QSARs to predict adsorption affinity of organic micropollutants for activated carbon and β-cyclodextrin polymer adsorbents

Structural Features of Styrene-Functionalized Cyclodextrin Polymers That Promote the Adsorption of Perfluoroalkyl Acids in Water

Cross-linked β-cyclodextrin (β-CD) polymers are promising adsorbents for the removal of per- and polyfluoroalkyl substances (PFAS) from contaminated water sources, including contaminated groundwater, drinking water, and wastewater. We previously reported porous, styrene-functionalized β-cyclodextrin (StyDex) polymers derived from radical polymerization with vinyl comonomers. Because of the versatility of these polymerizations, StyDex polymer compositions are tunable, which facilitates efforts to establish structure-adsorption relationships and to discover improved materials. Here, we evaluate the material properties and PFAS adsorption of 20 StyDex derivatives with varied comonomer structure and loading, regiochemistry of styrene placement on the CD monomer, and CD size. A StyDex polymer containing N,N'-dimethylbutyl ammonium ions exhibited the most effective PFAS adsorption in batch experiments. Furthermore, a StyDex polymer containing β-CD exhibited size-selective host-guest interactions with perfluoroalkyl acids (PFAAs) and neutral contaminants in aqueous electrolyte when compared to similar polymers containing either α-CD or γ-CD. Polymers based on β-CD monomers with an average of seven styrene groups randomly positioned over the 21 available hydroxyl groups performed similarly to those based on a β-CD monomer functionalized regiospecifically at each of the seven 6' positions. The former β-CD monomer is prepared in a single step from unmodified β-CD, so the ability to use it without compromising performance demonstrates promise for developing economically competitive adsorbents. These results offered important insights into structure-adsorption properties of StyDex polymers and will inform the design of improved StyDex formulations.

Experimental determination and QSAR analysis of the rate constants for SO5•- reactions with aromatic micropollutants in water

S(IV)-based systems used for advanced oxidation processes (AOPs) have been constructed for the degradation of organic contaminants via oxysulfur radicals, including SO3•-, SO4•-, and SO5•-. Although SO5•- is proposed as an active species in AOPs processes, research on the reactivity of SO5•- has remained unclear. In this work, 53 target aromatic micropollutants (AMPs), including 13 phenols, 27 amines, and 13 PPCPs were selected to determine the second-order reaction rate constants for SO5•- using the competitive kinetics method, in which the [Formula: see text] values, observed at pH 4 ranged from (2.44 ± 0.00) × 105 M-1 s-1 to (4.41 ± 0.28) × 107 M-1 s-1. Quantitative structure-activity relationship (QSAR) models for the oxidation of AMPs by SO5•- were developed based on 40 [Formula: see text] values of amines and phenols, and their molecular descriptors, using the stepwise multiple linear regression method. This comprehensive model exhibited the excellent goodness-of-fit (Radj2 = 0.802), robustness (QLOO2 = 0.749), and predictability (Qext2 = 0.656), and the one-electron oxidation potential (Eox), energy of the highest occupied molecular orbital energy (EHOMO), and most positive net atomic charge on the carbon atoms (qC+) were considered the most influential descriptors for the comprehensive model, indicating that SO5•- oxidizes pollutants via single electron transfer reaction and exhibits a strong oxidation capacity, especially for pollutants containing electron-donating groups. Moreover, the [Formula: see text] values of 13 PPCPs were predicted using this comprehensive model, which suggested the practical application significance of the QSAR model. This study emphasizes the direct oxidation capacity of SO5•-, which is important to evaluate and simulate AOPs based on S(IV).

Trace Organic Contaminant Removal from Municipal Wastewater by Styrenic β-Cyclodextrin Polymers

Trace organic contaminants (TrOCs) present major removal challenges for wastewater treatment. TrOCs, such as perfluoroalkyl and polyfluoroalkyl substances (PFAS), are associated with chronic toxicity at ng L-1 exposure levels and should be removed from wastewater to enable safe reuse and release of treated effluents. Established adsorbents, such as granular activated carbon (GAC), exhibit variable TrOC removal and fouling by wastewater constituents. These shortcomings motivate the development of selective novel adsorbents that also maintain robust performance in wastewater. Cross-linked β-cyclodextrin (β-CD) polymers are promising adsorbents with demonstrated TrOC removal efficacy. Here, we report a simplified and potentially scalable synthesis of a porous polymer composed of styrene-linked β-CD and cationic ammonium groups. Batch adsorption experiments demonstrate that the polymer is a selective adsorbent exhibiting complete removal for six out of 13 contaminants with less adsorption inhibition than GAC in wastewater. The polymer also exhibits faster adsorption kinetics than GAC and ion exchange (IX) resin, higher adsorption affinity for PFAS than GAC, and is regenerable by solvent wash. Rapid small-scale column tests show that the polymer exhibits later breakthrough times compared to GAC and IX resin. These results demonstrate the potential for β-CD polymers to remediate TrOCs from complex water matrices.

Machine learning: Next promising trend for microplastics study

Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.

Discriminative Behavior of Cyclodextrin Polymers against Dissolved Organic Matter: Role of Cavity Size and Sorbate Properties

Cyclodextrin polymers (CDPs) are promising next-generation adsorbents in water purification technologies. The selectivity of the polymer derivate cross-linked with tetrafluoroterephthalonitrile (TFN-CDP) for nonionic and cationic micropollutants (MPs) over dissolved organic matter (DOM) renders the adsorbent also attractive for many analytical applications. The molecular drivers of the observed selectivity are, nonetheless, not yet fully understood. To provide new insights into the sorption mechanism, we (i) synthesized TFN-CDPs with different cavity sizes (α-, β-, γ-CDP); (ii) assessed their extraction efficiencies for selected nonionic MPs in competition with different DOM size fractions (<1, 1-3, 3-10, >10 kDa) to test for size-selectivity; and (iii) performed nontargeted, ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry analysis on CDP-extracted DOM compounds (<1 kDa) to probe for molecular sorbate properties governing their selective sorption. First, no evidence of size-selectivity was obtained through either the different CD cavity sizes (i) or the two independent approaches (ii) and (iii). Second, we found a dominant impact of sorbate oxygenation and polarity on the extraction of DOM and MPs, respectively, with relatively oxygen-poor/nonpolar molecules favorably retained on all α-, β-, and γ-CDP. Third, our data indicates exclusion of an anionic matrix, such as carboxylic acids, but preferential sorption of cationic nitrogen-bearing DOM, pointing at repulsive and attractive forces with the negatively charged cross-linker as a likely reason. Therefore, we ascribe TFN-CDP's selectivity to nonpolar and electrostatic interactions between MPs/DOM and the polymer building blocks. These molecular insights can further aid in the optimization of efficient and selective sorbent design for environmental and analytical applications.

Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction

Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol-1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.

Avoiding Interferences in Advance: Cyclodextrin Polymers to Enhance Selectivity in Extraction of Organic Micropollutants for Carbon Isotope Analysis

Compound-specific isotope analysis (CSIA) of organic water contaminants can provide important information about their sources and fate in the environment. Analyte enrichment from water remains nonetheless a critical yet inevitable step before measurement. Commercially available solid-phase extraction (SPE) sorbents are inherently nonselective leading to co-extraction of concurrent dissolved organic matter (DOM) and in turn to analytical interferences, especially for low-occurring contaminants. Here, we (i) increased extraction selectivity by synthesizing cyclodextrin polymers (α-, β-, γ-CDP) as SPE sorbents, (ii) assessed their applicability to carbon isotope analysis for a selection of pesticides, and (iii) compared them with commonly used commercial sorbents. Extraction with β-CDP significantly reduced backgrounds in gas chromatography-isotope ratio mass spectrometry (GC-IRMS) and enhanced sensitivity by a factor of 7.5, which was further confirmed by lower carbon-normalized C_DOM/C_analyte ratios in corresponding extracts as derived from dissolved organic carbon (DOC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Gibbs free energies of adsorption demonstrated weak competition between DOM and analyte on the three CDPs. No isotopic fractionation (Δδ¹³C within ± 0.3‰) was observed for the investigated pesticides after using β-CDP as an SPE sorbent covering a range of concentrations (5-500 μg L^-1), flow velocities (5-40 cm min^-1), and sorbent regeneration (up to six times). The present study highlights the benefit of selecting innovative extraction sorbents to avoid interferences in advance. This strategy in combination with existing cleanup approaches offers new prospects for CSIA at field concentrations of tens to hundreds of nanograms per liter.

Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches

The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO₂ P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO₂ P25 in the roles such as an adsorbent and photocatalyst. The effect of pH and γ(TiO₂ P25) as variables on the extent of removal of organics by adsorption on TiO₂ P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of the studied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of the studied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO₂ P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO₂ P25, providing insights into influential structural features facilitating adsorption process.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., K_XAD-A) are indispensable to obtain POPs concentration, and the K_XAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of K_XAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting K_XAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²_adj and Q²_ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²_adj = 0.991, Q²_ext = 0.935, RMSE_tra = 0.271 and RMSE_ext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting K_XAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Surface functional groups determine adsorption of pharmaceuticals and personal care products on polypropylene microplastics

The pervasiveness of microplastics (MPs), which can absorb pharmaceuticals and personal care products (PPCPs), has a certain impact on pollutant migration in natural waters. The adsorption behaviors of PPCPs on the aged polypropylene (PP) followed the pseudo-second-order kinetics and Langmuir isotherm, and the adsorption capacity (q_e) on the aged PP was much higher than that on the fresh PP. The Weber-Morris and Boyd models confirmed that the liquid-film and intra-particle diffusion affected the adsorption of PPCPs on the aged PP while the surface diffusion was a rate-limiting step for the fresh PP. The analysis of SEM-EDS, BET, FT-IR, and XPS further showed that changes in the type and content of the surface functional groups of PP led to differences in adsorption capacity and adsorption interactions. The Dragon-descriptor-based LFER and the quantum-chemical-descriptor-based QSAR models reflected the difference in adsorption interaction mechanisms. The examined models showed that the adsorption of the fresh PP toward PPCPs relied on hydrophobic and hydrogen bonding interaction, while for the aged PP electrostatic interaction and hydrogen bonding controlled the adsorption. The findings clarified interactions between PPCPs and MPs and provided a theoretical basis for the assessment of environmental behavior and ecological risk when MPs and PPCPs coexist.

Identifying the physicochemical properties of β-cyclodextrin polymers that determine the adsorption of perfluoroalkyl acids

Cyclodextrin polymers (CDPs) are emerging adsorbents with demonstrated potential to remove perfluoroalkyl acids (PFAAs) from water. However, little is known about how the physicochemical properties of different types of CDPs determine PFAA adsorption on CDPs. In this study, we investigated the adsorption performance of 34 CDPs which consist of 14 different crosslinkers and exhibit a wide range of physicochemical properties. The performance metrics included adsorption kinetics, equilibrium adsorption density, and adsorption affinity for six PFAAs. We then used complementary bivariate and multivariate analyses to discover relationships between sixteen measurable physicochemical properties of the CDPs and their performance as adsorbents. We found that: (1) CDPs with a less negative or more positive surface charge will exhibit enhanced adsorption of all types of PFAAs; (2) CDPs with greater porosity and surface area will exhibit enhanced adsorption kinetics for all types of PFAAs; (3) CDPs with greater crosslinker content will exhibit enhanced adsorption of short-chain PFAAs; (4) CDPs containing more hydrophobic crosslinkers will exhibit enhanced equilibrium adsorption density and adsorption affinity for longer-chain PFAAs; and (5) CDPs with smaller particle sizes will exhibit enhanced adsorption kinetics and equilibrium adsorption density for all PFAAs. These insights will enable the further development of CDPs and other novel adsorbents to optimize their performance for removing PFAAs during water and wastewater treatment or groundwater remediation.

Combining Experimental Sorption Parameters with QSAR to Predict Neonicotinoid and Transformation Product Sorption to Carbon Nanotubes and Granular Activated Carbon

We recently discovered that transformation of the neonicotinoid insecticidal pharmacophore alters sorption propensity to activated carbon, with products adsorbing less than parent compounds. To assess the environmental fate of novel transformation products that lack commercially available standards, researchers must rely on predictive approaches. In this study, we combined computationally derived quantitative structure-activity relationship (QSAR) parameters for neonicotinoids and neonicotinoid transformation products with experimentally determined Freundlich partition constants (log K F for sorption to carbon nanotubes [CNTs] and granular activated carbon [GAC]) to model neonicotinoid and transformation product sorption. QSAR models based on neonicotinoid sorption to functionalized/nonfunctionalized CNTs (used to generalize/simplify neonicotinoid-GAC interactions) were iteratively generated to obtain a multiple linear regression that could accurately predict neonicotinoid sorption to CNTs using internal and external validation (within 0.5 log units of the experimentally determined value). The log K F,CNT values were subsequently related to log K F,GAC where neonicotinoid sorption to GAC was predicted within 0.3 log-units of experimentally determined values. We applied our neonicotinoid-specific model to predict log K F,GAC for a suite of novel neonicotinoid transformation products (i.e., formed via hydrolysis, biotransformation, and chlorination) that do not have commercially available standards. We present this modeling approach as an innovative yet relatively simple technique to predict fate of highly specialized/unique polar emerging contaminants and/or transformation products that cannot be accurately predicted via traditional methods (e.g., pp-LFER), and highlights molecular properties that drive interactions of emerging contaminants.

Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds

Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (K_PE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the K_PE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (K_mic-w) and the LDPE-micelle partition coefficient (K_PE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the K_PE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental K_PE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.

How properties of low molecular weight model competitors impact organic micropollutant adsorption onto activated carbon at realistically asymmetric concentrations

Low molecular weight (LMW) dissolved organic matter (DOM) is the predominant competitor for adsorption sites against organic micropollutants (OMPs) in activated carbon adsorption. However, top-down approaches using highly complex mixtures of real water DOM do not allow to concisely examine the impacts of specific LMW DOM molecular properties on competitive adsorption. Therefore, we followed a bottom-up approach using fifteen model compounds (mDOM) to elucidate how important DOM characteristics, including hydrophobicity and unsaturated structures (ring, double/triple bond), impact competitiveness. Large concentration asymmetry (~500 μg DOC/μg OMP) made mDOM compounds, which were overall less preferentially adsorbed than OMPs, become competitive against OMPs and inhibit OMP adsorption kinetics by pre-occupation of adsorption sites. Our results revealed that both hydrophobicity interactions and π-interactions increased mDOM competitiveness, while π-interactions outweighed hydrophobic interactions. However, π-interactions could not be satisfactorily evaluated with a parameter such as specific ultraviolet absorbance (SUVA) due to interferences of carboxyl groups in aromatic mDOMs. Instead, mDOM adsorbability, described by mDOM adsorption capacity, proved to be a comprehensive indicator for mDOM competitiveness. To our knowledge, this is the first study that systematically clarifies the impacts of intricately interacting molecular properties on DOM adsorption and the related competition against OMP adsorption. DOM adsorbability may inspire a new fractionation, and assist the further isolation, identification and detailed characterization of LMW DOM competitors in real DOM-containing waters.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

Polymerized Molecular Receptors as Adsorbents to Remove Micropollutants from Water

Organic micropollutants (MPs) are increasing in number and concentration in water systems as a result of human activities. Often from human origin, these micropollutants build up in the environment because organisms lack the mechanisms to metabolize these substances, which cause negative health, ecological, and economic effects. Adsorption-based remediation processes for these compounds often rely on activated carbon materials. However, activated carbons are ineffective against certain MPs, exhibit low removal efficiencies in the presence of common aqueous matrix constituents, and require energy-intensive activation and regeneration processes. To overcome the deficiencies of traditional technologies, novel adsorbents based on molecular receptors offer promising alternative solutions. This Account describes the recent development of polymer adsorbents based on molecular receptors for removing trace organic chemicals from water. Polymer networks based on molecular receptors have high binding affinities for many MPs but, unlike activated carbons, have a specific molecule-binding mechanism that prevents these polymers from being fouled by matrix constituents such as natural organic matter. The size and hydrophobic pocket of the β-cyclodextrin receptor preferentially adsorbs target molecules such as organic micropollutants in the presence of matrix constituents, and the nature of the cross-linker tunes the binding affinity and selectivity of the adsorbent for specific classes of MPs, including those of varying charge and hydrophobicity. β-cyclodextrin polymers also exhibit rapid adsorption kinetics and are easily regenerated. This Account details β-cyclodextrin polymers made with three different cross-linkers, including a polymer that is postsynthetically transformed from a negatively charged polymer to a positively charged polymer to invert the polymer's micropollutant adsorption profile. Morphological constraints have so far limited these cross-linked polymers' ability to be used in commercial applications, but two methods to create larger and more uniformly sized particles for use in flow-through applications are described here. β-Cyclodextrin polymers are useful for trapping organic micropollutants such as bisphenol A, perfluorooctanoic acid, and many kinds of pharmaceuticals and pesticides, but their binding pockets are too large to capture micropollutants that are small or of high polarity. Other molecular receptors such as resorcinarene cavitands can target lower-molecular-weight MPs, including halomethane disinfection byproducts and industrial solvents, that are not bound strongly by β-cyclodextrins. These materials demonstrate the potential of expanding the library of polymers based on molecular receptors. Overall, these emerging adsorbents show promise for the removal of legacy and emerging MPs from water, as well as the ability to rationally tune the adsorbent's structure to target the most persistent and toxic MPs.

Removal of micropollutants in drinking water using UV-LED/chlorine advanced oxidation process followed by activated carbon adsorption

This study investigated the removal of three selected micropollutants (i.e., bisphenol A, diclofenac and caffeine) in drinking water using the UV-LED/chlorine advanced oxidation process (AOP) followed by activated carbon adsorption. The degradation of bisphenol A, diclofenac and caffeine was predominantly contributed by chlorination (>60%), direct UV photolysis (>80%) and radical oxidation (>90%), respectively, during the treatment by the UV-LED/chlorine AOP at three tested UV wavelengths (i.e., 265, 285 and 300 nm). The most effective UV wavelengths for the degradation of bisphenol A, diclofenac and caffeine were 265, 285 and 300 nm, respectively. The degradation of all the three micropollutants was enhanced with increasing pH from 6 to 8, though the reasons for the pH dependence were different. The residues of the micropollutants and their degradation (by)products were removed by post-adsorption using granular activated carbon (GAC). Interestingly and more importantly, the adsorption rates of the degradation (by)products were 2-3 times higher than the adsorption rates of the corresponding micropollutants, indicating the formation of more adsorbable (by)products after the AOP pre-treatment. The UV-LED/chlorine AOP followed by GAC adsorption provides a multi-barrier treatment system for enhancing micropollutant removal in potable water. The findings also suggest the merit of the sequential use of UV-LEDs followed by GAC in treating chlorine-containing potable water in small-scale water treatment systems (e.g., point-of-use or point-of-entry water purifiers).

β-Cyclodextrin Polymers with Different Cross-Linkers and Ion-Exchange Resins Exhibit Variable Adsorption of Anionic, Zwitterionic, and Nonionic PFASs

Per- and polyfluoroalkyl substances (PFASs) occur in groundwater as mixtures of anionic, cationic, zwitterionic, and nonionic species, although few remediation technologies have been evaluated to assess the removal of different types of PFASs. In this study, we evaluated the performance of three β-cyclodextrin polymers (CDPs), an anion-exchange (AE) resin, and a cation-exchange (CE) resin for the removal of anionic, zwitterionic, and nonionic PFASs from water. We found that a CDP with a negative surface charge rapidly removes all zwitterionic PFASs with log K_D values ranging between 2.4 and 3.1, and the CE resin rapidly removes two zwitterionic PFASs with log K_D values of 1.8 and 1.9. The CDPs with a positive surface charge rapidly remove all anionic PFASs with log K_D values between 2.7 and 4.1, and the AE resin removes all anionic PFASs relatively slowly with log K_D values between 2.0 and 2.3. All adsorbents exhibited variable removal of the nonionic PFASs and some adsorption inhibition at higher pH values and in the presence of groundwater matrix constituents. Our findings provide insight into how adsorbents can be combined to remediate groundwater contaminated with complex mixtures of different types of PFASs.

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 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.

QSPR study on the polyacrylate-water partition coefficients of hydrophobic organic compounds

The partition coefficient is essential for the analysis of organic chemicals using solid-phase microextraction (SPME) techniques. In this study, a quantitative structure-property relationship (QSPR) model was developed with chemical descriptors for the prediction of the polyacrylate (PA)-water partition coefficient (K_PA-w). The major variables influencing K_PA-w in the QSPR model were CrippenlogP (crippen octanal-water partition coefficient), RNCG (relative negative charge-most negative charge/total negative charge), VE2_Dzv (average coefficient sum of the last eigenvector from the Barysz matrix/weighted by van der Waals volume), and ATSC4v (centred Broto-Moreau autocorrelation-lag 4/weighted by van der Waals volume). The relative determination coefficient (R²) and cross-validation coefficient (Q²) were 0.898 and 0.858, respectively, which implied that the model had excellent robustness. Mechanistic interpretation suggested that the factors affecting the partitioning process between PA and water are the hydrophobicity, relative negative charge, and van der Waals volume of a chemical. The results of this study provide a good tool for predicting the log K_PA-w values of diverse hydrophobic organic compounds (HOCs) within the applicability domain to reduce experimental costs and the time required for innovation.

Evaluating the effects of water matrix constituents on micropollutant removal by activated carbon and β-cyclodextrin polymer adsorbents

The performance of adsorbents for the removal of organic micropollutants (MPs) from water can be influenced by the presence of water matrix constituents. The objective of this research was to evaluate the influence of water matrix constituents on the performance of coconut-shell activated carbon (CCAC), porous β-cyclodextrin polymer (CDP), and CDP coated on cellulose microcrystal (CDP@CMC) adsorbents. MP removals were measured in batch experiments for a mixture of 90 MP at 1 μg L^-1 and MP breakthrough was measured in rapid small-scale column test (RSSCT) experiments for a mixture of 15 MP at 500 ng L^-1. All experiments were performed first with nanopure water, and subsequently with six different water samples collected from two separate groundwater, surface water, and wastewater effluent sources. The results of batch and RSSCT experiments demonstrate more rapid adsorption kinetics and less adsorption inhibition in the presence of matrix constituents for CDP adsorbents relative to CCAC. Further, the treatment capacity of CDP@CMC in the RSSCT experiments was higher than that of CCAC, particularly in more complex water matrices. Statistical analyses were performed to investigate associations between adsorption inhibition among groups of MPs and the concentrations of specific water matrix constituents. For CCAC, adsorption inhibition was observed for all MPs and was primarily attributed to the presence of dissolved organic matter with molar weight less than 1000 Da. For CDP adsorbents, adsorption inhibition was primarily observed for cationic MPs and was attributed to the screening of the negative surface charge of CDP by inorganic ions in water samples with high ionic strength. These data further demonstrate the value of CDP as an alternative adsorbent to CCAC for the removal of MPs during water and wastewater treatment.

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

Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R²) of 0.949 and cross-validation coefficient (Q_Loo²) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.

The classification and application of cyclodextrin polymers: a review

After introducing the concept of cyclodextrin polymers, their classification and applications have been summarized.