Sorption mechanism and predictive models for removal of cationic organic contaminants by cation exchange resins

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

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

Reconsidering operation pattern for cation-exchange resin assistant anaerobic fermentation of waste activated sludge: Shorting residence period towards dosage-reduction and anti-fouling

Short-chain fatty acids (SCFAs) generation through anaerobic fermentation has been regarded as a promising pathway to achieve carbon recovery and economic benefits in waste activated sludge management. Despite the cation exchange resin (CER) assistant anaerobic fermentation strategy has been previously reported for enhancing anaerobic fermentation, the overlarge CER usage and serious CER pollution have limited its engineering application. This study provided a reconsideration for the operation pattern modification. Through 4-day anaerobic fermentation with CER residence period shrinking to 1 day, 40.9% sludge VSS solubilization and reduction were achieved, triggering a considerable sludge hydrolysis rate of 28.4%. Thereby, SCFAs production was improved to 264.8 mg COD/g VSS. Such performances were approximately 80.2-87.8% of those with conventional CER residence period (8 days). The organic composition distribution and parallel factor analysis demonstrated that similar biodegradability and utilizability of fermentative liquid were achievable with various operation patterns. Compared with the conventional operation pattern, the modified operation pattern with shortened CER residence period (1 day) also displayed satisfying anaerobic fermentation efficiency and numerous engineering bene fits, e.g. decreased CER usage, reduced engineering footprint, relieved CER fouling, and increased operation convenience. The findings might provide sustainable development for CER assistant anaerobic fermentation strategy and enlighten the direction of anaerobic fermentation process.

Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric

Solute descriptors have been widely used to model chemical transfer processes through poly-parameter linear free energy relationships (pp-LFERs); however, there are still substantial difficulties in obtaining these descriptors accurately and quickly for new organic chemicals. In this research, models (PaDEL-DNN) that require only SMILES of chemicals were built to satisfactorily estimate pp-LFER descriptors using deep neural networks (DNN) and the PaDEL chemical representation. The PaDEL-DNN-estimated pp-LFER descriptors demonstrated good performance in modeling storage-lipid/water partitioning coefficient (log K_{storage-lipid/water}), bioconcentration factor (BCF), aqueous solubility (ESOL), and hydration free energy (freesolve). Then, assuming that the accuracy in the estimated values of widely available properties, e.g., logP (octanol-water partition coefficient), can calibrate estimates for less available but related properties, we proposed logP as a surrogate metric for evaluating the overall accuracy of the estimated pp-LFER descriptors. When using the pp-LFER descriptors to model log K_{storage-lipid/water}, BCF, ESOL, and freesolve, we achieved around 0.1 log unit lower errors for chemicals whose estimated pp-LFER descriptors were deemed "accurate" by the surrogate metric. The interpretation of the PaDEL-DNN models revealed that, for a given test chemical, having several (around 5) "similar" chemicals in the training data set was crucial for accurate estimation while the remaining less similar training chemicals provided reasonable baseline estimates. Lastly, pp-LFER descriptors for over 2800 persistent, bioaccumulative, and toxic chemicals were reasonably estimated by combining PaDEL-DNN with the surrogate metric. Overall, the PaDEL-DNN/surrogate metric and newly estimated descriptors will greatly benefit chemical transfer modeling.

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.

Coupling a Feedforward Network (FN) Model to Real Adsorbed Solution Theory (RAST) to Improve Prediction of Bisolute Adsorption on Resins

When predicting bisolute adsorption, the adsorbed solution theory (AST) and real adsorbed solution theory (RAST) either frequently show high prediction deviations or require bisolute adsorption data. Emerging feedforward network (FN) models can provide high prediction accuracy but lack broad applicability. To avoid those limitations, adsorption experiments were performed for a total of 12 single solutes and 55 bisolutes onto two widely used resins (MN200 and XAD-4). Different FN-based models were then built and compared with AST and RAST, based on which a new modeling strategy coupling FN to RAST and requiring only single-solute data was proposed. The root-mean-square error (RMSE) of predictions by the FN-RAST is 0.082 log units for 50 bisolute adsorption on MN200, much lower than that by AST (0.164) and slightly higher than that by RAST (0.069) or the best FN model (0.068). The FN-RAST model further provided satisfactory predictions for 5 bisolute adsorption on XAD-4 (RMSE = 0.10), which is comparable to that by RAST (0.10) and much lower than those by AST (0.26) and FN model (0.38). Therefore, the FN-RAST enjoys both satisfactory prediction accuracy and some broad applicability. The values of Abraham descriptors E and S were also founded to help assess/compare the nonideal behavior in different bisolute mixtures.

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.

Novel liquid fermentation medium of Cordyceps militaris and optimization of hydrothermal reflux extraction of cordycepin

In this study, we developed a novel liquid fermentation medium of Cordyceps militaris using pupa powder and wheat bran as nitrogen resources instead of the traditionally used peptone. This process not only reduced the cost by approximately 50%, but increased production by over 30%. Then, we explored a method to extract and purify cordycepin by combining hydrothermal reflux extraction with macroporous resin adsorption, which is inexpensive and suitable for the industrial production. The optimum conditions for hydrothermal reflux were extracting three times at 95 °C with 1:10 sample-to-water ratio, and the cordycepin purity with macroporous resin HPD-100 reached 95.23%.[Formula: see text].

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

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

Modeling Bisolute Adsorption of Aromatic Compounds Based on Adsorbed Solution Theories

A large number of organic contaminants are commonly found in industrial and municipal wastewaters. For proper unit design to remove contaminant mixtures by adsorption, multicomponent adsorption equilibrium models are necessary. The present work examined the applicability of Ideal Adsorbed Solution Theory (IAST), a prevailing thermodynamic model, and its derivatives, i.e., Segregated IAST (SIAST) and Real Adsorbed Solution Theory (RAST), to bisolute adsorption of organic compounds onto a hyper-cross-linked polystyrene resin, MN200. Both IAST and SIAST were found to be less accurate in fitting the experimental bisolute adsorption isotherms than RAST. RAST incorporated with an empirical four-parameter equation developed in this work can fit the adsorbed phase activity coefficients, γ_i, better than RAST combined with the Wilson equation or the Nonrandom two-liquid (NRTL) model. Moreover, two polyparameter linear free energy relationships were developed for the adsorption of a number of solutes at low concentrations in the presence of a major contaminant (4-methylphenol or nitrobenzene). Results show that these relationships have a great potential in predicting γ_i of solutes when the adsorbed amounts are dominated by a major contaminant. To the best of our knowledge, this is the first study predicting γ_i for bisolute adsorption based on molecular descriptors. Overall, our findings have proved a major step forward to accurately modeling multisolute adsorption equilibrium.

Aluminum-Cycle Ion Exchange Process for Hardness Removal: A New Approach for Sustainable Softening

From a sustainability viewpoint, sodium exchange softening, although used widely, is under scrutiny due to its production of excess Na-laden spent regenerant and subsequent discharge to the environment. Many arid regions are introducing regulations disallowing dumping of concentrated sodium salts, the residuals from popular Na-exchange softening. The sodium content of the softened water is, also, always higher than in the feed, which poses a dietary health concern when used for drinking or cooking. An efficient, easy-to-operate hardness removal process with reduced sodium in both the treated water and in the spent regenerant is an unmet global need. Use of a cation exchange resin in Al³⁺-form for hardness removal, that is, exchange of divalent Ca²⁺ or Mg²⁺ with trivalent Al³⁺, is counterintuitive, and this is particularly so, because the aluminum ion to be exchanged has higher affinity than calcium. Nevertheless, ion exchange accompanied by precipitation of aluminum hydroxide allows progress of the cation exchange reaction leading to hardness removal. Experimental results demonstrated that calcium can be consistently removed for multiple cycles using a stoichiometric amount of AlCl₃ as the regenerant. The process essentially operates at the maximum possible thermodynamic efficiency: removal of one equivalent of Ca²⁺ corresponds to use of one equivalent of Al³⁺ as a regenerant. During the Al-cycle process there is no increase in Na⁺ concentration and partial reduction in the total dissolved solids (TDS) of the treated water. It is noteworthy that the ion-exchange resin used, components of the fixed-bed column and operational protocol are nearly the same as traditional softening processes on Na-cycle. Thus, existing Na-cycle systems can be retrofitted into Al-cycle operation without major difficulty.

Column Chromatography To Obtain Organic Cation Sorption Isotherms

Column chromatography was evaluated as a method to obtain organic cation sorption isotherms for environmental solids while using the peak skewness to identify the linear range of the sorption isotherm. Custom packed HPLC columns and standard batch sorption techniques were used to intercompare sorption isotherms and solid-water sorption coefficients (Kd) for four organic cations (benzylamine, 2,4-dichlorobenzylamine, phenyltrimethylammonium, oxytetracycline) with two aluminosilicate clay minerals and one soil. A comparison of Freundlich isotherm parameters revealed isotherm linearity or nonlinearity was not significantly different between column chromatography and traditional batch experiments. Importantly, skewness (a metric of eluting peak symmetry) analysis of eluting peaks can establish isotherm linearity, thereby enabling a less labor intensive means to generate the extensive data sets of linear Kd values required for the development of predictive sorption models. Our findings clearly show that column chromatography can reproduce sorption measures from conventional batch experiments with the benefit of lower labor-intensity, faster analysis times, and allow for consistent sorption measures across laboratories with distinct chromatography instrumentation.