Dynamic changes of peripheral inflammatory markers link with disease severity and predict short-term poor outcome of myasthenia gravis.

The relationship between peripheral inflammatory markers, their dynamic changes, and the disease severity of myasthenia gravis (MG) is still not fully understood. Besides, the possibility of using it to predict the short-term poor outcome of MG patients have not been demonstrated. This study aims to investigate the relationship between peripheral inflammatory markers and their dynamic changes with Myasthenia Gravis Foundation of America (MGFA) classification (primary outcome) and predict the short-term poor outcome (secondary outcome) in MG patients. The study retrospectively enrolled 154 MG patients from June 2016 to December 2021. The logistic regression was used to investigate the relationship of inflammatory markers with MGFA classification and determine the factors for model construction presented in a nomogram. Finally, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were utilized to evaluate the incremental capacity. Logistic regression revealed significant associations between neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), aggregate index of systemic inflammation (AISI) and MGFA classification (p = 0.013, p = 0.032, p = 0.017, respectively). Incorporating dynamic changes of inflammatory markers into multivariable models improved their discriminatory capacity of disease severity, with significant improvements observed for NLR, systemic immune-inflammation index (SII) and AISI in NRI and IDI. Additionally, AISI was statistically associated with short-term poor outcome and a prediction model incorporating dynamic changes of inflammatory markers was constructed with the area under curve (AUC) of 0.953, presented in a nomograph. The inflammatory markers demonstrate significant associations with disease severity and AISI could be regarded as a possible and easily available predictive biomarker for short-term poor outcome in MG patients.

Preparation and characterization of vitamin E microcapsules stabilized by Zein with different polysaccharides.

Vitamin E (VE) microencapsulation using a green surfactant emulsifier not only protects the active substance and is also environmentally friendly. In this study, we used alcohol ether glycoside as an emulsifier to prepare VE microcapsules using the biological macromolecule Zein and various polysaccharides. The resulting nano microcapsules exhibited a spherical structure, stable morphology, uniform size, and a >90% encapsulation efficiency. They also had good thermal stability and slow-release properties. Of these, xanthan gum/Zein-VE microcapsules were superior, with antioxidant properties up to 3.05-fold higher than untreated VE. We successfully developed VE nano microcapsules that meet eco-friendly and sustainable requirements, which may have applications in the food and pharmaceutical industries.

Experimental and numerical investigations on stress concentration factors of concrete filled steel tube X-joints.

An SHS-CFSHS X-joint is fabricated by welding two square hollow section (SHS) braces to a concrete-filled square hollow section (CFSHS) chord. In this paper, the stress concentration factors (SCFs) of SHS-CFSHS X-joints are investigated through experimental tests and finite element analysis (FEA), with the hot spot stress method serving as the analytical approach. Eight specimens are designed and manufactured, with FE models built in software ANSYS. These FE models are validated against the test results. The specimens are tested under brace axial tension to determine the SCFs of the X-joints. It shows that the concrete filled in the chord effectively reduces the SCFs of the X-joints. To further explore various load conditions and the influence of the parameters, FEA is carried out and a total of 64 FE models are built. Based on the FEA results, multiple regression analysis is used to obtain the SCF formulae of SHS-CFSHS X-joints under axial tension load and in-plane bending load in the brace, respectively. Comparison and analysis of the SCF results obtained from experimental tests, the proposed formulae, and FE simulations reveal that the formulae presented in this study are both conservative and suitable for predicting SCFs.

Incidence and Characteristics of Knee Ligament and Meniscal Injuries in Patients With Posterolateral Tibial Plateau Fractures.

Background: Anterior cruciate ligament (ACL) tears are commonly seen with concomitant injuries to the posterolateral tibial plateau, while the occurrence of ACL injuries in posterolateral tibial plateau fractures (PTPFs) remains unclear. Purpose: To (1) explore the incidence of knee ligament (anterior or posterior cruciate ligament, medial or lateral collateral ligament) and medial or lateral meniscus injuries in patients with PTPF and (2) find reliable PTPF-related parameters to predict the risk of knee ligament and meniscal injuries. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Patients diagnosed with PTPF who had computed tomography and magnetic resonance imaging (MRI) data were identified. Morphological parameters of the PTPF were measured on sagittal computed tomography images. Knee ligament and meniscal injuries were assessed using MRI. The association of ACL injuries with meniscal injuries was analyzed. Receiver operating characteristic (ROC) analysis was used to determine the value and cutoff point of the PTPF morphological parameters for diagnosing complete in-substance ACL tears. Results: Overall, 113 patients with PTPF were included. ACL injuries were present in 94 (83.2%) patients, including 43 (38.1%) avulsion fractures and 28 (24.8%) complete in-substance tears. Patients with in-substance ACL tears had a higher incidence of lateral meniscus posterior horn tears compared with the other patients (PBonferroni < .001). ROC analysis revealed that both the fracture depression angle (cutoff point, 25.5°) and the posterior articular surface loss percentage (cutoff point, 37.5%) had a sensitivity >90% and a specificity >80% for the diagnosis of complete in-substance ACL tears. Conclusion: ACL injuries were seen in 83.2% of the study patients. Complete in-substance ACL tears were associated with an increased incidence of lateral meniscus posterior horn tears. Among PTPF parameters, fracture depression angle and posterior articular surface loss percentage showed a high predictive value for the presence of complete in-substance ACL tears, thereby reducing delays in diagnosis and treatment.

A multidisciplinary collaborative diagnosis and rehabilitation program for dysphagia in general hospitals.

Dysphagia is a common complication of various clinical conditions, with an increased incidence as age advances. Complications such as aspiration, malnutrition, and aspiration pneumonia caused by dysphagia significantly affect the overall treatment outcomes of patients. Scholars both domestically and internationally are increasingly focusing on early rehabilitation for dysphagia. This article summarizes common conditions causing dysphagia, clinical manifestations, complications, screening assessment, diagnosis, rehabilitation, and nutritional support related to dysphagia. It emphasizes the arrival at a multidisciplinary collaborative diagnosis and formulation of a rehabilitation management plan for dysphagia in general hospitals in order to provide strategic suggestions for establishing a multidisciplinary collaborative model for swallowing disorder management in general hospitals.

Systemic Inflammatory Response Index, a Potential Inflammatory Biomarker in Disease Severity of Myasthenia Gravis: A Pilot Retrospective Study.

Purpose: Myasthenia gravis (MG) is a chronic autoimmune disease caused by neuromuscular junction (NMJ) dysfunction. Our current understanding of MG's inflammatory component remains poor. The systemic inflammatory response index (SIRI) presents a promising yet unexplored biomarker for assessing MG severity. This study aimed to investigate the potential relationship between SIRI and MG disease severity. Patients and Methods: We conducted a retrospective analysis of clinical data from 171 MG patients admitted between January 2016 and June 2021. Patients with incomplete data, other autoimmune diseases, or comorbidities were excluded. Disease severity was evaluated using the Myasthenia Gravis Foundation of America (MGFA) classification and Myasthenia Gravis Activities of Daily Living (MG-ADL) on admission. The association between SIRI and disease severity was assessed through logistic regression analysis, along with receiver operating characteristic (ROC) curve and decision curve analysis (DCA) comparisons with established inflammation indicators. Results: After exclusion, 143 patients were analyzed in our study. SIRI levels significantly differed between patients with higher and lower disease severity (p < 0.001). Univariate logistic regression showed that SIRI had a significant effect on high disease severity (OR = 1.376, 95% CI 1.138-1.664, p = 0.001). This association remained significant even after adjusting for age, sex, disease duration, history of MG medication and thymoma (OR = 1.308, 95% CI 1.072-1.597, p = 0.008). Additionally, a positive correlation between SIRI and MG-ADL was observed (r = 0.232, p = 0.008). Significant interactions were observed between SIRI and immunosuppressor (p interaction = 0.001) and intravenous immunoglobulin (p interaction = 0.005). DCA demonstrated the superior net clinical benefit of SIRI compared to other markers when the threshold probability was around 0.2. Conclusion: Our findings indicate a strong independent association between SIRI and disease severity in MG, suggesting SIRI's potential as a valuable biomarker for MG with superior clinical benefit to currently utilized markers.

Characteristics of the oral microbiota in patients with primary Sjögren's syndrome.

OBJECTIVE: Primary Sjögren's syndrome (pSS) is an autoimmune disease with unknown etiology that is considered to be related to environmental and genetic factors. The aim of this study was to clarify the oral microflora characteristics of pSS patients and to reveal the connection between oral bacterial composition and dental caries using a high-throughput sequencing technique. METHODS: Thirty-five pSS patients and 20 healthy controls were enrolled in this study. We collected saliva and plaque samples from pSS patients and saliva samples from healthy controls. We used 16S ribosomal DNA (16S rDNA) high-throughput sequencing targeting the V3-V4 hypervariable region to determine the composition and structure of the microbiota in the three sample sets. Finally, bioinformatics analyses, including the diversity of the microbiota, species differences, and functional prediction were performed. RESULTS: In the alpha diversity and beta diversity analysis, the Chao1 (P < 0.01), observed species (P < 0.01), and PD whole tree indices (P < 0.01) were significantly lower in the saliva and plaque samples of pSS patients than in the saliva samples of healthy controls, but the Shannon (P < 0.01) and Simpson indices (P < 0.01) were significantly higher in the healthy controls, and their total diversity significantly differed. In the main flora composition at the genus level (top 10), we identified Prevotella and Veillonella as more enriched in the saliva of pSS patients and Fusobacterium, Actinomyces, and Leptotrichia as more enriched in the plaque of pSS patients. Predictive functional analysis showed that the oral microbiota of pSS patients was related to translation, metabolism of cofactors and vitamins, and nucleotide metabolism. CONCLUSIONS: The oral microbial ecology of patients with pSS is dysregulated, resulting in a decrease in overall diversity. Prevotella and Veillonella may be related to pSS, while Fusobacterium, Actinomyces, and Leptotrichia may be related to dental caries in pSS patients. Key Points • This study revealed differences in the oral microbial composition of patients with pSS compared to healthy controls. • We included a plaque group of pSS patients to identify the microbiota related to pSS and dental caries. • Prevotella and Veillonella may contribute to pSS, and Fusobacterium, Actinomyces, and Leptotrichia are associated with dental caries in pSS patients.

Association of Lipoprotein-associated phospholipase A2 With Carotid Intima-Media Thickness in Acute Cerebral Infarction Patients.

Lipoprotein-associated phospholipase A2 (Lp-PLA2) is an inflammatory marker associated with atherosclerotic and cardiovascular diseases. This study aimed to explore the association of Lp-PLA2 with carotid intima-media thickness (cIMT) in patients with acute ischemic stroke (AIS) and explore a threshold level to predict the risk of vulnerable plaques. This retrospective observational study included patients with AIS in the Neurology Department of our Hospital between January 2018 and December 2019. The study included 293 patients aged 65.29 ± 12.11 years, including 212 males, of whom 124 had carotid intima-media thickening (42.32%). Multivariable logistic regression showed that Lp-PLA2 level was an independent risk factor for cIMT (odds ratio [OR] = 1.004, 95% confidence interval [95% CI] 1.001-1.008, P = .008). Threshold effect analysis showed that the risk of vulnerable carotid plaque occurrence increased by 2% for every 1 ng/mL increase in Lp-PLA2 level with serum Lp-PLA2 levels between 157 and 279 ng/mL; this increase was statistically significant (OR = 1.02, 95% CI 1.01-1.03, P < .001). Serum Lp-PLA2 is an independent risk factor for increased cIMT in patients with AIS, and a threshold Lp-PLA2 level between 157 and 279 ng/mL showed a higher risk of carotid plaque rupture.

An "Observable" horseshoe map.

In this article, a family of diffeomorphisms with growing horseshoes contained in global attracting regions is presented, where the dimension of the unstable direction can be any fixed integer and a growing horseshoe means that the number of the folds of the horseshoe is increasing as a parameter is varied. Moreover, it is demonstrated that the horseshoe-like attractors are observable for certain parameters.

Self-assembly mechanism, physicochemical analyses and application performance investigations of branched alkyl glycosides with alcohol ether carboxylic acids of varied epoxide numbers.

Green surfactants, specifically alkyl glycosides and fatty alcohol ether carboxylic acids, are known for their biocompatibility, multiresponsiveness, and versatile applications, garnering significant attention in the realms of green and colloid chemistry. This study systematically investigated the mechanism underlying micelle formation within aqueous solutions comprising alcohol ether carboxylic acids featuring diverse EO group chain quantities (AEC-nH, where n equals 5, 7, and 9) and branched alkyl glycosides (IG). The elucidation of these mechanisms sheds light on their prospective application properties. It was observed that the self-assembly of micelles in these hybrid systems is predominantly influenced by hydrogen bonding, electrostatic interactions, and hydrophobic forces. The spherical-rod morphology of the micelles responds to the varying numbers of EO group chains, with an increased number of EO leading to the formation of rod-like micelles, which exhibit relative instability, while a decreased number of EO results in the formation of spherical micelles with relative stability. Additionally, by means of kinetic analysis, it was determined that the micelle formation process of the three hybrid systems is driven by enthalpy, and a mixed diffusion-kinetics adsorption mechanism is involved in the adsorption process. These findings significantly impact their application properties. This report stands as the first exploration of the synergistic mechanisms and application performance of two types of green surfactants in aqueous solutions, considering the influence of different numbers of EO group chains. Not only does it provide fundamental insights into their properties, but it also offers novel perspectives on the applications of green surface activation.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis.

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification.

Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions. However, traditional brain connectivity networks are irrelevant to the downstream prediction tasks. This article proposes a connectivity-aware graph neural network (CAGNN) using a self-attention mechanism that can generate task-relevant connectivity networks via end-to-end training. Our method achieved an accuracy of 72.6% and outperformed other convolutional neural networks (CNNs) and graph generation methods based on a drowsy driving dataset. In addition, we introduced a squeeze-and-excitation (SE) block to capture important features and demonstrated that the SE attention score can reveal the most important feature band. We compared our generated connectivity graphs in the drowsy and alert states and found drowsiness connectivity patterns, including significantly reduced occipital connectivity and interregional connectivity. Additionally, we performed a post hoc interpretability analysis and found that our method could identify drowsiness features such as alpha spindles. Our code is available online at https://github.com/ALEX95GOGO/CAGNN.

Human neural stem cells.

'Human neural stem cells' jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human neural stem cells (hNSCs) in China. This standard specifies the technical requirements, test methods, test regulations, instructions for use, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for hNSCs, which is applicable to the quality control for hNSCs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that publication of the guideline will facilitate institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of hNSCs for clinical development and therapeutic applications.

Human midbrain dopaminergic progenitors.

Human midbrain dopaminergic progenitors (mDAPs) are one of the most representative cell types in both basic research and clinical applications. However, there are still many challenges for the preparation and quality control of mDAPs, such as the lack of standards. Therefore, the establishment of critical quality attributes and technical specifications for mDAPs is largely needed. "Human midbrain dopaminergic progenitor" jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human mDAPs in China. This standard specifies the technical requirements, test methods, inspection rules, instructions for usage, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for human mDAPs, which is applicable to the quality control for human mDAPs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that the publication of this guideline will facilitate the institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of human mDAPs for clinical development and therapeutic applications.

BioTreasury: a community-based repository enabling indexing and rating of bioinformatics tools.

The exponential growth of bioinformatics tools in recent years has posed challenges for scientists in selecting the most suitable one for their data analysis assignments. Therefore, to aid scientists in making informed choices, a community-based platform that indexes and rates bioinformatics tools is urgently needed. In this study, we introduce BioTreasury ( http://biotreasury.rjmart.cn ), an integrated community-based repository that provides an interactive platform for users and developers to share their experiences in various bioinformatics tools. BioTreasury offers a comprehensive collection of well-indexed bioinformatics software, tools, and databases, totaling over 10,000 entries. In the past two years, we have continuously improved and maintained BioTreasury, adding several exciting features, including creating structured homepages for every tool and user, a hierarchical category of bioinformatics tools and classifying tools using large language model (LLM). BioTreasury streamlines the tool submission process with intelligent auto-completion. Additionally, BioTreasury provides a wide range of social features, for example, enabling users to participate in interactive discussions, rate tools, build and share tool collections for the public. We believe BioTreasury can be a valuable resource and knowledge-sharing platform for the biomedical community. It empowers researchers to effectively discover and evaluate bioinformatics tools, fostering collaboration and advancing bioinformatics research.

Classification of inattentional blindness using brain dynamics of ERPs.

Situational awareness (SA) is vital for understanding our surroundings. Multiple variables, including inattentive blindness (IB), contribute to the deterioration of SA, which may have detrimental effects on individuals' cognitive performance. IB occurs due to attentional limitations, ignoring critical information and resulting in a loss of SA and a decline in general performance, particularly in complicated situations requiring substantial cognitive resources. To the best of our knowledge, however, past research has not fully uncovered the neurological characteristics of IB nor classified these characteristics in life-alike virtual situations. Therefore, the purpose of this study is to determine whether ERP dynamics in the brain may be utilised as a neural feature to predict the occurrence of IB using machine learning (ML) algorithms. In a virtual reality simulation of an IB experiment, 30 participants' behaviour and Electroencephalography (EEG) measurements were obtained. Participants were given a target detection task in the IB experiment without knowing the unattended shapes displayed on the background building. The targets were presented in three different sensory modalities (auditory, visual, and visual-auditory). On the post-experiment questionnaire, participants who claimed not to have noticed the unattended shapes were assigned to the IB group. Subsequently, the Aware group was formed from individuals who reported seeing the unattended shapes. Using EEGNet to classify IB and Aware groups demonstrated a high classification performance. According to the research, ERP brain dynamics are associated with the awareness of unattended shapes and have the potential to serve as a reliable indication for predicting the visual consciousness of unexpected objects.(p/)(p)Clinical relevance- This research offers a potential brain marker for the mixed-reality and BCI systems that will be used in the future to identify cognitive deterioration, maintain attentional capacity, and prevent disasters.

A Novel Form of Neuregulin 1 Type III Caused by N-Terminal Processing.

Nrg1 (Neuregulin 1) type III, a susceptible gene of schizophrenia, exhibits a critical role in the central nervous system and is essential at each stage of Schwann's cell development. Nrg1 type III comprises double-pass transmembrane domains, with the N-terminal and C-terminal localizing inside the cells. The N-terminal transmembrane helix partially overlaps with the cysteine-rich domain (CRD). In this study, Nrg1 type III constructs with different tags were transformed into cultured cells to verify whether CRD destroyed the transmembrane helix formation. We took advantage of immunofluorescent and immunoprecipitation assays on whole cells and analyzed the N-terminal distribution. Astonishingly, we found that a novel form of Nrg1 type III, about 10% of Nrg1 type III, omitted the N-terminal transmembrane helix, with the N-terminal positioning outside the membrane. The results indicated that the novel single-pass transmembrane status was a minor form of Nrg1 type III caused by N-terminal processing, while the major form was a double-pass transmembrane status.

Research progress of ferroptosis in Parkinson's disease: a bibliometric and visual analysis.

Background: In recent years, the role of ferroptosis in Parkinson's disease (PD) has become a research hotspot based on evidence of abnormal iron deposition and lipid peroxidation damage in the brains of PD patients. This study aims to examine the relevant research on ferroptosis and PD from a bibliometric perspective. Methods: Original research and review articles related to ferroptosis and PD were retrieved from the Web of Science Core Collection (WOSCC) database. Statistical analysis and visualization of information including countries, institutions, authors, journals, and keywords of the included studies were conducted using the R software package "bibliometrix." Results: A total of 414 articles met the inclusion criteria, averaging 37.86 citations per article. From 2012 to 2022, the average annual growth rate of research in this area was 63.44%. The corresponding authors of published articles were mainly affiliated with institutions in China, the United States, and Australia. Shanghai Jiao Tong University in China and the University of Melbourne in Australia emerged as the most active and influential institutions. The journal with the highest H-index and publication output was Free Radical Biology and Medicine. "Ferroptosis," "immunotherapy," "prognosis" and "microenvironment" were identified as high-frequency keywords, indicating current and future research directions in this field. Conclusion: This bibliometric study provides insights into current research hotspots and emerging trends in the growing field of ferroptosis research related to PD. The high-frequency keywords identified highlight active areas of investigation involving methods, mechanisms, and populations of interest.

[Characteristics and Ecological Risk Assessment of Pharmaceutical Active Compounds in Guangdong Province].

Pharmaceutically active compounds(PhACs) have become a class of new pollutants in the environment after extensive production and use of PhACs in China. To investigate the pollution characteristics of PhACs in Guangdong Province, raw sewage was collected from 186 sewage treatment plants in 21 cities, including 178 townships and administrative districts in Guangdong Province. The pollution levels of ten typical PhACs in influent water of sewage treatment plants were analyzed using automatic solid phase extraction and high performance liquid chromatography-triple quadrupole mass spectrometry. The spatial distribution characteristics of PhACs in Guangdong Province were fully revealed, and the potential ecological risks of PhACs were evaluated. The results showed that PhACs were detected in all wastewater plants, and the mass concentration of PhACs ranged from 21.00 to 9558.25 ng·L-1. Metoprolo, acetaminophen, bezafibrate, and caffeine were the main pollutants. In terms of spatial distribution, the average mass concentration of ΣPhACs in various regions of Guangdong Province was in the following order:Pearl River Delta>North Guangdong>East Guangdong≈West Guangdong. When the mass concentration of ΣPhACs was over 2500 ng·L-1 in the influent water of sewage treatment plants, the concentration of PhACs in effluent was estimated according to the sewage disposal technology. The ecological risk of PhACs was carried out based on the effluent. The results revealed that the ecological risk of PhACs was low in Guangdong Province, and the risk of bezafibrate was moderate in the cities of Shaoguan, Jiangmen, and Shenzhen. The highest ecological risk of ΣPhACs was located in Shaoguan.

AgBr nanoparticle surface modified SnO2 enhanced visible light catalytic performance: characterization, mechanism and kinetics study.

In this study, a simple hydrothermal procedure and in situ precipitation method were used to prepare SnO2-AgBr composites, where the molar ratios of SnO2 and AgBr were 1 : 1, 1 : 2 and 2 : 1. Characterization results showed that the composites had excellent dispersion, crystallinity, and purity. A photocatalytic degradation experiment and first-order kinetic model indicate that SnO2-AgBr (1 : 1) had the best photocatalytic performance, and the degradation rates of 30 mg L-1 simulated MO and MG wastewater reached 96.71% and 93.36%, respectively, in 150 min, which were 3.5 times those of SnO2. The degradation rate of MO and MG increases with the dosage. Humic acid inhibited the degradation of MG, while a low concentration of humic acid promoted the degradation of MO, and the composite has good stability with pH. A free radical trapping experiment shows that ·OH and ·O2- were the main active substances, and h+ was the secondary one. According to the results of the characterization and photocatalysis experiments, a Z-scheme mechanism for the SnO2-AgBr composite was proposed, and the degradation pathway of target pollutants was speculated upon. This study has conceived novel methods for the development of a mature Z-scheme mechanism and in doing so has provided new approaches for the development of photocatalysis for water pollution control.