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2D particle surfactants are attractive for the formation of highly stable emulsions and use as templates to prepare composite structures with performance properties dependent on the composition. Cobalt oxide nanosheets (CONs) are a relatively understudied class of 2D particle surfactants that can be produced by the chemical exfoliation of lithium cobalt oxide, a transition metal oxide known for excellent gas-sensing, catalytic, and electrochemical properties. Here, we report a simple method to access CONs stabilized oil-in-water Pickering emulsions and use these as templates to prepare particles with a core of polymer and shell of CONs. Salt-flocculated CONs produce emulsions with droplets of hydrophobic monomer ( e.g. , styrene) in water that are stable for at least 24 hours, and suspension free radical polymerization is used to produce CON-armored particles. Characterization by X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and thermal gravimetric analysis (TGA) confirmed the presence of CONs on the surface of the polymer core. We then demonstrated the CON-armored polymer particles can activate the oxidant peroxymonosulfate (PMS) for the degradation of bisphenol A (BPA). Freshly prepared and artificially aged CON-armored particles showed full degradation of BPA in less than an hour and no decrease in activity was observed after two uses. CON-armored particles combine high surface area of the nanosheets with the ease of recoverability of the particles. 

Using machine learning (ML) to develop quantitative structure—activity relationship (QSAR) models for contaminant reactivity has emerged as a promising approach because it can effectively handle non-linear relationships. However, ML is often data-demanding, whereas data scarcity is common in QSAR model development. Here, we proposed two approaches to address this issue: combining small datasets and transferring knowledge between them. First, we compiled four individual datasets for four oxidants, i.e., SO4•-, HClO, O3 and ClO2, each dataset containing a different number of contaminants with their corresponding rate constants and reaction conditions (pH and/or temperature). We then used molecular fingerprints (MF) or molecular descriptors (MD) to represent the contaminants; combined them with ML algorithms to develop individual QSAR models for these four datasets; and interpreted the models by the Shapley Additive exPlantion (SHAP) method. The results showed that both the optimal contaminant representation and the best ML algorithm are dataset dependent. Next, we merged these four datasets and developed a unified model, which showed better predictive performance on the datasets of HClO, O3 and ClO2 because the model ‘corrected’ some wrongly learned effects of several atom groups. We further developed knowledge transfer models based on the second approach, the effectiveness of which depends on if there is consistent knowledge shared between the two datasets as well as the predictive performance of the respective single models. This study demonstrated the benefit of combining small similar datasets and transferring knowledge between them, which can be leveraged to boost the predictive performance of ML-assisted QSAR models. 

As one of the most powerful approaches to mechanistically understanding complex chemical reaction systems and performing simulations or predictions, kinetic modeling has been widely used to investigate advanced oxidation processes (AOPs). However, most of the available models are built based on limited systems or reaction mechanisms so they cannot be readily extended to other systems or reaction conditions. To overcome such limitations, this study developed a comprehensive model on phenol oxidation with over 550 reactions, covering the most common reaction mechanisms in nine AOPs—four peroxymonosulfate (PMS), four peroxydisulfate (PDS), and one H2O2 systems—and considering the effects of co-existing anions (chloride, bromide, and carbonate) and product formation. Existing models in the literature were first gathered and revised by correcting inaccurately used reactions and adding other necessary reactions. Extensive model tuning and validation were then conducted by fitting the model against experimental data from both this study and the literature. When investigating the effects of anions, we found that PDS/CuO suffered the least impact, followed by the H2O2/UV and other PDS systems, and finally the PMS systems. Halogenated organic byproducts were mainly observed in the PMS systems in the presence of halides. Most of the 556 reactions were found to be important based on the sensitivity analysis, with some involving anions even among the most important, which explained why these anions can substantially alter some of the reaction systems. With this comprehensive model, a deep understanding and reliable prediction can be made for the oxidation of phenol (and likely other phenolic compounds) in systems containing one or more of the above AOPs. 

We used ab initio molecular dynamics simulations to address Mn oxidation states in different Mn systems. We first develop a correlation between Mn partial atomic charge and the oxidation state, based on results of 31 simulations on known Mn aqueous complexes. The results collapse to a master curve; the dependence of partial atomic charge on oxidation state weakens with increasing oxidation state, which concurs with a previously proposed feedback effect. This correlation is then used to address oxidation states in Mn systems used as oxidants. Simulations of MnO2 polymorphs immersed in water give average oxidation states (AOS) in excellent agreement with experimental results, in that b-MnO2 has the highest AOS, a-MnO2 has an intermediate AOS, and d-MnO2 has the lowest AOS. Furthermore, the oxidation state varies substantially with the atom’s environment, and these structures include Mn(III) and Mn(V) species that are expected to be active. In regard to the MnO4−/HSO3−/O2 system that has been shown to be a highly effective oxidant, we propose a novel Mn complex that could give rise to the oxidative activity, where Mn(III) is stabilized by sulfite and dissolved O2 ligands. Our simulations also show that the O2 would be activated to O22- in this complex under acidic conditions, and could lead to the formation of OH radicals that serve as oxidants.