The effectiveness and safety of acupoint herbal patching for allergic rhinitis: protocol for a systematic review and meta-analysis.

BACKGROUND: Allergic rhinitis (AR) is a common inflammatory disease of the nasal mucosa that is characterized by symptoms such as sneezing, nasal congestion, nasal itching, and rhinorrhoea. In recent years, acupoint herbal patching (AHP) therapy has gained a growing interest as a potential management option for AR. This systematic review and meta-analysis will evaluate the clinical research evidence on the effectiveness and safety of AHP as a treatment option for AR outside of the Sanfu or Sanjiu days (summer or winter solstice). The results of this review will provide up-to-date evidence-based guidance for healthcare providers and individuals seeking alternative treatments for AR. METHODS: A comprehensive search of electronic databases (PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, China National Knowledge Infrastructure (CNKI), CQVIP, Sino-Med, and Wanfang Databases) will be conducted from their inception to June 2023. The inclusion criteria will be limited to randomized controlled trials that evaluate the effectiveness or efficacy of non-Sanfu or non-Sanjiu AHP for AR. The primary outcome measure will be the total nasal symptom score. The methodological quality of included studies will be assessed using the Revised Cochrane risk-of-bias tool for randomized trials (RoB 2), and meta-analyses will be performed using RevMan (V.5.3) statistical software. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach will be used to determine the certainty of evidence. DISCUSSION: This systematic review and meta-analysis will provide valuable insights into the effectiveness and safety of non-Sanfu or non-Sanjiu AHP as a treatment option for AR. The study aims to produce a high-quality review by adhering to PRISMA-P guidelines and using clinical guideline recommended outcome measures. The results of this review may offer additional treatment options for AR patients who seek complementary and alternative therapies, and hold significant implications for future research in this field. Overall, this study has the potential to inform clinical practice and improve patient outcomes. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022181322.

Utilizing cost-effective pyrocarbon for highly efficient gold retrieval from e-waste leachate.

Addressing burdens of electronic waste (E-waste) leachate while achieving sustainable and selective recovery of noble metals, such as gold, is highly demanded due to its limited supply and escalating prices. Here we demonstrate an environmentally-benign and practical approach for gold recovery from E-waste leachate using alginate-derived pyrocarbon sorbent. The sorbent demonstrates potent gold recovery performance compared to most previously reported advanced sorbents, showcasing high recovery capacity of 2829.7 mg g-1, high efficiency (>99.5%), remarkable selectivity (Kd ~ 3.1 × 108 mL g-1), and robust anti-interference capabilities within environmentally relevant contexts. The aromatic structures of pyrocarbon serve as crucial electrons sources, enabling a hydroxylation process that simultaneously generates electrons and phenolic hydroxyls for the reduction of gold ions. Our investigations further uncover a "stepwise" nucleation mechanism, in which gold ions are reduced as intermediate gold-chlorine clusters, facilitating rapid reduction process by lowering energy barriers from 1.08 to -21.84 eV. Technoeconomic analysis demonstrates its economic viability with an input-output ratio as high as 1370%. Our protocol obviates the necessity for organic reagents whilst obtaining 23.96 karats gold product from real-world central processing units (CPUs) leachates. This work introduces a green sorption technique for gold recovery, emphasizing its role in promoting a circular economy and environmental sustainability.

Experimental Verification of Erchen Decoction Plus Huiyanzhuyu Decoction in the Treatment of Laryngeal Squamous Cell Carcinoma Based on Network Pharmacology.

BACKGROUND: The prescription of Chinese herbal medicine (CHM) consists of multiple herbs that exhibit synergistic effects due to the presence of multiple components targeting various pathways. In clinical practice, the combination of Erchen decoction and Huiyanzhuyu decoction (EHD) has shown promising outcomes in treating patients with laryngeal squamous cell carcinoma (LSCC). However, the underlying mechanism by which EHD exerts its therapeutic effects in LSCC remains unknown. METHODS: Online databases were utilized for the analysis and prediction of the active constituents, targets, and key pathways associated with EHD in the treatment of LSCC. The protein-protein interaction (PPI) network of common targets was constructed and visualized using Cytoscape 3.8.1 software. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the functional roles of core targets within the PPI network. Protein clustering was conducted utilizing the MCODE plug-in. The obtained results highlight the principal targets and pathways involved. Subsequently, clinical samples were collected to validate alterations in the levels of these main targets through Western blotting (WB) and immunohistochemistry (IHC). Furthermore, both in vivo and in vitro experiments were conducted to investigate the therapeutic effects of EHD on healing LSCC and elucidate its underlying mechanism. Additionally, to ensure experimental reliability and reproducibility, quality control measures utilizing HPLC were implemented for EHD herbal medicine. RESULTS: The retrieval and analysis of databases in EHD medicine and LSCC disease yielded a total of 116 overlapping targets. The MCODE plug-in methods were utilized to acquire 8 distinct protein clusters through protein clustering. The findings indicated that both the first and second clusters exhibited a size greater than 6 scores, with key genes PI3K and ErbB occupying central positions, while the third and fourth clusters were associated with proteins in the PI3K, STAT3, and Foxo pathways. GO functional analysis reported that these targets had associations mainly with the pathway of p53 mediated DNA damage and negative regulation of cell cycle in terms of biological function; the death-induced signaling complex in terms of cell function; transcription factor binding and protein kinase activity in terms of molecular function. The KEGG enrichment analysis demonstrated that these targets were correlated with several signaling pathways, including PI3K-Akt, FoxO, and ErbB2 signaling pathway. On one hand, we observed higher levels of key genes such as P-STAT3, P-PDK1, P-Akt, PI3K, and ErbB2 in LSCC tumor tissues compared to adjacent tissues. Conversely, FOXO3a expression was lower in LSCC tumor tissues. On the other hand, the key genes mentioned above were also highly expressed in both LSCC xenograft nude mice tumors and LSCC cell lines, while FOXO3a was underexpressed. In LSCC xenograft nude mice models, EHD treatment resulted in downregulation of P-STAT3, P-PDK1, PI3K, P-AKT, and ErbB2 protein levels but upregulated FOXO3a protein level. EHD also affected the levels of P-STAT3, P-PDK1, PI3K, P-AKT, FOXO3a, and ErbB2 proteins in vitro: it inhibited P-STAT3, P-AKT, and ErbB2, while promoting FOXO3a; however, it had no effect on PDK1 protein. In addition, HPLC identified twelve compounds accounting for more than 30% within EHD. The findings from this study can serve as valuable guidance for future experimental investigations. CONCLUSION: The possible mechanism of EHD medicine action on LSCC disease is speculated to be closely associated with the ErbB2/PI3K/AKT/FOXO3a signaling pathway.

Predicting Odor Sensory Attributes of Unidentified Chemicals in Water Using Fragmentation Mass Spectra with Machine Learning Models.

Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterborne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odorant identification and odor evaluation. In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS2 spectra as input features. We demonstrate that model performance using MS2 spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model's robustness in predicting odor sensory attributes of unidentified chemicals by using the experimentally obtained MS2 spectra from nontarget analysis on authentic water samples. Interpreting the developed models, we identify the intricate interaction of functional groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., in the inherent olfactory mechanisms. These findings streamline the odor sensory attribute prediction and are crucial advancements toward credible tracking and efficient control of off-odors in water.

Integration of nontarget analysis with machine learning modeling for prioritization of odorous volatile organic compounds in surface water.

Assessing the odor risk caused by volatile organic compounds (VOCs) in water has been a big challenge for water quality evaluation due to the abundance of odorants in water and the inherent difficulty in obtaining the corresponding odor sensory attributes. Here, a novel odor risk assessment approach has been established, incorporating nontarget screening for odorous VOC identification and machine learning (ML) modeling for odor threshold prediction. Twenty-nine odorous VOCs were identified using two-dimensional gas chromatography-time of flight mass spectrometry from four surface water sampling sites. These identified odorants primarily fell into the categories of ketones and ethers, and originated mainly from biological production. To obtain the odor threshold of these odorants, we trained an ML model for odor threshold prediction, which displayed good performance with accuracy of 79%. Further, an odor threshold-based prioritization approach was developed to rank the identified odorants. 2-Methylisoborneol and nonanal were identified as the main odorants contributing to water odor issues at the four sampling sites. This study provides an accessible method for accurate and quick determination of key odorants in source water, aiding in odor control and improved water quality management. ENVIRONMENTAL IMPLICATION: Water odor episodes have been persistent and significant issues worldwide, posing severe challenges to water treatment plants. Unpleasant odors in aquatic environments are predominantly caused by the occurrence of a wide range of volatile organic chemicals (VOCs). Given the vast number of newly-detected VOCs, experimental identification of the key odorants becomes difficult, making water odor issues complex to control. Herein, we propose a novel approach integrating nontarget analysis with machine learning models to accurate and quick determine the key odorants in waterbodies. We use the approach to analyze four samples with odor issues in Changsha, and prioritized the potential odorants.

Moderate oxidation of algae-laden water: Principals and challenges.

The occurrence of seasonal algae blooms represents a huge dilemma for water resource management and has garnered widespread attention. Therefore, finding methods to control algae pollution and improve water quality is urgently needed. Moderate oxidation has emerged as a feasible way of algae-laden water treatment and is an economical and prospective strategy for controlling algae and endogenous and exogenous pollutants. Despite this, a comprehensive understanding of algae-laden water treatment by moderate oxidation, particularly principles and summary of advanced strategies, as well as challenges in moderate oxidation application, is still lacking. This review outlines the properties and characterization of algae-laden water, which serve as a prerequisite for assessing the treatment efficiency of moderate oxidation. Biomass, cell viability, and organic matter are key components to assessing moderate oxidation performance. More importantly, the recent advancements in employing moderate oxidation as a treatment or pretreatment procedure were examined, and the suitability of different techniques was evaluated. Generally, moderate oxidation is more promising for improving the solid-liquid separation process by the reduction of cell surface charge (stability) and removal/degradation of the soluble algae secretions. Furthermore, this review presents an outlook on future research directions aimed at overcoming the challenges encountered by existing moderate oxidation technologies. This comprehensive examination aims to provide new and valuable insights into the moderate oxidation process.

MnFe layered double hydroxides confined MnOx for peroxymonosulfate activation: A novel manner for the selective production of singlet oxygen.

Singlet oxygen (1O2) is a reactive species for the selective degradation of stubborn organic pollutants. Given its resistance to harsh water environment, the effective and exclusive generation of 1O2 is acknowledged as a key strategy to mitigate water production costs and ensure water supply safety. Herein, we synthesized MnOx intercalated MnFe layered double hydroxides (MF-MnOx) to selectively produce 1O2 through the activation of PMS. The distinctive confined structure endowed MF-MnOx with a special pathway for the PMS activation. The direct oxidation of BPA on the intercalated MnOx induced the charge imbalance in the MnFe-LDH layer, resulting in the selective generation of 1O2. Moreover, acceptable activity deterioration of MF-MnOx was observed in a 10 h continuous degradation test in actual water, substantiating the application potential of MF-MnOx. This work presents a novel catalyst for the selective production of 1O2, and evaluates its prospects in the remediation of micro-polluted water.

Molecularly Imprinted Heterostructure-Based Electrochemosensor for Ultratrace and Precise Detection of 2-Methylisoborneol in Water.

Ultratrace 2-methylisoborneol (2-MIB, ∼ng/L) in source water is the main odorant in the algae-derived odor episodes, whose accurate on-site detection will have a promising application potential. Due to the chemical inertness of 2-MIB, sensitive and selective detection of 2-MIB remains much challenging. Herein, molecularly imprinted polymer cavities were polymerized on the heterostructure Ti3C2Tx@CuFc-metal-organic framework to selectively capture 2-MIB, where the heterostructure could catalyze the probe redox reaction of [Fe(CN)63-/4-] and amplify the corresponding current signals. The prepared electrochemical sensor showed higher sensitivity on 2-MIB detection than the reported ones. Excellent stability, reusability, and selectivity for 2-MIB detection were also verified. The linear range and limit of detection of our sensor for 2-MIB were optimized to 0.0001-100 µg/L and 30 pg/L, respectively, performing much better than the reported sensors. Comparable performance to gas chromatography-mass spectrometry was achieved when the sensor was applied to real water samples with or without 2-MIB standards. Overall, our research has made great progress in the application of an on-site sensor in 2-MIB detection and well advances the development of molecularly imprinted polymer-based electrochemical sensors.

The E3 ligase NEURL3 suppresses epithelial-mesenchymal transition and metastasis in nasopharyngeal carcinoma by promoting vimentin degradation.

BACKGROUND: Metastasis has emerged as the major reason of treatment failure and mortality in patients with nasopharyngeal carcinoma (NPC). Growing evidence links abnormal DNA methylation to the initiation and progression of NPC. However, the precise regulatory mechanism behind these processes remains poorly understood. METHODS: Bisulfite pyrosequencing, RT-qPCR, western blot, and immunohistochemistry were used to test the methylation and expression level of NEURL3 and its clinical significance. The biological function of NEURL3 was examined both in vitro and in vivo. Mass spectrometry, co-immunohistochemistry, immunofluorescence staining, and ubiquitin assays were performed to explore the regulatory mechanism of NEURL3. RESULTS: The promoter region of NEURL3, encoding an E3 ubiquitin ligase, was obviously hypermethylated, leading to its downregulated expression in NPC. Clinically, NPC patients with a low NEURL3 expression indicated an unfavorable prognosis and were prone to develop distant metastasis. Overexpression of NEURL3 could suppress the epithelial mesenchymal transition and metastasis of NPC cells in vitro and in vivo. Mechanistically, NEURL3 promoted Vimentin degradation by increasing its K48-linked polyubiquitination at lysine 97. Specifically, the restoration of Vimentin expression could fully reverse the tumor suppressive effect of NEURL3 overexpression in NPC cells. CONCLUSIONS: Collectively, our study uncovers a novel mechanism by which NEURL3 inhibits NPC metastasis, thereby providing a promising therapeutic target for NPC treatment.

Predicting reactivity dynamics of halogen species and trace organic contaminants using machine learning models.

Reactions of reactive halogen species (Cl•, Br•, and Cl2•-) with trace organic contaminants (TrOCs) have received much attention in recent years, and their k values are fundamental parameters for understanding their reaction mechanisms. However, k values are usually unknown. In this study, we developed machine learning (ML)-based quantitative structure-activity relationship (QSAR) models to predict k values. We tested five algorithms, namely, random forest, neural network, XGBoost, support vector machine (SVM), and multilinear regression, using molecular descriptors (MDs) and molecular fingerprints (MFs) as inputs. The optimal algorithms were MD-XGBoost for Cl• and Br•, and MF-SVM for Cl2•-, respectively, with R2test values of 0.876, 0.743, and 0.853. We found that electron-withdrawing/donating groups tended to interfere with the reactivity of Cl2•- more than Cl• and Br•. This explains why MFs are better inputs for predictive models of Cl2•-, whereas MDs are more suitable for Cl• and Br•. Furthermore, we interpreted the models using SHAP analysis, and the results indicated that our models accurately predicted k values both statistically and mechanistically. Our models provide useful tools for obtaining unknown k values and help researchers understand the inherent relationships between the models.

Recent advances in dynamic dual mode systems for daytime radiative cooling and solar heating.

Thermal management, including heating and cooling, plays an important role in human productive activities and daily life. Nevertheless, in the actual environment, almost all the ambient scenarios come with the challenge that the objects are located in a quite dynamic and variable environment, which includes fluctuations in aspects such as space, time, sunlight, season, and temperature. It is imperative to develop low-energy or even zero-energy thermal-management technologies with renewable and clean energy. In this review, we summarised the latest technological advances and the prospects in this burgeoning field. First, we present the fundamental principles of the daytime passive radiative cooling (PDRC) thermal management device. Next, In the domain of dual-mode systems, they are classified into various types based on the diverse mechanisms of transitioning between cooling and heating states, including electrical responsive, mechanical responsive, temperature responsive, and solution responsive. Furthermore, we conducted an in-depth analysis of the principles and design methodologies associated with these categories, followed by a comparative assessment of their performance in radiative cooling and solar heating applications. Finally, this review presents the challenges and opportunities of dynamic dual mode thermal management, while also identifying future directions.

DNAJA4 suppresses epithelial-mesenchymal transition and metastasis in nasopharyngeal carcinoma via PSMD2-mediated MYH9 degradation.

Emerging evidence indicates that DNA methylation plays an important role in the initiation and progression of nasopharyngeal carcinoma (NPC). DNAJA4 is hypermethylated in NPC, while its role in regulating NPC progression remains unclear. Here, we revealed that the promoter of DNAJA4 was hypermethylated and its expression was downregulated in NPC tissues and cells. Overexpression of DNAJA4 significantly suppressed NPC cell migration, invasion, and EMT in vitro, and markedly inhibited the inguinal lymph node metastasis and lung metastatic colonization in vivo, while it did not affect NPC cell viability and proliferation capability. Mechanistically, DNAJA4 facilitated MYH9 protein degradation via the ubiquitin-proteasome pathway by recruiting PSMD2. Furthermore, the suppressive effects of DNAJA4 on NPC cell migration, invasion, and EMT were reversed by overexpression of MYH9 in NPC cells. Clinically, a low level of DNAJA4 indicated poor prognosis and an increased probability of distant metastasis in NPC patients. Collectively, DNAJA4 serves as a crucial driver for NPC invasion and metastasis, and the DNAJA4-PSMD2-MYH9 axis might contain potential targets for NPC treatments.

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer.

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control. In this study, we develop an automatic framework of feature engineering based on variation sliding layer (VSL) to control the air demand precisely. Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand. The developed models with VSL-ML models were also successfully implemented under the full-scale wastewater treatment plant, showing a 16.12 % reduction in demand compared to conventional aeration control of preset dissolved oxygen (DO) and feedback to the blower. The VSL-ML models showed great potential to be applied for the precision air demand prediction and control. The package as a tripartite library of Python is called wwtpai, which is freely accessible on GitHub and CSDN to remove technical barriers to the application of AI technology in WWTPs.

Removal of Microcystis aeruginosa by manganese activated sodium percarbonate: Performance and role of the in-situ formed MnO2.

Previous studies have found that pre-oxidation of manganese salts such as potassium permanganate and potassium manganate can remove algae in water, while existing problems such as excessive oxidation and appearance of chromaticity. In this study, our objective was to induce a Fenton-like reaction by activating sodium percarbonate (SPC) with divalent manganese (Mn(II)) to pre-oxidize algae-contaminated water. The optimal dosage of Mn(II)/SPC was determined by assessing the zeta potential of the algae and the residual Mn(II) in the solution. Moreover, we conducted a characterization of the cells post-reaction and assessed the levels of dissolved organic carbon (DOC). The disinfection by-products (DBPs) (sodium hypochlorite disinfection)of the algae-containing water subsequent to Mn(II)/SPC treatment were measured. Experiments show that Mn(II)/SPC pre-oxidation at optimal dosage acquired 88% removal of algae and less damage to the cell membrane. Moreover, the Mn(II) acted not only as a catalyst but also formed MnO2 which adsorbed onto the cell surface and facilitated sedimentation. Furthermore, this technology exhibits the capability to effectively manage algal organic matters present in water, thereby mitigating the formation of nitrogen-containing DBPs. These results highlight the potential of Mn(II)/SPC treatment for treating water contaminated with algae, thus ensuring the safety and quality of water resources.

Facile synthesis of Ag/ZIF-8@ZIF-67 as an electrochemical sensing platform for sensitive detection of halonitrophenols in drinking water.

Halonitrophenols (HNPs) are an emerging type of aromatic disinfection byproduct, with detected concentrations of ∼nmol L-1 in source water and drinking water. Currently, there are no standard methods for identifying HNPs, and most of the reported methods are time-consuming and equipment-dependent. A core-shell metal-organic framework (MOF) based electrochemical sensor (Ag/ZIF-8@ZIF-67) capable of detecting 2,6-dichloro-4-nitrophenol (2,6-DCNP) is reported in this study. The electrochemical sensor obtains the concentration of 2,6-DCNP by detecting the peak current passing through the sensor. In this sensor, Ag nanoparticles (AgNPs) play a key role in electrochemical sensing by reducing nitro groups via electron transfer, and porous structure with a large surface area is offered by ZIF-8@ZIF-67. The cyclic voltammetry (CV) response of Ag/ZIF-8@ZIF-67 was found to be approximately 1.75 times and 2.23 times greater than that of Ag/ZIF-8 and Ag/ZIF-67, respectively, suggesting an ideal synergistic effect of the core-shell structures. The Ag/ZIF-8@ZIF-67 sensor exhibited exceptional sensitivity to 2,6-DCNP, exhibiting a broad linear response range (R2 = 0.992) from 240 nmol L-1 to 288 µmol L-1 and a low detection limit of 20 nmol L-1. Furthermore, the sensor exhibited good anti-interference for isomers and common distractors in water, excellent stability and reproducibility, and high recovery in actual water samples. Our reported sensor gives a novel strategy for sensitive, selective, and in situ detection of 2,6-DCNP in practical analysis.

CpCAF1 from Chimonanthus praecox Promotes Flowering and Low-Temperature Tolerance When Expressed in Arabidopsis thaliana.

CCR4-associated factor I (CAF1) is a deadenylase that plays a critical role in the initial step of mRNA degradation in most eukaryotic cells, and in plant growth and development. Knowledge of CAF1 proteins in woody plants remains limited. Wintersweet (Chimonanthus praecox) is a highly ornamental woody plant. In this study, CpCAF1 was isolated from wintersweet. CpCAF1 belongs to the DEDDh (Asp-Glu-Asp-Asp-His) subfamily of the DEDD (Asp-Glu-Asp-Asp) nuclease family. The amino acid sequence showed highest similarity to the homologous gene of Arabidopsis thaliana. In transgenic Arabidopsis overexpressing CpCAF1, the timing of bolting, formation of the first rosette, and other growth stages were earlier than those of the wild-type plants. Root, lateral branch, rosette leaf, and silique growth were positively correlated with CpCAF1 expression. FLOWERING LOCUS T (FT) and SUPPRESSOROF OVEREXPRESSION OF CO 1 (SOC1) gene expression was higher while EARLY FLOWERING3 (ELF3) and FLOWERING LOCUS C (FLC) gene expression of transgenic Arabidopsis was lower than the wild type grown for 4 weeks. Plant growth and flowering occurrences were earlier in transgenic Arabidopsis overexpressing CpCAF1 than in the wild-type plants. The abundance of the CpCAF1 transcript grew steadily, and significantly exceeded the initial level under 4 °C in wintersweet after initially decreasing. After low-temperature exposure, transgenic Arabidopsis had higher proline content and stronger superoxide dismutase activity than the wild type, and the malondialdehyde level in transgenic Arabidopsis was decreased significantly by 12 h and then increased in low temperature, whereas it was directly increased in the wild type. A higher potassium ion flux in the root was detected in transgenic plants than in the wild type with potassium deficiency. The CpCAF1 promoter was a constitutive promoter that contained multiple cis-acting regulatory elements. The DRE, LTR, and MYB elements, which play important roles in response to low temperature, were identified in the CpCAF1 promoter. These findings indicate that CpCAF1 is involved in flowering and low-temperature tolerance in wintersweet, and provide a basis for future genetic and breeding research on wintersweet.

Insights into the fate and properties of organic halamines during ultraviolet irradiation: Implications for drinking water safety.

Organic halamines compounds present a significant threat to the safety of drinking water due to their potential toxicity and stability. While Ultraviolet (UV) disinfection is commonly used for water treatment, its specific effects on organic halamines and the underlying mechanisms remain poorly understood. In this study, we investigated eight amino acid-derived organic chlor- and bromamines as representative compounds. Our findings revealed that organic halamines have a slow hydrolysis rate (<10-3 M-1 s-1) and can persist in water for extended periods (30-2000 min). However, their disinfection efficacy against Staphylococcus aureus and their ability to degrade micropollutants like carbamazepine were found to be limited. Interestingly, under UV irradiation, the N-X bonds in organic halamines were observed to break, leading to accelerated decomposition and the generation of abundant free radicals. These free radicals synergistically facilitated the removal of micropollutants and the inactivation of pathogenic microorganisms. It is worth noting that this transformation of organic halamines during UV disinfection resulted in a slight increase in the concentrations of nitrogenous disinfection byproducts. These findings shed light on the behavior and characteristics of organic halamines during UV disinfection processes, providing crucial insights for effectively managing drinking water quality impacted by these compounds. By understanding the implications of organic halamines, we can refine water treatment strategies and ensure the safety of drinking water supplies.

Pre-oxidation coupled with charged covalent organic framework membranes for highly efficient removal of organic chloramines precursors in algae-containing water treatment.

Organic chloramines in water would pose both chemical and microbiological risks. It is essential to remove the precursors of organic chloramine (amino acids and decomposed peptides/proteins) to limit its formation in disinfection. In our work, nanofiltration was chosen to remove organic chloramines precursors. To solve the "trade-off" effect and low rejection of small molecules in algae organic matter, we synthesized a thin film composite (TFC) nanofiltration (NF) membrane with a crumpled polyamide (PA) layer via interfacial polymerization on polyacrylonitrile (PAN) composite support loaded with covalent organic framework (COF) nanoparticles (TpPa-SO3H). The obtained NF membrane (PA-TpPa-SO3H/PAN) increased the permeance from 10.2 to 28.2 L m-2 h-1 bar-1 and the amino acid rejection from 24% to 69% compared to the control NF membrane. The addition of TpPa-SO3H nanoparticles decreased the thickness of PA layers, increased the hydrophilicity of the membrane, and increased the transition energy barrier for amino acids transferring through the membrane, which was identified by scanning electron microscope, contact angle test, and density functional theory computations, respectively. Finally, pre-oxidation coupled with PA-TpPa-SO3H/PAN membrane nanofiltration on the limitation of organic chloramines formation was evaluated. We found that the combined application of KMnO4 pre-oxidation and PA-TpPa-SO3H/PAN membranes nanofiltration in algae-containing water treatment could minimize the formation of organic chloramines in subsequent chlorination and maintain a high flux during filtration. Our work provides an effective way for algae-containing water treatment and organic chloramines control.

Molecular insights towards changing behaviors of organic matter in a full-scale water treatment plant using FTICR-MS.

The changing behavior of organic matter in a full-scale water treatment process was characterized based on the three-dimensional excitation-emission matrix (3D-EEM) and Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). Polyaluminum chloride (PAC) as a coagulant can help to effectively remove soluble microbial by-products-like and aromatic protein-like substances during coagulation and sedimentation, corresponding to tannin and coagulated aromatic regions. The leakage of soluble microbial products during sand filtration resulted in an increase in the intensity of biomass-like regions. Nitrogen-containing compounds have higher weighted average value of double bond equivalents (DBEw) and the modified aromaticity index (AImod-w) than nitrogen-free compounds. Water treatment can preferentially remove unsaturated nitrogen-containing compounds with more O atoms and higher-oxidation-state carbon. The dissolved organic carbon (DOC) and UV254 were not correlated well with changes in nitrogen-containing compounds due to the preferential removal of nitrogen-containing compounds. This study revealed the specificity of organic matter removal during water treatment, and it was helpful in optimizing treatment processes for various raw water to ensure water quality.

Predictive models for the aqueous phase reactivity of inorganic radicals with organic micropollutants.

Single-electron transfer (SET) is one of the most common reaction mechanisms for degrading organic micropollutants (OMPs) in advanced oxidation processes. We collected 300 SET reactions (CO3•-, SO4•-, Cl2•-, and Br2•--mediated) and calculated three key parameters for understanding the SET mechanism: aqueous phase free energies of activation (ΔG‡), free energies of reactions (ΔG), and orbital energy gaps of reactants (EOMPsHOMO-ERadiLUMO). Then, we classified the OMPs according to their structure, developed and evaluated linear energy relationships of the second-order rate constants (k) with ΔG‡, ΔG, or EOMPsHOMO-ERadiLUMO in each class. Considering that a single descriptor cannot capture all the chemical diversity, we combined ΔG‡, ΔG, and EOMPsHOMO-ERadiLUMO as inputs to develop multiple linear regression (MLR) models. Chemical classification is critical to the linear model described above. However, OMPs usually have multiple functional groups, making the classification challenging and uncertain. Therefore, we tried machine learning algorithms to predict k values without chemical classification. We found that decision trees (R2 = 0.88-0.95) and random forest (R2 = 0.90-0.94) algorithms show better performance on the prediction of the k values, whereas boosted tree algorithm cannot make an accurate prediction (R2 = 0.19-0.36). Overall, our study provides a powerful tool to predict the aqueous phase reactivity of OMP to certain radicals without the need for chemical classification.