Influence of Divalent Cation Inhibition and Dissolved Organic Matter Enhancement on Phenol Oxidation Kinetics by Manganese Oxides.

Manganese oxides can oxidize organic compounds, such as phenols, and may potentially be used in passive water treatment applications. However, the impact of common water constituents, including cations and dissolved organic matter (DOM), on this reaction is poorly understood. For example, the presence of DOM can increase or decrease phenol oxidation rates with manganese oxides. Furthermore, the interactions of DOM and cations and their impact on the phenol oxidation rates have not been examined. Therefore, we investigated the oxidation kinetics of six phenolic contaminants with acid birnessite in ten whole water samples. The oxidation rate constants of 4-chlorophenol, 4-tert-octylphenol, 4-bromophenol, and phenol consistently decreased in all waters relative to buffered ultrapure water, whereas the oxidation rate of bisphenol A and triclosan increased by up to 260% in some waters. Linear regression analyses and targeted experiments demonstrated that the inhibition of phenol oxidation is largely determined by cations. Furthermore, quencher experiments indicated that radical-mediated interactions from oxidized DOM contributed to enhanced oxidation of bisphenol A. The variable changes between compounds and water samples demonstrate the challenge of accurately predicting contaminant transformation rates in environmentally relevant systems based on experiments conducted in the absence of natural water constituents.

A Comparative Study on the Oxidation Mechanisms of Substituted Phenolic Pollutants by Ferrate(VI) through Experiments and Density Functional Theory Calculations.

In this work, the oxidation of five phenolic contaminants by ferrate(VI) was comparatively investigated to explore the possible reaction mechanisms by combined experimental results and theoretical calculations. The second-order rate constants were positively correlated with the energy of the highest occupied molecular orbital. Considering electronic effects of different substituents, the easy oxidation of phenols by ferrate(VI) could be ranked as the electron-donating group (-R) > weak electron-withdrawing group (-X) > strong electron-withdrawing group (-(C═O)-). The contributions of reactive species (Fe(VI), Fe(V)/(IV), and •OH) were determined, and Fe(VI) was found to dominate the reaction process. Four main reaction mechanisms including single-oxygen transfer (SOT), double-oxygen transfer (DOT), •OH attack, and electron-transfer-mediated coupling reaction were proposed for the ferrate(VI) oxidation process. According to density functional theory calculation results, the presence of -(C═O)- was more conducive for the occurrence of DOT and •OH attack reactions than -R and -X, while the tendency of SOT for different substituents was -R > -(C═O)- > -X and that of e--transfer reaction was -R > -X > -(C═O)-. Moreover, the DOT pathway was found in the oxidation of all four substituted phenols, indicating that it may be a common reaction mechanism during the ferrate(VI) oxidation of phenolic compounds.

Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks.

Numerous harmful phenolic contaminants are discharged into water that pose a serious threat to environment where two of the most important purification methodologies for the mitigation of phenolic contaminants are adsorption and photocatalysis. Besides cost, each process has drawbacks in terms of productivity, environmental impact, sludge creation, and the development of harmful by-products. To overcome these limitations, the modeling and optimization of water treatment methods is required. Artificial Intelligence (AI) is employed for the interpretation of treatment-based processes due to powerful learning, simplicity, high estimation accuracy, effectiveness, and improvement of process efficiency where artificial neural networks (ANNs) are most frequently employed for predicting and analyzing the efficiency of processes applied for the mitigation of these phenolic contaminants from water. ANNs are superior to conventional linear regression models because the latter are incapable of dealing with non-linear systems. ANNs can also reduce the operational cost of treating phenol-contaminated water. A correlation coefficient of >0.99 can be achieved using ANN with enhanced phenol mitigation percentage accuracy generally ranging from 80 % to 99.99 %. Using ANN optimization, the maximum phenol mitigation efficiencies achieved were 99.99 % for phenol, 99.93 % for bisphenol A, 99.6 % for nonylphenol, 97.1 % for 2-nitrophenol, 96.6 % for 4-chlorophenol and 90 % for 2,6-dichlorophenol. In numerous ANN models, Levenberg-Marquardt backpropagation algorithm for training was employed using MATLAB software. This study overviews their employment and application for optimization and modeling of removal processes and explicitly discusses the important input and output parameters necessary for better performance of the system. The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. Furthermore, several challenges and future prospects have also been discussed.

Introducing a prediction method for the photodegradation of p-cresol, a phenolic contaminant of emerging concern, in wastewater effluent.

Despite extensive efforts to understand the photodegradation of phenolic contaminants of emerging concern (PhCECs) in aquatic systems, prediction methods, especially in waters containing effluent organic matter (EfOM), remain underdeveloped. This study introduces a prediction method for p-cresol, a representative PhCECs, based on correlations between EfOM optical parameters and p-cresol kinetic parameters. We examined p-cresol photodegradation in various EfOM samples, characterized by their optical properties, and used the reaction rate coefficient between EfOM and p-cresol, α3EfOM⁎, to quantify and predict p-cresol degradation in different wastewater effluent samples. Results showed significant correlations between p-cresol's photodegradation rate constant (0.144 to 0.441 h-1) and EfOM characteristics, with α3EfOM⁎ values ranging from 4 × 1011 to 10 × 1011 M-1 s-1. The method was validated with p-cresol at concentrations ranging from 25 to 100 µM and multiple EfOM samples. The method's applicability was further evaluated using propranolol, a pharmaceutical contaminant of emerging concern, demonstrating its versatility for predicting the degradation behavior of other contaminants in different wastewater samples. The method accurately predicted p-cresol and propranolol degradation across diverse wastewater samples, suggesting its potential for expansion to other classes of contaminants, aiding in water quality management, improving wastewater treatment processes, and enhancing environmental risk assessments.

Electrochemically producing high-valent iron-oxo species for phenolics-laden high chloride wastewater pretreatment.

Electrochemical advanced oxidation processes (EAOPs) have shown great promise for treating industrial wastewater contaminated with phenolic compounds. However, the presence of chloride in the wastewater leads to the production of undesirable chlorinated organic and inorganic byproducts, limiting the application of EAOPs. To address this challenge, we investigated the potential of incorporating Fe(II) and Fe(III) into the EAOPs with a boron-doped diamond (BDD) anode under near-neutral conditions. Our findings revealed that both Fe(II) and Fe(III) facilitated the generation of high-valent iron-oxo species (Fe(IV) and Fe(V)) in the anodic compartment, thereby reducing the oxidation contribution of reactive chlorine species. Remarkably, the addition of 1000 µM Fe(II) under high chloride conditions resulted in over a 2.8-fold increase in the oxidation rate of 50 µM phenolic contaminants at pH 6.5. Furthermore, 1000 µM Fe(II) contributed to a reduction of more than 66% in the formation of chlorinated byproducts, consequently enhancing the biodegradability of the treated water. Additionally, transitioning from batch mode to continuous flow mode further amplified the positive effects of Fe(II) on the EAOPs. Overall, this study presents a modified electrochemical approach that simultaneously enhanced the degradation of phenolic contaminants and improved the biodegradability of wastewater with high chloride concentrations.

DOM optical parameters as a tool to understand degradation of phenolic contaminants of emerging concern.

Composition and source of dissolved organic matter (DOM) in water influence the rate of production of reactive intermediates (RIs), affecting the photodegradation of phenolic contaminants of emerging concern (PhCECs). However, this relationship has not been fully quantified. Here, for the first time, we propose a mechanism for photodegradation of a surrogate of PhCECs, p-cresol, in different DOM standard solutions under simulated sunlight irradiation. More importantly, the correlation of DOM optical parameters and p-cresol photodegradation kinetic parameters was determined by Pearson correlation. Results showed that indirect photodegradation was the only degradation pathway for p-cresol, mainly through reaction with excited triplet state of dissolved organic matter (3DOM*). Singlet oxygen (1O2) and hydroxyl radical (•OH) hindered degradation of p-cresol by decreasing the steady state concentration of 3DOM*. Moreover, less aromatic and smaller molecular size DOM showed higher steady-state concentration and quantum yield of 1O2, and 3DOM*, resulting in faster p-cresol photodegradation. Finally, 7 out of 8 optical parameters showed strong correlation with the p-cresol photodegradation rate constant. The mechanism and correlations found are a potential tool to predict PhCECs photodegradation in water using DOM optical parameters.

Impacts of properties of dissolved organic matters on indirect photodegradation of genistein.

Excited triplet states of dissolved organic matters (3DOM*) are one of the most important photochemically-produced reactive intermediates leading to transformation of organic contaminants. However, relationships of photodegradation kinetics of different dissociation states of phenolic organic contaminants with chemical components or properties of 3DOM* are largely unknown. In this study, roles of 3DOM* in photodegradation of polyhydroxy phenolic genistein (Gs) at pH 5, 8 and 12 were investigated taking five kinds of DOM from different sources as examples. Relationships between photodegradation kinetics constants and DOM properties were built. Results showed that the contributions of direct 3DOM*-induced reactions to the total indirect photodegradation of Gs and second-order reaction rate constants (kDOM,Gs) of Gs with 3DOM* increased with pH increases. This was mainly attributed to decreases in vertical ionization energy of Gs at higher pH, endowing Gs with stronger electron donating capacities. kDOM,Gs was found to positively correlate with the specific ultraviolet absorbance at 254 nm, reflecting aromaticity of DOM, and negatively correlate with the absorbance ratio at 254 and 365 nm and contents of dissociated acidic functional groups of DOM, representing molecular weights of DOM, antioxidants and the repulsive forces between 3DOM* and Gs. This study provided a new insight into relationship between DOM properties and indirect photodegradation kinetics of phenolic contaminants in aquatic environments.

Z-scheme bismuth-rich bismuth oxide iodide/bismuth oxide bromide hybrids with novel spatial structure: Efficient photocatalytic degradation of phenolic contaminants accelerated by in situ generated redox mediators.

Z-scheme photocatalysts commonly possess both high charge separation efficiency and strong redox ability. In this paper, novel 3-dimensional/2-dimensional (3D/2D) structured bismuth-rich bismuth oxide iodide/bismuth oxide bromide (Bi4O5I2/BiOBr) hybrids with Z-scheme heterojunctions were first prepared. The in situ generated I3-/I- and Bi5+/Bi3+ redox mediators in Bi4O5I2/BiOBr hybrids greatly improve their photocatalytic activity toward phenolic contaminants. Their structure, morphology, optical properties, and electrochemical properties were characterized. Scanning electron microscopy images demonstrated that the 2D BiOBr nanoplates were evenly and tightly anchored on the surface of the 3D Bi4O5I2 microspheres. This novel 3D/2D spatial structure was beneficial for the formation of heterojunctions between BiOBr and Bi4O5I2, which improved the quantum efficiency through interfacial charge transfer. The Bi4O5I2/BiOBr hybrids exhibited excellent photocatalytic activities toward o-phenylphenol (OPP), p-tert-butylphenol, 4-chlorophenol, and p-nitrophenol. Bi4O5I2/BiOBr-5 possessed the best activity in decomposing OPP, which was approximately 3.43 times higher than that of pure Bi4O5I2. The well-matched energy bands of components in the hybrids facilitated the interfacial charge separation through an effective Z-scheme transfer direction guided by I3-/I- and Bi5+/Bi3+ redox mediators. Based on the results of electron spin paramagnetic resonance and trapping experiments, a mechanism was proposed for the degradation of pollutants using the Bi4O5I2/BiOBr hybrids.

Electro membrane extraction using sorbent filled porous membrane bag.

Electro membrane extraction-solid-liquid phase microextraction (EME-SLPME) was developed for the first time to determine phenolic contaminants in water. The extraction system consisted of a solid/liquid interface that permitted a three-phase microextraction approach involving an aqueous sample (donor phase): an organic solvent-sorbent within a membrane bag, and an organic solvent (extractant phase), operated in a direct immersion sampling system. The sorbent, reduced graphene oxide/polyvinyl alcohol, synthesized using graphene oxide and polyvinyl alcohol by dispersing the graphene oxide in polyvinyl alcohol and chemically reducing it in aqueous solution. The prepared sorbent was dispersed in 1-octanol and the solution was immobilized by sonication in the membrane bag wall pores which was in contact with the aqueous donor solution and organic extractant solvent (1-octanol) in the main bag itself. The analytes were transported by application of an electrical potential difference of 100V across the sorbent/solvent phase from the aqueous sample into the organic extractant phase in the membrane bag. After extraction and derivatization, gas chromatography-mass spectrometry was used to determine the derivatized analytes. This proposed EME-LSPME procedure provided high extraction efficiency with relative recoveries up to 99.6%. A linearity range of between 0.05 and 100µgL(-1) with corresponding coefficients of determination (r(2)) of between 0.987 and 0.996 were obtained. The limits of detection were in the range of between 0.003 and 0.053µgL(-1). This proposed method was successfully applied to the extraction of phenolic contaminants from water sample.