Insights into the Mechanism of Ozone Activation and Singlet Oxygen Generation on N-Doped Defective Nanocarbons: A DFT and Machine Learning Study

Machine Learning-Assisted Molecular Structure Embedding for Accurate Prediction of Emerging Contaminant Removal by Ozonation Oxidation

Ozone has demonstrated high efficacy in depredating emerging contaminants (ECs) during drinking water treatment. However, traditional quantitative structure-activation relationship (QSAR) models often fall short in effectively normalizing and characterizing diverse molecular structures, thereby limiting their predictive accuracy for the removal of various ECs. This study uses embedded molecular structure vectors generated by a graph neural network (GNN), combined with functional group prompts, as inputs to a feedforward neural network. A data set of 28 ECs and 542 data points, representing diverse molecular structures and physiochemical properties, was built to predict the residual rate of ECs (REC) in ozonation oxidation. Compared to traditional QSAR models, the GNN-based molecular structure embedded methods significantly improve prediction accuracy. The resulting KANO-EC model achieved an R2 of 0.97 for REC, demonstrating its ability to capture complex structural features. Moreover, KANO-EC maintains exceptional interpretability, elucidating key functional groups (e.g., carbonyls, hydroxyls, aromatic rings, and amines) involved in the oxidation mechanism. This study presents the KANO-EC model as a novel approach for predicting the ozonation removal efficiency of current and potential ECs. The model also provides valuable insights for developing efficient control strategies for ensuring the long-term safety and sustainability of drinking water supplies.

Enhanced electro-fenton degradation of tetracycline pharmaceutical wastewater by N-doped carbon modified titanium membrane aeration: Formation of highly selective singlet oxygen

Singlet oxygen (1O2), the lowest excited electronic state of molecular oxygen, plays an important role in advanced oxidation, but how to directionally enhance the generation of 1O2 is a challenge. In this study,we use membrane aeration electrode modified by carbon-nitrogen for the first time to enhance the generation of 1O2 in the EF (Electro-Fenton) system. The carbon-nitrogen supported tubular titanium membrane (TTM@CN) aeration electrode was prepared by a simple dopamine-loaded one-step sintering method. A membrane aeration EF system was designed with TTM@CN as cathode and netted ruthenium-iridium titanium electrode as anode, and the output of 1O2 was greatly improved. The results of quenching experiments show that the main way of singlet oxygen production is 3O2 → H2O2 → 1O2. In addition, the results of density functional theory (DFT) show that the empty orbital of C above Fermi level in heterojunction is obviously filled, and the density of states tends to shift to the depth of valence band. The system with metal Ti as carrier can quickly transfer electrons to the layer of C, which makes the states density of C increase significantly near Fermi level. It can reduce 3O2 to H2O2 more quickly, and H2O2 can be further converted to 1O2. The system showed excellent degradation performance in a wide pH range (1-12) and excellent stability in 20 cycle experiments, which provided a reference significance for promoting the development of sustainable EF technology.

Combination of Density Functional Theory and Machine Learning Provides Deeper Insight of the Underlying Mechanism in the Ultraviolet/Persulfate System

The competition between radical and nonradical processes in the activated persulfate system is a captivating and challenging topic in advanced oxidation processes. However, traditional research methods have encountered limitations in this area. This study employed DFT combined with machine learning to establish a quantitative structure-activity relationship between contributions of active species and molecular structures of pollutants in the UV persulfate system. By comparing models using different input data sets, it was observed that the protonation and deprotonation processes of organic molecules play a crucial role. Additionally, the condensed Fukui function, as a local descriptor, is found to be less effective compared to the dual descriptor due to its imprecise definition of f0. The sulfate radical exhibits high selectivity toward local electrophilic sites on molecules, while global descriptors determined by their chemical properties provide better predictions for contribution rates of hydroxyl radicals. Interestingly, there exists a piecewise function relating the contribution rates of different active species to ELU-HO, which is further supported by experimental data. Currently, this relationship cannot be explained by classical chemical theory and requires further investigation. Perhaps this is a new perspective brought to us by combining DFT with machine learning.

Carbonaceous materials in structural dimensions for advanced oxidation processes

Carbonaceous materials have attracted extensive research and application interests in water treatment owing to their advantageous structural and physicochemical properties. Despite the significant interest and ongoing debates on the mechanisms through which carbonaceous materials facilitate advanced oxidation processes (AOPs), a systematic summary of carbon materials across all dimensions (0D-3D nanocarbon to bulk carbon) in various AOP systems remains absent. Addressing this gap, the current review presents a comprehensive analysis of various carbon/oxidant systems, exploring carbon quantum dots (0D), nanodiamonds (0D), carbon nanotubes (1D), graphene derivatives (2D), nanoporous carbon (3D), and biochar (bulk 3D), across different oxidant systems: persulfates (peroxymonosulfate/peroxydisulfate), ozone, hydrogen peroxide, and high-valent metals (Mn(VII)/Fe(VI)). Our discussion is anchored on the identification of active sites and elucidation of catalytic mechanisms, spanning both radical and nonradical pathways. By dissecting catalysis-related factors such as sp2/sp3 C, defects, and surface functional groups that include heteroatoms and oxygen groups in different carbon configurations, this review aims to provide a holistic understanding of the catalytic nature of different dimensional carbonaceous materials in AOPs. Furthermore, we address current challenges and underscore the potential for optimizing and innovating water treatment methodologies through the strategic application of carbon-based catalysts. Finally, prospects for future investigations and the associated bottlenecks are proposed.

Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals

Machine learning (ML) is expected to bring new insights into the impact of organic structures on the reaction mechanisms in reactive oxygen species oxidation. However, understanding the underlying chemical mechanisms still faces challenges due to the limited interpretability of the ML models. In this study, interpretable ML models were established to predict the second-order rate constants between hydroxyl radicals (•OH) and organics (k•OH). It was found that the energy of the highest occupied molecular orbital (EHOMO), the number of aromatic rings (NAR), and the number of carbon atoms of organics (NC) have important impacts on k•OH. The positive correlation between k•OH and EHOMO can be explained by the regularity of electrophilic reaction, while the relationship between k•OH and NAR and NC seems to be related with reactive sites. Furthermore, a rapid judgment method for reaction mechanism was developed based on an unsupervised learning approach which automatically divided organics into three clusters. Additionally, this methodology was applied to the reaction between organics and sulfate radicals. This study offers a rational model for predicting reaction mechanisms and provides more insights into the impact of organic structures on the reaction mechanism from the perspective of big data.

Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process

Heterogeneous activation of peracetic acid (PAA) process is a promising method for removing organic pollutants from water. Nevertheless, this process is constrained by several complex factors, such as the selection of catalysts, optimization of reaction conditions, and identification of mechanism. In this study, a task decomposition strategy was adopted by combining a catalyst and reaction condition optimization machine learning (CRCO-ML) model and a mechanism identification machine learning (MI-ML) model to address these issues. The Categorical Boosting (CatBoost) model was identified as the best-performing model for the dataset (1024 sets and 7122 data points) in this study, achieving an R2 of 0.92 and an RMSE of 1.28. Catalyst composition, PAA dosage, and catalyst dosage were identified as the three most important features through SHAP analysis in the CRCO-ML model. The HCO3- is considered the most influential water matrix affecting the k value. The errors between all reverse experiment results and the predictions of the CRCO-ML and MI-ML models were <10 % and 15 %, respectively. This interdisciplinary work provides novel insights into the design and application of the heterogeneous activation of PAA process, significantly contributing to the rapid development of this technology.

Co-catalysis strategy for low-oxidant-consumption Fenton-like chemistry: From theoretical understandings to practical applications and future guiding strategies

Recently, great effects have been made for the co-catalysis strategy to solve the bottlenecks of Fenton system. A series of co-catalysis strategies using various inorganic metal co-catalysts and organic co-catalysts have been developed in various oxidant (i.e., hydrogen peroxide (H2O2) and persulfate) systems with significantly promotion of catalytic performances and lower oxidant consumption (only 5-10 % of conventional Fenton/Fenton-like systems). However, the developments of these co-catalysis strategies from theoretical understandings to practical applications and future guiding strategies were overlooked, which was an essential problem that must be considered for the future scale-up applications of co-catalysis systems. In this paper, these co-catalysis strategies with low-oxidant-consumption characteristics have been reviewed by the comparison of their co-catalysis mechanisms, as well as their advantages and disadvantages. We also discussed the recent developments of amplifying devices based on the co-catalysis systems. The scale-up performances of co-catalysis strategies based on these amplifying devices have also been assessed. In addition, future guiding strategies for the development of co-catalysis strategy with low-oxidant-consumption characteristics have also been first time outlined by the combination of the technical-economic analysis (TEA), life cycle assessment (LCA) and machine learning (ML). Finally, the paper systematically discusses the development opportunities, technical bottlenecks and future development directions of co-catalysis strategies with the prospect of large-scale applications. Basically, this work provides a systematic review on co-catalysis strategy with low-oxidant-consumption characteristic from theoretical understandings to practical applications and future guiding strategies.

Non-radical oxidation driven by iron-based materials without energy assistance in wastewater treatment

Chemical oxidation is extensively utilized to mitigate the impact of organic pollutants in wastewater. The non-radical oxidation driven by iron-based materials is noted for its environmental friendliness and resistance to wastewater matrix, and it is a promising approach for practical wastewater treatment. However, the complexity of heterogeneous systems and the diversity of evolutionary pathways make the mechanisms of non-radical oxidation driven by iron-based materials elusive. This work provides a systematic review of various non-radical oxidation systems driven by iron-based materials, including singlet oxygen (1O2), reactive iron species (RFeS), and interfacial electron transfer. The unique mechanisms by which iron-based materials activate different oxidants (ozone, hydrogen peroxide, persulfate, periodate, and peracetic acid) to produce non-radical oxidation are described. The roles of active sites and the unique structures of iron-based materials in facilitating non-radical oxidation are discussed. Commonly employed identification methods in wastewater treatment are compared, such as quenching, chemical probes, spectroscopy, mass spectrometry, and electrochemical testing. According to the process of iron-based materials driving non-radical oxidation to remove organic pollutants, the driving factors at different stages are summarized. Finally, challenges and countermeasures are proposed in terms of mechanism exploration, detection methods and practical applications of non-radical oxidation driven by iron-based materials. This work provides valuable insights for understanding and developing non-radical oxidation systems.

High-efficient M-NC single-atom catalysts for catalytic ozonation in water purification: Performance and mechanisms

Heterogeneous catalytic ozonation (HCO) holds promise in water purification but suffers from limited accessible metal sites, metal leaching, and unclear structure-activity relationships. This work reported M-NC (M=Co, Ni, Fe, and Mn) single-atom catalysts (SACs) with high atomic efficiency and minimal metal release. The new HCO systems, especially the Co-based system, exhibited impressive performance in various refractory contaminant removal, involving various reactive species generation, such as •OHads, •OHfree, *O, and 1O2. For sulfamethoxazole removal, the normalized kobs for Co-NC, Ni-NC, Fe-NC, and Mn-NC were determined as 13.53, 3.94, 3.55, and 4.13 min-1·mMmetal-1·g·m-2 correspondingly, attributed to the abundant acid sites, faster electron transfer, and lower energy required for O3 decomposition and conversion. The metal atoms and hydroxyl groups, individually serving as Lewis and Bronsted acid sites (LAS and BAS), were the primary centers for •OH generation and O3 adsorption. The relationships between active sites and both O3 utilization and •OH generation were found. LAS and BAS were responsible for O3 adsorption, while strong LAS facilitated O3 conversion into •OH. Theoretical calculations revealed the catalytic mechanisms involved O3→ *O→ *OO→ O3•-→ •OH. This work highlights the significance of SAC design for HCO and advances the understanding of atomic-level HCO behavior.

Sustainable H2O2 production via solution plasma catalysis

Clean production of hydrogen peroxide (H2O2) with water, oxygen, and renewable energy is considered an important green synthesis route, offering a valuable substitute for the traditional anthraquinone method. Currently, renewable energy-driven production of H2O2 mostly relies on soluble additives, such as electrolytes and sacrificial agents, inevitably compromising the purity and sustainability of H2O2. Herein, we develop a solution plasma catalysis technique that eliminates the need for soluble additives, enabling eco-friendly production of concentrated H2O2 directly from water and O2. Screening over 40 catalysts demonstrates the superior catalytic performance of carbon nitride interacting with discharge plasma in water. High-throughput density functional theory calculations for 68 models, along with machine learning using 29 descriptors, identify cyano carbon nitride (CCN) as the most efficient catalyst. Solution plasma catalysis with the CCN achieves concentrated H2O2 of 20 mmol L-1, two orders of magnitude higher than photocatalysis by the same catalyst. Plasma diagnostics, isotope labeling, and COMSOL simulations collectively validate that the interplay of solution plasma and the CCN accounts for the significantly increased production of singlet oxygen and H2O2 thereafter. Our findings offer an efficient and sustainable pathway for H2O2 production, promising wide-ranging applications across the chemical industry, public health, and environmental remediation.

Optimization of Carbon-Defect Engineering to Boost Catalytic Ozonation Efficiency of Single Fe─N4 Coordination Motif

Carbon-defect engineering in single-atom metal-nitrogen-carbon (M─N─C) catalysts by straightforward and robust strategy, enhancing their catalytic activity for volatile organic compounds, and uncovering the carbon vacancy-catalytic activity relationship are meaningful but challenging. In this study, an iron-nitrogen-carbon (Fe─N─C) catalyst is intentionally designed through a carbon-thermal-diffusion strategy, exposing extensively the carbon-defective Fe─N4 sites within a micro-mesoporous carbon matrix. The optimization of Fe─N4 sites results in exceptional catalytic ozonation efficiency, surpassing that of intact Fe─N4 sites and commercial MnO2 by 10 and 312 times, respectively. Theoretical calculations and experimental data demonstrated that carbon-defect engineering induces selective cleavage of C─N bond neighboring the Fe─N4 motif. This induces an increase in non-uniform charges and Fermi density, leading to elevated energy levels at the center of Fe d-band. Compared to the intact atomic configuration, carbon-defective Fe─N4 site is more activated to strengthen the interaction with O3 and weaken the O─O bond, thereby reducing the barriers for highly active surface atomic oxygen (*O/*OO), ultimately achieving efficient oxidation of CH3SH and its intermediates. This research not only offers a viable approach to enhance the catalytic ozonation activity of M─N─C but also advances the fundamental comprehension of how periphery carbon environment influences the characteristics and efficacy of M─N4 sites.

Synergetic Manipulation Mechanism of Single-Atom M-N4 and M-OH (M = Mn, Fe, Co, Ni) Sites for Ozone Activation: Theoretical Prediction and Experimental Verification

Carbon-based single-atom catalysts (SACs) have been gradually introduced in heterogeneous catalytic ozonation (HCO), but the interface mechanism of O3 activation on the catalyst surface is still ambiguous, especially the effect of a surface hydroxyl group (M-OH) at metal sites. Herein, we combined theoretical calculations with experimental verifications to comprehensively investigate the O3 activation mechanisms on a series of conventional SAC structures with N-doped nanocarbon substrates (MN4-NCs, where M = Mn, Fe, Co, Ni). The synergetic manipulation effect of the metal atom and M-OH on O3 activation pathways was paid particular attention. O3 tends to directly interact with the metal atom on MnN4-NC, FeN4-NC, and NiN4-NC catalysts, among which MnN4-NC has the best catalytic activity for its relatively lower activation energy barrier of O3 (0.62 eV) and more active surface-adsorbed oxygen species (Oads). On the CoN4-NC catalyst, direct interaction of O3 with the metal site is energetically infeasible, but O3 can be activated to generate Oads or HO2 species from direct or indirect participation of M-OH sites. The experimental results showed that 90.7 and 82.3% of total organic carbon (TOC) was removed within 40 min during catalytic ozonation of p-hydroxybenzoic acid with MnN4-NC and CoN4-NC catalysts, respectively. Phosphate quenching, catalyst characterization, and EPR measurement further supported the theoretical prediction. This contribution provides fundamental insights into the O3 activation mechanism on SACs, and the methods and ideals could be helpful for future studies of environmental catalysis.

Precisely constructing orbital coupling-modulated iron dinuclear site for enhanced catalytic ozonation performance

The advancement of atomically precise dinuclear heterogeneous catalysts holds great potential in achieving efficient catalytic ozonation performance and contributes to the understanding of synergy mechanisms during reaction conditions. Herein, we demonstrate a "ship-in-a-bottle and pyrolysis" strategy that utilizes Fe2(CO)9 dinuclear-cluster to precisely construct Fe2 site, consisting of two Fe1-N3 units connected by Fe-Fe bonds and firmly bonded to N-doped carbon. Systematic characterizations and theoretical modeling reveal that the Fe-Fe coordination motif markedly reduced the devotion of the antibonding state in the Fe-O bond because of the strong orbital coupling interaction of dual Fe d-d orbitals. This facilitates O-O covalent bond cleavage of O3 and enhances binding strength with reaction intermediates (atomic oxygen species; *O and *OO), thus boosting catalytic ozonation performance. As a result, Fe dinuclear site catalyst exhibits 100% ozonation efficiency for CH3SH elimination, outperforming commercial MnO2 catalysts by 1,200-fold. This research provides insights into the atomic-level structure-activity relationship of ozonation catalysts and extends the use of dinuclear catalysts in catalytic ozonation and beyond.

Benzotriazole Ultraviolet Stabilizers (BUVSs) as Potential Protein Kinase Antagonists in Rice

The ubiquitous occurrence of benzotriazole ultraviolet stabilizers (BUVSs) in the environment and organisms has warned of their potential ecological and health risks. Studies showed that some BUVSs exerted immune and chronic toxicities to animals by disturbing signaling transduction, yet limited research has investigated the toxic effects on crop plants and the underlying mechanisms of signaling regulation. Herein, a laboratory-controlled hydroponic experiment was conducted on rice to explore the phytotoxicity of BUVSs by integrating conventional biochemical experiments, transcriptomic analysis, competitive sorption assays, and computational studies. The results showed that BUVSs inhibited the growth of rice by 6.30-20.4% by excessively opening the leaf stomas, resulting in increased transpiration. BUVSs interrupted the transduction of abscisic acid (ABA) signal through competitively binding to Ca2+-dependent protein kinase (CDPK), weakening the CDPK phosphorylation and further inhibiting the downstream signaling. As structural analogues of ATP, BUVSs acted as potential ABA signaling antagonists, leading to physiological dysfunction in mediating stomatal closure under stresses. This is the first comprehensive study elucidating the effects of BUVSs on the function of key proteins and the associated signaling transduction in plants and providing insightful information for the risk evaluation and control of BUVSs.

Differences in the degradation behavior of disinfection by-products in UV/PDS and UV/H2O2 processes and the effect of their chemical properties

In this work, sixteen typical chlorinated and brominated aromatic disinfection by-products (DBPs) were selected as examples to investigate their different degradation mechanisms initiated by HO• and SO4•-. Addition reactions were the main mode of degradation of DBPs by HO•, while SO4•- dominated H-abstraction reactions and single electron transfer reactions. Chlorinated compounds had higher reactivity than brominated compounds. Furthermore, substituents with stronger electron-donating effects promoted the electrophilic reaction of DBPs with the two radicals. In addition, we developed a model based on the chemical properties LUMO, fmax-, and hardness for predicting the average reaction energy barriers for the initial reactions of DBPs with HO• and SO4•-. The model had good predictive performance for the difficulty of degradation of different DPBs by HO• and SO4•-, with R2 values of 0.85 and 0.87, respectively. Through the degradation efficiency simulation, we found that longer reaction times, higher oxidant concentrations and lower pollutant concentrations were more favorable for the removal of DBPs. The UV/PDS process showed better degradation of DBPs than the UV/H2O2 process. In addition, most degradation products of DBPs exhibited less toxicity to aquatic organisms than their parent compounds. This study provided theoretical guidance for the degradation and removal of other aromatic DBPs at the molecular level.

Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation

More than 7000 per- and polyfluorinated alkyl substances (PFAS) have been documented in the U.S. Environmental Protection Agency's CompTox Chemicals database. These PFAS can be used in a broad range of industrial and consumer applications but may pose potential environmental issues and health risks. However, little is known about emerging PFAS bioaccumulation to assess their chemical safety. This study focuses specifically on the large and high-quality data set of fluorochemicals from the related environmental and pharmaceutical chemicals databases, and machine learning (ML) models were developed for the classification prediction of the unbound fraction of compounds in plasma. A comprehensive evaluation of the ML models shows that the best blending model yields an accuracy of 0.901 for the test set. The predictions suggest that most PFAS (∼92%) have a high binding fraction in plasma. Introduction of alkaline amino groups is likely to reduce the binding affinities of PFAS with plasma proteins. Molecular dynamics simulations indicate a clear distinction between the high and low binding fractions of PFAS. These computational workflows can be used to predict the bioaccumulation of emerging PFAS and are also helpful for the molecular design of PFAS to prevent the release of high-bioaccumulation compounds into the environment.

Catalytic ozonation mechanism over M1-N3C1 active sites

The structure-activity relationship in catalytic ozonation remains unclear, hindering the understanding of activity origins. Here, we report activity trends in catalytic ozonation using a series of single-atom catalysts with well-defined M1-N3C1 (M: manganese, ferrum, cobalt, and nickel) active sites. The M1-N3C1 units induce locally polarized M - C bonds to capture ozone molecules onto M atoms and serve as electron shuttles for catalytic ozonation, exhibiting excellent catalytic activities (at least 527 times higher than commercial manganese dioxide). The combined in situ characterization and theoretical calculations reveal single metal atom-dependent catalytic activity, with surface atomic oxygen reactivity identified as a descriptor for the structure-activity relationship in catalytic ozonation. Additionally, the dissociation barrier of surface peroxide species is proposed as a descriptor for the structure-activity relationship in ozone decomposition. These findings provide guidelines for designing high-performance catalytic ozonation catalysts and enhance the atomic-level mechanistic understanding of the integral control of ozone and methyl mercaptan.

Mn-doped carbon-Al2SiO5 fibers enable catalytic ozonation for wastewater treatment: Interface modulation and mass transfer enhancement

Heterogeneous catalytic ozonation is an efficient approach to remove hazardous and refractory organic contaminants in wastewater. It is crucial to design an ozone catalyst with high catalytic activity, high mass transfer and facile separation properties. Herein, easily separable aluminosilicate (Al2SiO5) fibers were developed as carriers and after interface modulation, Mn-doped carbon-Al2SiO5 (Mn-CAS) fibrous catalysts were proposed for catalytic ozonation. The growth of carbon shells on Al2SiO5 fiber surface and the introduction of metal Mn provided abundant Lewis acid sites to catalyze ozone. The Mn-CAS fiber/O3 system exhibited superior reactivity to degrade oxalic acid with a rate constant of 0.034 min-1, which was about 19 times as high as Al2SiO5/O3. For coal gasification wastewater treatment, Mn-CAS fibers also demonstrated high catalytic activity and stability and the COD removal was over 56%. Computational fluid dynamic simulations proved the high mass transfer properties of fibrous catalysts. Hydroxyl radicals (•OH) were identified as the predominant active species for organic degradation. Particularly, the catalytic pathways of O3 to •OH on Mn-O4 sites were revealed by theoretical calculations. This work provides a novel fibrous catalyst with high reactivity and mass transfer as well as easy separation characteristics for catalytic ozonation and wastewater purification.

Machine learning strategy on activation energy of environmental heterogeneous reactions and its application to atmospheric formation of typical montmorillonite-bound phenoxy radicals

Heterogeneous transformation of organic pollutants into more toxic chemicals poses substantial health risks to humans. Activation energy is an important indicator that help us to understand transformation efficacy of environmental interfacial reactions. However, the determination of activation energies for large numbers of pollutants using either the experimental or high-accuracy theoretical methods is expensive and time-consuming. Alternatively, the machine learning (ML) method shows the strength in predictive performance. In this study, using the formation of a typical montmorillonite-bound phenoxy radical as an example, a generalized ML framework RAPID was proposed for activation energy prediction of environmental interfacial reactions. Accordingly, an explainable ML model was developed to predict the activation energy via easily accessible properties of the cations and organics. The model developed by decision tree (DT) performed best with the lowest root-mean-squared error (RMSE = 0.22) and the highest coefficient of determination values (R2 score = 0.93), the underlying logic of which was well understood by combining model visualization and SHapley Additive exPlanations (SHAP) analysis. The performance and interpretability of the established model suggest that activation energies can be predicted by the well-designed ML strategy, and this would allow us to predict more heterogeneous transformation reactions in the environmental field.

Accelerated Catalytic Ozonation in a Mesoporous Carbon-Supported Atomic Fe-N4 Sites Nanoreactor: Confinement Effect and Resistance to Poisoning

The design of a micro-/nanoreactor is of great significance for catalytic ozonation, which can achieve effective mass transfer and expose powerful reaction species. Herein, the mesoporous carbon with atomic Fe-N4 sites embedded in the ordered carbon nanochannels (Fe-N4/CMK-3) was synthesized by the hard-template method. Fe-N4/CMK-3 can be employed as nanoreactors with preferred electronic and geometric catalytic microenvironments for the internal catalytic ozonation of CH3SH. During the CH3SH oxidation process, the mass transfer coefficient of the Fe-N4/CMK-3 confined system with sufficient O3 transfer featured a level of at least 1.87 × 10-5, which is 34.6 times that of the Fe-N4/C-Si unconfined system. Detailed experimental studies and theoretical calculations demonstrated that the anchored atomic Fe-N4 sites and nanoconfinement effects regulated the local electronic structure of the catalyst and promoted the activation of O3 molecules to produce atomic oxygen species (AOS) and reactive oxygen species (ROS), eventually achieving efficient oxidation of CH3SH into CO2/SO42-. Benefiting from the high diffusion rate and the augmentation of AOS/ROS, Fe-N4/CMK-3 exhibited an excellent poisoning tolerance, along with high catalytic durability. This contribution provides the proof-of-concept strategy for accelerating catalytic ozonation of sulfur-containing volatile organic compounds (VOCs) by combining confined catalysis and atomic catalysts and can be extended to the purification of other gaseous pollutants.

Single-Atom Fe-N4 Sites for Catalytic Ozonation to Selectively Induce a Nonradical Pathway toward Wastewater Purification

Nonradical oxidation has been determined to be a promising pathway for the degradation of organic pollutants in heterogeneous catalytic ozonation (HCO). However, the bottlenecks are the rational design of catalysts to selectively induce nonradicals and the interpretation of detailed nonradical generation mechanisms. Herein, we propose a new HCO process based on single-atom iron catalysts, in which Fe-N₄ sites anchored on the carbon skeleton exhibited outstanding catalytic ozonation activity and stability for the degradation of oxalic acid (OA) and p-hydroxybenzoic acid (pHBA) as well as the advanced treatment of a landfill leachate secondary effluent. Unlike traditional radical oxidation, nonradical pathways based on surface-adsorbed atomic oxygen (*O_ad) and singlet oxygen (¹O₂) were identified. A substrate-dependent behavior was also observed. OA was adsorbed on the catalyst surface and mainly degraded by *O_ad, while pHBA was mostly removed by O₃ and ¹O₂ in the bulk solution. Density functional theory calculations and molecular dynamics simulations revealed that one terminal oxygen atom of ozone preferred bonding with the central iron atom of Fe-N₄, subsequently inducing the cleavage of the O-O bond near the catalyst surface to produce *O_ad and ¹O₂. These findings highlight the structural design of an ozone catalyst and an atomic-level understanding of the nonradical HCO process.

Catalytic ozonation for imazapic degradation over kelp-derived biochar: Promotional role of N- and S-based active sites

It is a feasible strategy to prepare reliable biochar catalysts for heterogeneous catalytic ozonation (HCO) processes by using inexpensive, high quality, and easily available raw materials. Here, an environmentally friendly, simple, and green biochar catalyst rich in nitrogen (N) and sulfur (S) has been prepared by the pyrolysis of kelp. Compared with directly carbonized kelp biomass (KB), acid-activated KB (KBA) and base-activated KB (KBB) have higher specific surface areas and more extensive porous structures, although only KBB displays effective ozone activation. Imazapic (IMZC), a refractory organic herbicide, was chosen as the target pollutant, which has apparently not hitherto been investigated in the HCO process. Second-order rate constants (k) for the reactions of IMZC with three different reactive oxygen species (ROS), specifically kO3, IMZC, kOH, IMZC, and k1O2, IMZC, have been determined as 0.974, 2.48 × 109, and 6.23 × 105 M-1 s-1, respectively. The amounts of graphitic N and thiophene S derived from the intrinsic N and S showed good correlations with the IMZC degradation rate, implicating them as the main active sites. OH and O2- and 1O2 were identified as main ROS in heterogeneous catalytic ozonation system for IMZC degradation. This study exemplified the utilization of endogenous N and S in biological carbon, and provided more options for the application of advanced oxidation processes and the development of marine resources.