Sequential bio-treatment of ammonia-rich wastewater from Chinese medicine residue utilization: Regulation of dissolved oxygen.

To effectively treat actual ammonia-rich Chinese medicine residue (CMR) resource utilization wastewater, we optimized an anaerobic-microaerobic two-stage expanded granular sludge bed (EGSB) and moving bed sequencing batch reactor (MBSBR) combined process. By controlling dissolved oxygen (DO) levels, impressive removal efficiencies were achieved. Microaeration, contrasting with anaerobic conditions, bolstered dehydrogenase activity, enhanced electron transfer, and enriched the functional microorganism community. The increased relative abundance of Synergistetes and Proteobacteria facilitated hydrolytic acidification and fostered nitrogen and phosphorus removal. Furthermore, we examined the impact of DO concentration in MBSBR on pollutant removal and microbial metabolic activity, pinpointing 2.5 mg/L as the optimal DO concentration for superior removal performance and energy conservation.

Feedback of chain elongation microorganisms on iron-based conductive materials: Enhanced microbial functions and biotoxicity adaptation mechanisms.

Despite the increased research efforts aimed at understanding iron-based conductive materials (CMs) for facilitating chain elongation (CE) to produce medium chain fatty acids (MCFAs), the impact of these materials on microbial community functions and the adaptation mechanisms to their biotoxicity remain unclear. This study found that the supply of zero-valent iron (ZVI) and magnetite enhanced the MCFAs carbon-flow distribution by 26 % and 52 %, respectively. Metagenomic analysis revealed the upregulation of fatty acid metabolism, pyruvate metabolism and ABC transporters with ZVI and magnetite. The predominant functional microorganisms were Massilibacterium and Tidjanibacter with ZVI, and were Petrimonas and Candidatus_Microthrix with magnetite. Furthermore, it was demonstrated that CE microorganisms respond and adapt to the biotoxicity of iron-based CMs by adjusting Two-component system and Quorum sensing for the first time. In summary, this study provided a new deep-insight on the feedback mechanisms of CE microorganisms on iron-based CMs.

Upgrading soybean dreg to caproate via intermediate of lactate and mediator of biochar.

To address the environmental hazards posed by high-yield soybean dreg (SD), a high-value strategy is firstly proposed by synthesizing caproate through chain elongation (CE). Optimized conditions for lactate-rich broth as intermediate, utilizing 50 % inoculum ratio, 40 g/L substrate concentration, and pH 5, resulting in 2.05 g/L caproate from direct fermentation. Leveraging lactate-rich broth supplemented with ethanol, caproate was optimized to 2.76 g/L under a refined electron donor to acceptor of 2:1. Furthermore, incorporating 20 g/L biochar elevated caproate production to 3.05 g/L and significantly shortened the lag phase. Mechanistic insights revealed that biochar's surface-existed quinone and hydroquinone groups exhibit potent redox characteristics, thereby facilitating electron transfer. Moreover, biochar up-regulated the abundance of key genes involved in CE process (especially fatty acids biosynthesis pathway), also enriching Lysinibacillus and Pseudomonas as an unrecognized cooperation to CE. This study paves a way for sustainable development of SD by upgrading to caproate.

Enhancing simultaneous nitrogen and phosphorus availability through biochar addition during Chinese medicinal herbal residues composting: Synergism of microbes and humus.

The disposal of Chinese medicinal herbal residues (CMHRs) derived from Chinese medicine extraction poses a significant environmental challenge. Aerobic composting presents a sustainable treatment method, yet optimizing nutrient conversion remains a critical concern. This study investigated the effect and mechanism of biochar addition on nitrogen and phosphorus transformation to enhance the efficacy and quality of compost products. The findings reveal that incorporating biochar considerably enhanced the process of nutrient conversion. Specifically, biochar addition promoted the retention of bioavailable organic nitrogen and reduced nitrogen loss by 28.1 %. Meanwhile, adding biochar inhibited the conversion of available phosphorus to non-available phosphorus while enhancing its conversion to moderately available phosphorus, thereby preserving phosphorus availability post-composting. Furthermore, the inclusion of biochar altered microbial community structure and fostered organic matter retention and humus formation, ultimately affecting the modification of nitrogen and phosphorus forms. Structural equation modeling revealed that microbial community had a more pronounced impact on bioavailable organic nitrogen, while humic acid exerted a more significant effect on phosphorus availability. This research provides a viable approach and foundation for regulating the levels of nitrogen and phosphorus nutrients during composting, serving as a valuable reference for the development of sustainable utilization technologies pertaining to CMHRs.

How hetero-single-atom dispersion reconstructed electronic structure of carbon materials and regulated Fenton-like oxidation pathways.

Single-atom catalysts (SACs) have emerged as competitive candidates for Fenton-like oxidation of micro-pollutants in water. However, the impact of metal insertion on the intrinsic catalytic activity of carrier materials has been commonly overlooked, and the environmental risk due to metal leaching still requires attention. In contrast to previous reports, where metal sites were conventionally considered as catalytic centers, our study investigates, for the first time, the crucial catalytic role of the carbon carrier modulated through hetero-single-atom dispersion and the regulation of Fenton-like oxidation pathways. The inherent differences in electronic properties between Fe and Co can effectively trigger long-range electron rearrangement in the sp2-carbon-conjugated structure, creating more electron-rich regions for peroxymonosulfate (PMS) complexation and initiating the electron transfer process (ETP) for pollutant degradation, which imparts the synthesized catalyst (FeCo-NCB) with exceptional catalytic efficiency despite its relatively low metal content. Moreover, the FeCo-NCB/PMS system exhibits enduring decontamination efficiency in complex water matrices, satisfactory catalytic stability, and low metal leaching, signifying promising practical applications. More impressively, the spatial relationship between metal sites and electron density clouds is revealed to determine whether high-valent metal-oxo species (HVMO) are involved during the decomposition of surface complexes. Unlike single-type single-atom dispersion, where metal sites are situated within electron-rich regions, hetero-single-atom dispersion can cause the deviation of electron density clouds from the metal sites, thus hindering the in-situ oxidation of metal within the complexes and minimizing the contribution of HVMO. These findings provide new insights into the development of carbon-based SACs and advance the understanding of nonradical mechanisms underpinning Fenton-like treatments.

MCMC algorithm based on Markov random field in image segmentation.

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.

Enhanced Oxidation of Organic Compounds by the Ferrihydrite-Ferrate System: The Role of Intramolecular Electron Transfer and Intermediate Iron Species.

Previous studies mostly held that the oxidation capacity of ferrate depends on the involvement of intermediate iron species (i.e., FeIV/FeV), however, the potential role of the metastable complex was disregarded in ferrate-based heterogeneous catalytic oxidation processes. Herein, we reported a complexation-mediated electron transfer mechanism in the ferrihydrite-ferrate system toward sulfamethoxazole (SMX) degradation. A synergy between intermediate FeIV/FeV oxidation and the intramolecular electron transfer step was proposed. Specifically, the conversion of phenyl methyl sulfoxide (PMSO) to methyl phenyl sulfone (PMSO2) suggested that FeIV/FeV was involved in the oxidation of SMX. Moreover, based on the in situ Raman test and chronopotentiometry analysis, the formation of the metastable complex of ferrihydrite/ferrate was found, which possesses higher oxidation potential than free ferrate and could achieve the preliminary oxidation of organics via the electron transfer step. In addition, the amino group of SMX could complex with ferrate, and the resulting metastable complex of ferrihydrite/ferrate would combine further with SMX molecules, leading to intramolecular electron transfer and SMX degradation. The ferrate loss experiments suggested that ferrihydrite could accelerate the decomposition of ferrate. Finally, the effects of pH value, anions, humic acid, and actual water on the degradation of SMX by ferrihydrite-ferrate were also revealed. Overall, ferrihydrite demonstrated high catalytic capacity, good reusability, and nontoxic performance for ferrate activation. The ferrihydrite-ferrate process may be a green and promising method for organic removal in wastewater treatment.

Attenuation effects of ZVI/PDS pretreatment on propagation of antibiotic resistance genes in bioreactors: Driven by antibiotic residues and sulfate assimilation.

Sulfate radical-based advanced oxidation processes (AOPs) combined biological system was a promising technology for treating antibiotic wastewater. However, how pretreatment influence antibiotic resistance genes (ARGs) propagation remains largely elusive, especially the produced by-products (antibiotic residues and sulfate) are often ignored. Herein, we investigated the effects of zero valent iron/persulfate pretreatment on ARGs in bioreactors treating sulfadiazine wastewater. Results showed absolute and relative abundance of ARGs reduced by 59.8%- 81.9% and 9.1%- 52.9% after pretreatments. The effect of 90-min pretreatment was better than that of the 30-min. The ARGs reduction was due to decreased antibiotic residues and stimulated sulfate assimilation. Reduced antibiotic residues was a major factor in ARGs attenuation, which could suppress oxidative stress, inhibit mobile genetic elements emergence and resistant strains proliferation. The presence of sulfate in influent supplemented microbial sulfur sources and facilitated the in-situ synthesis of antioxidant cysteine through sulfate assimilation, which drove ARGs attenuation by alleviating oxidative stress. This is the first detailed analysis about the regulatory mechanism of how sulfate radical-based AOPs mediate in ARGs attenuation, which is expected to provide theoretical basis for solving concerns about by-products and developing practical methods to hinder ARGs propagation.

Light and nitrogen vacancy-intensified nonradical oxidation of organic contaminant with Mn (III) doped carbon nitride in peroxymonosulfate activation.

Recently, Mn-based materials have a great potential for selective removal of organic contaminants with the assistance of oxidants (PMS, H2O2) and the direct oxidation. However, the rapid oxidation of organic pollutants by Mn-based materials in PMS activation still presents a challenge due to the lower conversion of surface Mn (III)/Mn (IV) and higher reactive energy barrier for reactive intermediates. Here, we constructed Mn (III) and nitrogen vacancies (Nv) modified graphite carbon nitride (MNCN) to break these aforementioned limitations. Through analysis of in-situ spectra and various experiments, a novel mechanism of light-assistance non-radical reaction is clearly elucidated in MNCN/PMS-Light system. Adequate results indicate that Mn (III) only provide a few electrons to decompose Mn (III)-PMS* complex under light irradiation. Thus, the lacking electrons necessarily are supplied from BPA, resulting in its greater removal, then the decomposition of the Mn (III)-PMS* complex and light synergism form the surface Mn (IV) species. Above Mn-PMS complex and surface Mn (IV) species lead to the BPA oxidation in MNCN/PMS-Light system without the involvement of sulfate (SO4• ̶) and hydroxyl radicals (•OH). The study provides a new understanding for accelerating non-radical reaction in light/PMS system for the selective removal of contaminant.

Application of machine learning and deep learning methods for hydrated electron rate constant prediction.

Accurately determining the second-order rate constant with eaq- (keaq-) for organic compounds (OCs) is crucial in the eaq- induced advanced reduction processes (ARPs). In this study, we collected 867 keaq- values at different pHs from peer-reviewed publications and applied machine learning (ML) algorithm-XGBoost and deep learning (DL) algorithm-convolutional neural network (CNN) to predict keaq-. Our results demonstrated that the CNN model with transfer learning and data augmentation (CNN-TL&DA) greatly improved the prediction results and overcame over-fitting. Furthermore, we compared the ML/DL modeling methods and found that the CNN-TL&DA, which combined molecular images (MI), achieved the best overall performance (R2test = 0.896, RMSEtest = 0.362, MAEtest = 0.261) when compared to the XGBoost algorithm combined with Mordred descriptors (MD) (0.692, RMSEtest = 0.622, MAEtest = 0.399) and Morgan fingerprint (MF) (R2test = 0.512, RMSEtest = 0.783, MAEtest = 0.520). Moreover, the interpretation of the MD-XGBoost and MF-XGBoost models using the SHAP method revealed the significance of MDs (e.g., molecular size, branching, electron distribution, polarizability, and bond types), MFs (e.g, aromatic carbon, carbonyl oxygen, nitrogen, and halogen) and environmental conditions (e.g., pH) that effectively influence the keaq- prediction. The interpretation of the 2D molecular image-CNN (MI-CNN) models using the Grad-CAM method showed that they correctly identified key functional groups such as -CN, -NO2, and -X functional groups that can increase the keaq- values. Additionally, almost all electron-withdrawing groups and a small part of electron-donating groups for the MI-CNN model can be highlighted for estimating keaq-. Overall, our results suggest that the CNN approach has smaller errors when compared to ML algorithms, making it a promising candidate for predicting other rate constants.

Insight into Chinese medicine residue biochar combined with ultrasound for persulfate activation in atrazine degradation: Acanthopanax senticosus precursors, synergistic effects and toxicity assessment.

The synergistic activation of persulfate by multiple factors could degrade pollutants more efficiently. However, the co-activation method based on metal ions has the risk of leakage. The non-metallic coupling method could achieve the same efficiency as the metal activation and meanwhile release environmental stress. In this study, the original biochar (BC) was prepared through using Chinese medicinal residue of Acanthopanax senticosus as the precursor. Compared with other biochar, the pore size structure was higher and toxicity risk was lower. The ultrasonic (US)/Acanthopanax senticosus biochar (ASBC)/persulfate oxidation system was established for Atrazine (ATZ). Results showed that 45KHz in middle and low frequency band cooperated with ASBC600 to degrade nearly 70 % of ATZ within 50 min, and US promoted the formation of SO4- and OH. Meanwhile, the synergy index of US and ASBC was calculated to be 1.18, which showed positive synergistic effect. Finally, the potential toxicity was examined by using Toxicity Characteristic Leaching Procedure (TCLP) and luminescent bacteria. This study provides a promising way for the activation of persulfate, which is expected to bring a new idea for the win-win situation of pollutant degradation and solid waste resource utilization.

Machine learning approaches to predict the apparent rate constants for aqueous organic compounds by ferrate.

The apparent second-order rate constant with hexavalent ferrate (Fe(VI)) (kFe(VI)) is a key indicator to evaluate the removal efficiency of a molecule by Fe(VI) oxidation. kFe(VI) is often determined by experiment, but such measurements can hardly catch up with the rapid growth of organic compounds (OCs). To address this issue, in this study, a total of 437 experimental second-order kFe(VI) rate constants at a range of conditions (pH and temperature) were used to train four machine learning (ML) algorithms (lasso regression (LR), ridge regression (RR), extreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM)). Using the Morgan fingerprint (MF)) of a range of organic compounds (OCs) as the input, the performance of the four algorithms was comprehensively compared with respect to the coefficient of determination (R2) and root-mean-square error (RMSE). It is shown that the RR, XGBoost, and LightGBM models displayed generally acceptable performance kFe(VI) (R2test > 0.7). In addition, the shapely additive explanation (SHAP) and feature importance methods were employed to interpret the XGBoost/LightGBM and RR models, respectively. The results showed that the XGBoost/LightGBM and RR models suggestd pH as the most important predictor and the tree-based models elucidate how electron-donating and electron-withdrawing groups influence the reactivity of the Fe(VI) species. In addition, the RR model share eight common features, including pH, with the two tree-based models. This work provides a fast and acceptable method for predicting kFe(VI) values and can help researchers better understand the degradation behavior of OCs by Fe(VI) oxidation from the perspective of molecular structure.

Automatic control and optimal operation for greenhouse gas mitigation in sustainable wastewater treatment plants: A review.

In order to promote low-carbon sustainable operational management of the wastewater treatment plants (WWTPs), automatic control and optimal operation technologies, which devote to improving effluent quality, operational costs and greenhouse gas (GHG) emissions, have flourished in recent years. There is no consensus on the design procedure for optimal control/operation of sustainable WWTPs. In this review, we summarize recent researches on developing control and optimization strategies for GHG mitigation in WWTPs. Faced with the fact that direct carbon dioxide (CO2) emissions (considered biological origin) are generally not included in the carbon footprint of WWTPs, direct emissions (nitrous oxide (N2O), methane (CH4)) and indirect emissions are paid much attention. Firstly, the plant-wide models with GHG dynamic simulation, which are employed to design and evaluate the automatic control schemes as well as representative studies on identifying key factors affecting GHG emissions or comprehensive performance are outlined. Then, both traditional and advanced control methods commonly used in GHG mitigation are reviewed in detail, followed by the multi-objective optimization practices of control/operational parameters. Based on the mentioned control and (or) optimization strategies, a novel design framework for the optimal control/operation of sustainable WWTPs is proposed. The findings and design framework proposed in the paper will provide guidance for GHG mitigation and sustainable operation in WWTPs. It is foreseeable that more accurate and appropriate plant-wide models together with flexible control methods and intelligent optimization strategies will be developed to satisfy the upgrading requirements of WWTPs in the future.

Dissecting the roles of conductive materials in attenuating antibiotic resistance genes: Evolution of physiological features and bacterial community.

Supplying conductive materials (CMs) into anaerobic bioreactors is considered as a promising technology for antibiotic wastewater treatment. However, whether and how CMs influence antibiotic resistance genes (ARGs) spread remains poorly known. Here, we investigated the effects of three CMs, i.e., magnetite, activated carbon (AC), and zero valent iron (ZVI), on ARGs dissemination during treating sulfamethoxazole wastewater, by dissecting the shifts of physiological features and microbial community. With the addition of magnetite, AC, and ZVI, the SMX removal was improved from 49.05 to 71.56-92.27 %, while the absolute abundance of ARGs reducing by 26.48 %, 61.95 %, 48.45 %, respectively. The reduced mobile genetic elements and antibiotic resistant bacteria suggested the inhibition of horizontal and vertical transfer of ARGs. The physiological features, including oxidative stress response, quorum sensing, and secretion system may regulate horizontal transfer of ARGs. The addition of all CMs relieved oxidative stress compared with no CMs, but ZVI may cause additional free radicals that needs to be concerned. Further, ZVI and AC also interfered with cell communication and secretion system. This research deepens the insights about the underlying mechanisms of CMs in regulating ARGs, and is expected to propose practical ways for mitigating ARGs proliferation.

Insights into removal of sulfonamides in anaerobic activated sludge system: Mechanisms, degradation pathways and stress responses.

The fate of antibiotics in activated sludge has attracted increasing interests. However, the focus needs to shift from concerning removal efficiencies to understanding mechanisms and sludge responding to antibiotic toxicity. Herein, we operated two anaerobic sequencing batch reactors (ASBRs) for 200 days with sulfadiazine (SDZ) and sulfamethoxazole (SMX) added. The removal efficiency of SMX was higher than that of SDZ. SDZ was removed via adsorption (9.91-21.18%) and biodegradation (10.20-16.00%), while biodegradation (65.44-86.26%) was dominant for SMX removal. The mechanisms involved in adsorption and biodegradation were investigated, including adsorption strength, adsorption sites and the roles of enzymes. Protein-like substance (tryptophan) functioned vitally in adsorption by forming complexes with sulfonamides. P450 enzymes may catalyze sulfonamides degradation via hydroxylation and desulfurization. Activated sludge showed distinct responses to different sulfonamides, reflected in the changes of microbial communities and functions. These responses were related to sulfonamides removal, corresponding to the stronger adsorption capacity of activated sludge in ASBR-SDZ and degradation capacity in ASBR-SMX. Furthermore, the reasons for different removal efficiencies of sulfonamides were analyzed according to steric and electronic effects. These findings propose insights into antibiotic removal and broaden the knowledge for self-protection mechanisms of activated sludge under chronic toxicities of antibiotics.

Deciphering the transfers of antibiotic resistance genes under antibiotic exposure conditions: Driven by functional modules and bacterial community.

Antibiotics can exert selective pressures on sludge as well as affect the emergence and spread of antibiotic resistance genes (ARGs). However, the underlying mechanisms of ARGs transfers are still controversial and not fully understood in sludge system. In present study, two anaerobic sequence batch reactors (ASBR) were constructed to investigate the development of ARGs exposed to two sulfonamide antibiotics (SMs, sulfadiazine SDZ and sulfamethoxazole SMX) with increasing concentrations. The abundance of corresponding ARGs and total ARGs obviously increased with presence of SMs. Functional analyses indicated that oxidative stress response, signal transduction and type IV secretion systems were triggered by SMs, which would promote ARGs transfers. Network analysis revealed 18 genera were possible hosts of ARGs, and their abundances increased with SMs. Partial least-squares path modeling suggested functional modules directly influenced mobile genetic elements (MGEs) as well as the ARGs might be driven by both functional modules and bacteria community, while bacteria community composition played a more key role. Sludge with refractory antibiotics (SDZ) may stimulate the relevant functions and shift the microbial composition to a greater extent, causing more ARGs to emerge and spread. The mechanisms of ARGs transfers are revealed from the perspective of functional modules and bacterial community in sludge system for the first time, and it could provide beneficial directions, such as oxidative stress reduction, cellular communication control, bacterial composition directional regulation, for ARGs spread controlling in the future.

Novel Nonradical Oxidation of Sulfonamide Antibiotics with Co(II)-Doped g-C3N4-Activated Peracetic Acid: Role of High-Valent Cobalt-Oxo Species.

Herein, we report that Co(II)-doped g-C3N4 can efficiently trigger peracetic acid (PAA) oxidation of various sulfonamides (SAs) in a wide pH range. Quite different from the traditional radical-generating or typical nonradical-involved (i.e., singlet oxygenation and mediated electron transfer) catalytic systems, the PAA activation follows a novel nonradical pathway with unprecedented high-valent cobalt-oxo species [Co(IV)] as the dominant reactive species. Our experiments and density functional theory calculations indicate that the Co atom fixated into the nitrogen pots of g-C3N4 serves as the main active site, enabling dissociation of the adsorbed PAA and conversion of the coordinated Co(II) to Co(IV) via a unique two-electron transfer mechanism. Considering Co(IV) to be highly electrophilic in nature, different substituents (i.e., five-membered and six-membered heterocyclic moieties) on the SAs could affect their nucleophilicity, thus leading to the differences in degradation efficiency and transformation pathway. Also, benefiting from the selective oxidation of Co(IV), the established oxidative system exhibits excellent anti-interference capacity and achieves satisfactory decontamination performance under actual water conditions. This study provides a new nonradical approach to degrade SAs by efficiently activating PAA via heterogeneous cobalt-complexed catalysts.

Bio-CQDs surface modification BiOCl for the BPA elimination and evaluation in visible light: The contribution of C-localized level.

Carbon quantum dots (CQDs) doping semiconductors can boost solar-to-hydrogen conversion and the photodegradation in VIS-NIR light, therefore attract great attention, but the perspective of CQDs role is seldom explored. Here, a biomass-CQDs was assembled with BiOCl (CQDs/BiOCl), then served as the visible-photodegradation model for a mechanistic investigation. Furthermore, CQDs/BiOCl removed 90% bisphenol A (BPA) within 2 h under visible light. It was attributed to the C-localized state (CLS) produced by CQDs, which transfers forceless photo-electrons (e-) to generate holes (h+) in the CQDs/BiOCl valence band (VB) under visible light, the h+ mainly involved in the BPA degradation process. Then, the electrochemical experiments and theoretical calculations further proved that the efficiencies of charge separation (ηCS) and injection (ηCI) were proved by CQDs. Meanwhile, the possible BPA degradation pathways were accordingly proposed, and the ecotoxicity evaluation of the intermediates was also conducted by ECOSAR. The transformation pathways of BPA were divided into five orientations, and the toxicity of intermediates was decreased for Fish (LC50, ChV), Daphnid (LC50, ChV), Algae (EC50, ChV) except for P10 and P12. As the result, this study confirmed the feasibility of bio-CQDs/BiOCl preparation and it could be a photocatalyst to remove and detoxify BPA under visible light.

Insights into the oxidation of organic contaminants by Co(II) activated peracetic acid: The overlooked role of high-valent cobalt-oxo species.

The combination of Co(II) and peracetic acid (PAA) is a promising advanced oxidation process for the abatement of refractory organic contaminants, and acetylperoxy (CH3CO3•) and acetoxyl (CH3CO2•) radicals are generally recognized as the dominant and selective intermediate oxidants. However, the role of high-valent cobalt-oxo species [Co(IV)] have been overlooked. Herein, we confirmed that Co(II)/PAA reaction enables the generation of Co(IV) at acidic conditions based on multiple lines of evidences, including methyl phenyl sulfoxide (PMSO)-based probe experiments, 18O isotope-labeling technique, and in situ Raman spectroscopy. In-depth investigation reveals that the PAA oxidation mechanism is strongly pH dependent. The elevation of solution pH could induce major oxidants converting from Co(IV) to oxygen-centered radicals (i.e., CH3CO3• and CH3CO2•). The presence of H2O2 competitively consumes both Co(IV) and reactive radicals generated from Co(II)/PAA process, and thus, leading to an undesirably decline in catalytic performance. Additionally, as a highly reactive and selective oxidant, Co(IV) reacts readily with organic substances bearing electron-rich groups, and efficiently attenuating their biological toxicity. Our findings enrich the fundamental understanding of Co(II) and PAA reaction and will be useful for the application of Co(IV)-mediated processes.

Multipath elimination of bisphenol A over bifunctional polymeric carbon nitride/biochar hybrids in the presence of persulfate and visible light.

Polymeric carbon nitride (PCN) has become a star material either in photocatalysis or in persulfate (PS) activation. In this work, we synthesized bifunctional biochar (BC)-doped PCN through a facile one-pot thermal treatment process. The PCN/BC hybrid (CNBC) with an optimized proportion could not only activate PS directly, but also possessed improved optical properties. Amorphous BC domains generated from the carbonization of external corncob provided attachments for the in-situ growth of PCN and upgraded its catalytic ability including electron transport property, visible light (VIS) utilization, and oxidation power. Mechanism studies demonstrated that in the CNBC/PS system without VIS, a nonradical electron transfer route was responsible for the degradation of bisphenol A (BPA), while in the CNBC/PS/VIS system, radical/nonradical mixing mechanisms including mediated electron transfer, radical oxidation, and hole oxidation were unveiled. Degradation pathways of BPA were deduced including direct oxidation at the aromatic ring, ß-scission of isopropyl, and ring cleavage. Most of the intermediates were less toxic than BPA as assessed by the ECOSAR software. The CNBC/PS/VIS system showed satisfactory resistance to environmental interferences except for HCO3-. This work provides a simple but effective strategy for the synthesis of PCN-based bifunctional catalysts and deepens mechanistic insights into hybrid advanced oxidation technologies.