could be used as a common standard to well evaluate the adsorption properties of various adsorbents. Furthermore, we employed it to develop a model for predicting the adsorption properties of low-concentration VOCs on various adsorbents at 303 K. The developed model exhibited an excellent predictive ability by external validation. Moreover, the model showed wide applicability and predicted the lnK values of VOCs at 373 K and 403 K in R² of 0.910 and 0.889.

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

Predicting plant cuticle-water partition coefficients (K_cw) and understanding the partition mechanisms are crucial to assess environmental fate and risk of organic pollutants. Up to now, experimental K_cw values are determined for only hundreds of compounds because of high experimental cost. For this reason, computational models, which can predict K_cw values based on chemical structures, are promising approaches to evaluate new compounds. In this study, a large dataset consisting of 279 logK_cw values for 125 unique compounds were collected and curated. A poly-parameter linear free energy relationship (pp-LFER) model was developed with stepwise multiple linear regression based on this dataset. The resulted pp-LFER model has good predictability and robustness as indicated by determination coefficient (R²_adj,tra) of 0.93, bootstrapping coefficient (Q²_BOOT) of 0.92, external validation coefficient (Q²_ext) of 0.94 and root mean square error of 0.52 log units. Contribution analysis of different interactions indicated that dispersion and hydrophobic interactions have the highest positive contribution (56%) to increase the partition of pollutants onto plant cuticles. In addition, for organic pollutions containing benzene ring (13-31%), double bond (9-17%) or nitrogen-containing heterocycles (9-17%), π/n-electron pairs interactions exhibit obvious positive contributions to logK_cw. In conclusion, the proposed pp-LFER model is beneficial for predicting logK_cw of potential organic pollutants directly from their molecular structures.

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.

Adsorption kinetics and aggregation for three classes of carbonaceous adsorbents in the presence of natural organic matter

In this study, adsorption kinetics of phenanthrene (PNT) and trichloroethylene (TCE) by a graphene nanosheet (GNS), a graphene oxide nanosheet (GO), a single-walled carbon nanotube (SWCNT), a multi-walled carbon nanotube (MWCNT), and two coal based activated carbons (ACs) (F400 and HD3000) were examined in distilled and deionized water (DDW) and under natural organic matter (NOM) preloading conditions. The results showed the times needed for the adsorption of PNT and TCE to reach apparent equilibrium (i.e., ≤3% change per day) followed the order of GO ≥ MWCNT > GNS > SWCNT ∼ HD3000∼F400 and SWCNT > GNS ∼ HD3000 > F400 ∼ MWCNT > GO, respectively. The pseudo second order model successfully represented kinetics data for three classes of carbonaceous adsorbents. The Weber-Morris intraparticle diffusion model indicated three steps adsorption process for PNT and two step adsorption for TCE. In addition, the times needed to reach apparent equilibrium for the adsorption of PNT and TCE in the presence of hydrophobic (HPO) and hydrophilic (HPI) NOM solutions increased for all adsorbents (except for GO). In general, both NOM showed similar impacts on the adsorption rates of PNT and TCE. Aggregation of both GNS and CNTs rapidly occurred during initial couple hours of contact time during preloading, and spiking both PNT and TCE further increased their aggregation.

Carbon-based materials as adsorbent for antibiotics removal: Mechanisms and influencing factors

With the development of the removal of organic pollutants in the soil and water environment, antibiotics have been considered as emerging pollutants and received considerable attention among the scientific community. Thus, there is a need for an effective, economical, fast, operational feasible and environmental-friendly technology to remove antibiotics. Adsorption technology would be one of the most promising option on the basis that it best meets the criteria we set out above. From the most primitive activated carbon to the most innovative modified biochar, carbon-based materials have played a significant role in the adsorption process of antibiotics all the time. This paper reviews the adsorption behavior of some representative antibiotics (e.g., chloramphenicols, sulfonamides, tetracyclines, flouroquinolones) over various carbonaceous materials (i.e., activated carbon, carbon nanotubes, graphene, and biochar). Nevertheless, in addition to the structural characteristics and adsorption capacities of carbon-based materials, a special emphasis was placed on the underlying adsorption mechanisms and roles of different influencing factors in the adsorption process. Moreover, the knowledge gaps and research challenges have been highlighted, including design and optimization of the carbonaceous materials for antibiotics adsorption.

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.

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.

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.

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.

Linear free energy relationships for the adsorption of volatile organic compounds onto multiwalled carbon nanotubes at different relative humidities: comparison with organoclays and activated carbon

Accurate prediction of the sorption coefficients of volatile organic compounds (VOCs) on carbon nanotubes (CNTs) is of major importance for developing an effective VOC removal process and risk assessment of released nanomaterial-carrying contaminants. The linear free energy relationship (LFER) approach was applied to investigate the adsorption mechanisms of VOCs on multiwalled CNTs (MWCNTs). The gas-solid partition coefficients (log K_d) of 17 VOCs were determined at 0%, 55%, and 90% relative humidity (RH). The cavity/dispersion interaction is generally the most influential adsorption mechanism for all RH cases. The hydrogen-accepting interactions declined but with constant hydrogen-donating interactions during the increase of RH, suggesting that the acidity of VOC was important in forming sorptive interaction with the MWCNT surface. Moreover, the comparison of log K_d of VOCs on MWCNTs and other sorbents revealed that the sorption performance of MWCNTs is much more stable over a wider range of RHs due to better site availability and site quality. Furthermore, for all 6 adsorbents in all RHs, the positive contribution of hydrogen bonding ability was found as compared to the negative one found for sorbents completely in water, indicating that the hydrogen-bond donor and acceptor on the sorbent surface contribute to the sorption in the gas phase. In conclusion, the LFER-derived coefficients can be useful in predicting the performance of VOC adsorption on adsorbents and in facilitating the design of efficient VOC removal systems.