QSAR models for oxidative degradation of organic pollutants in the Fenton process

Experimental insights and modeling innovations: Deciphering Fe(VI) oxidation in imidazole ionic liquids through QSAR and RFR

In this investigation, we conducted a detailed analysis of the oxidation of 16 imidazole ionic liquid variants by Fe(VI) under uniform experimental setups, thereby securing a dataset of second-order reaction rate constants (kobs). This methodology ensures superior data consistency and comparability over traditional methods that amalgamate disparate data from varied studies. Utilizing 16 chemical structural parameters obtained via Density Functional Theory (DFT) as descriptors, we developed a Quantitative Structure Activity Relationship (QSAR) model. Through rigorous correlation analysis, Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Applicability Domain (AD) evaluation, we identified a pronounced negative correlation between the molecular orbital gap energy (Egap) and kobs. MLR analysis further underscored Egap as a pivotal predictive variable, with its lower values indicating heightened oxidative reactivity towards Fe(VI) in the ionic liquids, leading the QSAR model to achieve a predictive accuracy of 0.95. Furthermore, we integrated an advanced machine learning approach - Random Forest Regression (RFR), which adeptly highlighted the critical factors influencing the oxidation efficiency of imidazole ionic liquids by Fe(VI) through elaborate decision trees, feature importance assessment, Recursive Feature Elimination (RFE), and cross-validation strategies. The RFR model demonstrated a remarkable predictive performance of 0.98. Both QSAR and RFR models pinpointed Egap as a key descriptor significantly affecting oxidation efficiency, with the RFR model presenting lower root mean square errors, establishing it as a more reliable predictive tool. The application of the RFR model in this study significantly improved the model's stability and the intuitive display of key influencing factors, introducing promising advanced analytical tools to the field of environmental chemistry.

Prediction of phthalate acid esters degradation in soil using QSAR model: A combined consideration of soil properties and quantum chemical parameters

Phthalic acid esters (PAEs) are predominant hazardous substances and endocrine-disrupting compounds to be controlled in soil. The degradation behaviors of PAEs in soil had been long term concerned. Thus, the degradation rate (K) is important for assessing theexposure risk and is of great significance in evaluating the ecological risk of PAEs in soil environment. But by far, quantitative structure activity relationship (QSAR) models for PAEs degradation have rarely been considered in soil environment. In this study, quantum chemical parameters were considered along with soil properties as two kinds of descriptors in QSAR model. A total of 32 logk of PAEs were collected from reference and experiment. Degradation kinetics in soils were determined by pseudo-first order kinetic models. The residual concentration of PAEs in Udic ferrosols and Aquic cambisols suggesting a potential expose risks of PAEs to ecosystem in soil. The QSAR model between logk and quantum chemical parameters revealed that E_HOMO and qC- are two predominant factors in determining logk value. Furthermore,our study further indicated that soil organic matter (SOM) as new predictor contributes more to predict logk values of PAEs during degradation process than pH. Results from this study make a new contribution for methods to predict the degradation of PAEs in soil environment and highlight the potential to evaluate the environmental risks of degradation of PAEs.

Quantum parameter analysis of the adsorption mechanism by freshly formed ferric hydroxide for synthetic dye and antibiotic wastewaters

In this study, the adsorption effect by freshly formed ferric hydroxide (FFFH) for the removal of 47 synthetic dye and antibiotic wastewaters under different pH conditions (i.e., pH = 4, 7, and 10) was investigated. The average total organic carbon (TOC) removal rates (R_exp) of pollutants under acidic, neutral, and alkaline conditions were 27.10 ± 3.21%, 15.16 ± 2.48%, and 9.72 ± 2.81%, respectively. By analyzing the characteristics of FFFH measured by SEM, XRD, FT-IR, TGA and BET, the properties of pollutants, and the values of R_exp, one can conclude that the large specific surface area and rich hydroxyl groups on the surface of FFFH were the reasons for its adsorption capacity for organic pollutants, and the electrostatic adsorption was the main reason for higher removal rate in acidic condition. Subsequently, to better elucidate the intrinsic factors influencing the removal rates at the molecular structure level, three optimal quantitative structure-activity relationship (QSAR) models were established by using multiple linear regression (MLR) analysis. Results of model validations (e.g., regression coefficient, internal and external verifications, and Y-randomization) showed that the established models exhibited excellent stability, reliability, and robustness with the values of R² = 0.7544, 0.7764, 0.7528, Q²_INT = 0.6451, 0.6836, 0.6228, and Q²_EXT = 0.5890, 0.6029, 0.7298 under acidic, neutral, and alkaline conditions, respectively. The results of quantum parameter analysis revealed that the adsorption mechanism of FFFH for dyes and antibiotics mainly includes the activity of adsorption site, the behavior of electron transfer and the strength of molecular polarity.

Efficient Degradation of 2,4-Dichlorophenol on Activation of Peroxymonosulfate Mediated by MnO2

Sulfate radical based-advanced oxidation process has received increasing interest in the remediation of wastewater and contaminated soil. In this study, degradation of 2, 4-dichlorophenol (2, 4-DCP) was investigated over peroxymonosulfate (PMS) activation by MnO₂, which was prepared by liquid-phase oxidation method. The prepared MnO₂ was characterized by transition electron microscopy, X-ray diffraction, N₂ adsorption-desorption, and X-ray photoelectron spectroscopy. Characterization results showed that α-MnO₂ exhibited the highest surface area and Mn (III) content. The PMS activation by MnO₂ in 2, 4-DCP degradation followed the order of α-MnO₂ >  γ-MnO₂ > β-MnO₂, which is dependent on the properties of MnO₂ including crystal structure, surface area and Mn (III) content. Influences of initial concentration of 2, 4-DCP, PMS and MnO₂ dosage, pH and co-existing inorganic ions on the degradation were examined. Electron paramagnetic resonance (EPR) and quenching experiments with ethanol and tert-butanol suggested that sulfate radicals were the dominant radicals in the process. Findings in this study indicated that α-MnO₂ was an attractive catalyst for activation of PMS to degrade 2, 4-DCP in aqueous solution.

Predicting the rate constants of volatile organic compounds (VOCs) with ozone reaction at different temperatures

Based on the bond order, fukui indices and other related descriptors, as well as temperature, a new QSAR model was established to predict the rate constant (kO3) of VOCs degradation by O3. 302 logkO3 values (178-409 K) of 149 VOCs were divided into training set (242 logkO3) and test set (60 logkO3), respectively, which were used to construct and verify the QSAR model. The optimal model (R2 = 0.83, q2 = 0.82, Qext2 = 0.72) shows that EHOMO, BOx and q(C-)n have a greater influence on the value of logkO3. In addition, fukui indices and logkO3 are well correlated. The applicability domains of the current models can be used to predict kO3 of a wide range of VOCs at different temperatures.

Quantitative structure activity relationship (QSAR) modelling of the degradability rate constant of volatile organic compounds (VOCs) by OH radicals in atmosphere

The reaction with hydroxyl radicals (•OH) is an important way to remove the most volatile organic compounds (VOCs) in atmospheric environment. Thus, the reaction rate constant (k_OH) is important for assessing the persistence and exposure risk of VOCs, and is of great significance in evaluating the ecological risk of volatile organic chemicals. Fukui indices and bond order have a large effect on the degradation of VOCs, but so far, quantitative structure activity relationship (QSAR) models for VOCs degradation have rarely been considered these two factors. In this study, these two momentous factors will be considered along with other relevant quantitative parameters. A total of 180 substances are divided into training set (144 substances) and test set (36 substances), which are used to build and validate quantitative structure activity relationship (QSAR) models, respectively. Internal, external verification and y-randomization tests showed that the established model had excellent stability and reliability. The energy of the highest occupied molecular orbital (E_HOMO), the possibility of being attacked by radicals (f (0)_n) and the breaking of chemical bonds (BO_x) are the main factors affecting VOCs removal. Finally, the scope of the application domain was determined and the robustness of the model was further verified.

Two new predictors combined with quantum chemical parameters for the selection of oxidants and degradation of organic contaminants: A QSAR modeling study

Oxidation is an attractive treatment method to effectively remove organic contaminants in water. In this study, degradation of 30 organic compounds in different oxidation systems was evaluated, including oxygen (O₂), hydrogen peroxide (H₂O₂), ozone (O₃) and hydroxyl radical (HO). First, a quantitative structure-activity relationship (QSAR) model for oxidation-reduction potentials (ORPs) of organics was developed and exhibited a good performance to predict ORP values of organics with evaluation indices of squared correlation coefficient (R²) = 0.866, internal validation (q²) = 0.811 and external validation (Q_ext²) = 0.669. Four quantum parameters, including f(+)_n, f(-)_n, E_HOMO and E_B3LYP dominate the ORP values. Subsequently, a relationship between reaction rates (k) and the difference of ORP for oxidants and organics (ΔE_oxi-org) was established, however, which was limited (R²= 0.697). Therefore, two new predictors (slopes and intercepts) are proposed based on the linear relationships between k values and ORPs of oxidants. These new predictors can be applied to estimate the reaction rates and minimum oxidation potential for organic compounds. Afterwards, to express the two predictors, QSAR models were established. The two optimal QSAR models fitted very well with experimental values and were demonstrated to be stable and accurate based on R² (0.982 and 0.965), q² (0.950 and 0.950) and Q_ext² (0.985 and 0.989). BO_x, q(H)⁺ and q(C)_x were main factors influencing the slopes and intercepts. This study developed methods to predict ORPs of organics and established two new predictors to estimate the reaction rates undergoing different oxidation processes, offering new insights into the oxidant selection.

Quantitative Structure-Toxicity Relationship analysis of combined toxic effects of lignocellulose-derived inhibitors on bioethanol production

This study performed a Quantitative Structure-Toxicity Relationship (QSTR) model to evaluate the combined toxicity of lignocellulose-derived inhibitors on bioethanol production. Compared with all the control groups, the combined systems exhibited lower conductivity values, higher oxidation-reduction potential values, as well as maximum inhibition rates. These results indicated that the presence of combined inhibitors had a negative effect on the bioethanol fermentation process. Meanwhile, QSTR model was excellent for evaluating the combined toxic effects at lower ferulic acid concentration (([1:4] × IC50)) and (([1:1] × IC50)), due to higher R² values (0.994 and 0.762), lower P values (0.000 and 0.023) and relative error values (less than 30%). The obtained results also showed that the combined toxic effects of ferulic acid and representative lignocellulose-derived inhibitors were relevant to different molecular descriptors. Meanwhile, the interactions of combined inhibitors were weaker when ferulic acid was at low concentration ([1:4] × IC50).

Predicting the degradation potential of Acid blue 113 by different oxidants using quantum chemical analysis

In this work, quantum chemical analysis was used to predict the degradation potential of a recalcitrant dye, Acid blue 113, by hydrogen peroxide, ozone, hydroxyl radical and sulfate radical. Geometry optimization and frequency calculations were performed at ‘Hartree Fock’, ‘Becke, 3-parameter, Lee–Yang–Parr’ and ‘Modified Perdew-Wang exchange combined with PW91 correlation’ levels of study using 6-31G* and 6-31G** basis sets. The Fourier Transform-Raman spectra of Acid blue 113 were recorded and a complete analysis on vibrational assignment and fundamental modes of model compound was performed. Natural bond orbital analysis revealed that Acid blue 113 has a highly stable structure due to strong intermolecular and intra-molecular interactions. Mulliken charge distribution and molecular electrostatic potential map of the dye also showed a strong influence of functional groups on the neighboring atoms. Subsequently, the reactivity of the dye towards the oxidants was compared based on the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy values. The results showed that Acid blue 113 with a HOMO value -5.227 eV exhibits a nucleophilic characteristic, with a high propensity to be degraded by ozone and hydroxyl radical due to their lower HOMO-LUMO energy gaps of 4.99 and 4.22 eV respectively. On the other hand, sulfate radical and hydrogen peroxide exhibit higher HOMO-LUMO energy gaps of 7.92 eV and 8.10 eV respectively, indicating their lower reactivity towards Acid blue 113. We conclude that oxidation processes based on hydroxyl radical and ozone would offer a more viable option for the degradation of Acid blue 113. This study shows that quantum chemical analysis can assist in selecting appropriate advanced oxidation processes for the treatment of textile effluent.

Molecular insights into the mechanism and the efficiency-structure relationship of phosphorus removal by coagulation

Types and structures of phosphorus compounds influence the removal of phosphorus by coagulation. Until now, the molecular-level interaction between coagulants and phosphorus (especially organophosphates) and the relationship between removal efficiency and phosphorus structure have not been clear. This work investigated the removal of phosphorus with different structures using conventional coagulants (poly aluminum chloride (PACl) and polymerized ferric sulfate (PFS)) and a novel covalently-bound inorganic-organic hybrid coagulant (CBHyC). CBHyC removed more than 98% of phosphate and most of organophosphates, had more stable performance than PACl and PFS, and was less affected by pH, initial phosphorus concentration, and co-occurring materials. Molecular dynamics simulation demonstrated that CBHyC removed phosphorus mainly through electrostatic attraction and hydrophobic interaction. Furthermore, this work established QSAR (quantitative structure activity relationship) models for removal efficiency and organophosphate structure for the first time. The model showed that atomic charges of phosphorus atoms (Q_P) and hydrogen atoms (Q_H⁺) in the system and the energy gap (ΔE_MO) affected electronegativity and hydrophobicity, thus influencing organophosphate removal efficiency. The model had high fitting precision and good predictive ability and has the potential to greatly reduce the cost of optimizing processes and conditions for phosphorus removal.

2D-QSAR and 3D-QSAR simulations for the reaction rate constants of organic compounds in ozone-hydrogen peroxide oxidation

Synergistic oxidation of ozone (O₃) and hydrogen peroxide (H₂O₂) is an effective water treatment for the elimination of organic pollutants. In this study, 23 organic compounds were conducted to study the reaction rate constants during O₃-H₂O₂ oxidation. Then, two- and three-dimensional quantitative structure-activity relationship (QSAR) models were established to investigate the factors influencing the reaction rate constants by using multiple linear regression method and comparative molecular similarity index analysis (CoMSIA) method, respectively. Both of the two models showed good performance on predicting the reaction rate constants, the associated statistical indices of 2D-QSAR and 3D-QSAR models were R² = 0.898 and 0.952, q² = 0.841 and 0.951, Q_ext² = 0.968 and 0.970, respectively. But varied in the influence factors, as for the 2D-QSAR model, three quantum chemical parameters, included dipole moment, the largest change of charge in each atom during the nucleophilic attack, the maximum positive partial charge on a hydrogen atom linked with a carbon atom affected the reaction rate. While in the 3D-QSAR model, the electrostatic field played the most important role in evaluating the reaction rate with the contribution of 35.8%, followed by hydrogen bond acceptor and hydrophobic fields with the contribution of 24.9% and 23.2%, respectively. These two models provided predictive tools to study the influencing factors for the degradation of organics and might potentially be applied for estimating the removal properties of unknown organics in O₃-H₂O₂ oxidation process.

Nitrogen transformation of 41 organic compounds during SCWO: A study on TN degradation rate, N-containing species distribution and molecular characteristics

Supercritical water oxidation (SCWO) of 41 N-containing compounds was examined under a stable temperature and pressure of 450 °C and 24 MPa, respectively, reaction time ranged from 0.5 to 6 min with 500% excess oxygen, resulted in the degradation rate constants of total organic carbon (k_TOC) and total nitrogen (k_TN) were 0.162-3.693 and 0.065-0.416 min^-1, respectively. The N-containing products were primary N₂, ammonium and nitrate. As for amino-group compounds, the main product was ammonia, while for nitro-group compounds, nitrate was the predominant. In terms of diazo and N-heterocyclic group compounds, the main products generally were nitrate and ammonium, respectively. Interestingly, 2-, 3- and 4-nitroaniline, containing both of nitro and amino group, would directly decompose into N₂. The reaction pathways of acid orange 74 was proposed based on Fukui indices, which generally included denitrification, ring-open and mineralization. Density functional theory (DFT) method was applied to calculate the quantum properties of the 41 N-containing compounds in order to further examine the relationship between TN removal and molecular structural characteristics. The correlation result showed that among all the 17 molecular characteristics, F(+)_n, F(-)_n, F(0)_n, and E_HOMO achieved high correlation coefficients.

Supercritical water oxidation of 2-, 3- and 4-nitroaniline: A study on nitrogen transformation mechanism

Supercritical water oxidation (SCWO) of 2-, 3- and 4-nitroaniline (NA) was investigated under residence time of 1-6 min, pressure of 18-26 MPa, temperature of 350-500 °C, with initial concentration of 1 mM and 300% excess oxygen. Among these operating conditions, temperature and residence time played a more significant role in decomposing TOC and TN than pressure. Moreover, the products of N-containing species were mainly N₂, ammonia and nitrate. When temperature, pressure and retention time enhanced, the yields of NO₃^- and org-N were reduced, the amount of N₂ was increasing, the proportion of NH₄⁺, however, presented a general trend from rise to decline in general. The experiment of aniline/nitrobenzene indicated that TN removal behavior between amino and nitro groups would prefer to happen in the molecule rather than between the molecules, therefore, the smaller interval between the amino and nitro group was the more easily to interreact. This might explain the reason why TN removal efficiency was in an order that 2-NA > 3-NA > 4-NA. The NH₄⁺/NO₃^- experiment result demonstrated that ammonia and nitrate did convert into N₂ during SCWO, however, the formation of N₂ was little without auxiliary fuel. Density functional theory (DFT) method was used to calculate the molecular structures of 2-, 3- and 4-NA to further explore reaction mechanism, which verified that amino group was more easily to be attacked than nitro group. Based on these results, the conceivable reaction pathways of 2-, 3- and 4-NA were proposed, which contained three parts, namely denitrification, ring-open and mineralization.

Three-dimensional electro-Fenton oxidation of N-heterocyclic compounds with a novel catalytic particle electrode: high activity, wide pH range and catalytic mechanism

A novel three-dimensional (3D) heterogeneous electro-Fenton (EF) system with sludge deserved activated carbon from sewage and iron sludge (SAC-Fe) as catalytic particle electrodes (CPEs) was constructed in this study.

Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error

Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.

Quantitative Structure Activity Relationship Models for the Antioxidant Activity of Polysaccharides

In this study, quantitative structure activity relationship (QSAR) models for the antioxidant activity of polysaccharides were developed with 50% effective concentration (EC50) as the dependent variable. To establish optimum QSAR models, multiple linear regressions (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used, and 11 molecular descriptors were selected. The optimum QSAR model for predicting EC50 of DPPH-scavenging activity consisted of four major descriptors. MLR model gave EC50 = 0.033Ara-0.041GalA-0.03GlcA-0.025PC+0.484, and MLR fitted the training set with R = 0.807. ANN model gave the improvement of training set (R = 0.96, RMSE = 0.018) and test set (R = 0.933, RMSE = 0.055) which indicated that it was more accurately than SVM and MLR models for predicting the DPPH-scavenging activity of polysaccharides. 67 compounds were used for predicting EC50 of the hydroxyl radicals scavenging activity of polysaccharides. MLR model gave EC50 = 0.12PC+0.083Fuc+0.013Rha-0.02UA+0.372. A comparison of results from models indicated that ANN model (R = 0.944, RMSE = 0.119) was also the best one for predicting the hydroxyl radicals scavenging activity of polysaccharides. MLR and ANN models showed that Ara and GalA appeared critical in determining EC50 of DPPH-scavenging activity, and Fuc, Rha, uronic acid and protein content had a great effect on the hydroxyl radicals scavenging activity of polysaccharides. The antioxidant activity of polysaccharide usually was high in MW range of 4000-100000, and the antioxidant activity could be affected simultaneously by other polysaccharide properties, such as uronic acid and Ara.

Relationship between reaction rate constants of organic pollutants and their molecular descriptors during Fenton oxidation and in situ formed ferric-oxyhydroxides

Fenton oxidation is a promising water treatment method to degrade organic pollutants. In this study, 30 different organic compounds were selected and their reaction rate constants (k) were determined for the Fenton oxidation process. Gaussian09 and Material Studio software sets were used to carry out calculations and obtain values of 10 different molecular descriptors for each studied compound. Ferric-oxyhydroxide coagulation experiments were conducted to determine the coagulation percentage. Based upon the adsorption capacity, all of the investigated organic compounds were divided into two groups (Group A and Group B). The percentage adsorption of organic compounds in Group A was less than 15% (wt./wt.) and that in the Group B was higher than 15% (wt./wt.). For Group A, removal of the compounds by oxidation was the dominant process while for Group B, removal by both oxidation and coagulation (as a synergistic process) took place. Results showed that the relationship between the rate constants (k values) and the molecular descriptors of Group A was more pronounced than for Group B compounds. For the oxidation-dominated process, EHOMO and Fukui indices (f(0)x, f(-)x, f(+)x) were the most significant factors. The influence of bond order was more significant for the synergistic process of oxidation and coagulation than for the oxidation-dominated process. The influences of all other molecular descriptors on the synergistic process were weaker than on the oxidation-dominated process.