Relationship between triglyceride-glucose index baselines and trajectories with incident cardiovascular diseases in the elderly population.

BACKGROUND: The triglyceride-glucose (TyG) index is regarded as a sophisticated surrogate biomarker for insulin resistance, offering a refined means for evaluating cardiovascular diseases (CVDs). However, prospective cohort studies have not simultaneously conducted baseline and multi-timepoint trajectory assessments of the TyG index in relation to CVDs and their subtypes in elderly participants. METHODS: After excluding data deficiencies and conditions that could influence the research outcomes, this study ultimately incorporated a cohort of 20,185 participants, with data chronicles extending from 2016 to 2022. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Latent Class Trajectory Model (LCTM) was used to assess the change trends of the TyG index over multiple time points. Utilizing the Cox proportional-hazards models, we assessed the relationship between the baseline quartiles of the TyG index and various trajectories with CVDs and subtypes. RESULTS: During the mean follow-up time of 4.25 years, 11,099 patients experienced new CVDs in the elderly population. After stratifying by baseline TyG quartiles, the higher TyG level was associated with an increased risk of CVDs; the aHR and 95% CI for the highest quartile group were 1.28 (1.19-1.39). Five trajectory patterns were identified by the LCTM model. The low gradual increase group as the reference, the medium stable group, and the high gradual increase group exhibited an elevated risk of CVDs onset, aHR and 95%CIs were 1.17 (1.10-1.25) and 1.25 (1.15-1.35). Similar results were observed between the trajectories of the TyG index with subtypes of CVDs. CONCLUSION: Participants with high levels of baseline TyG index and medium stable or high gradual increase trajectories were associated with an elevated risk of developing CVDs in elderly populations.

Integrated mass spectrometry imaging and metabolomics reveals sublethal effects of indoxacarb on the red fire ant Solenopsis invicta.

BACKGROUND: Indoxacarb, representing an efficient insecticide, is normally made into a bait to spread the poison among red fire ants so that it can be widely applied in the prevention and control of Solenopsis invicta. However, the potential toxicity mechanism of S. invicta in response to indoxacarb remains to be explored. In this study, we integrated mass spectrometry imaging (MSI) and untargeted metabolomics methods to reveal disturbed metabolic expression levels and spatial distribution within the whole-body tissue of S. invicta treated with indoxacarb. RESULTS: Metabolomics results showed a significantly altered level of metabolites after indoxacarb treatment, such as carbohydrates, amino acids and pyrimidine and derivatives. Additionally, the spatial distribution and regulation of several crucial metabolites resulting from the metabolic pathway and lipids can be visualized using label-free MSI methods. Specifically, xylitol, aspartate, and uracil were distributed throughout the whole body of S. invicta, while sucrose-6'-phosphate and glycerol were mainly distributed in the abdomen of S. invicta, and thymine was distributed in the head and chest of S. invicta. Taken together, the integrated MSI and metabolomics results indicated that the toxicity mechanism of indoxacarb in S. invicta is closely associated with the disturbance in several key metabolic pathways, such as pyrimidine metabolism, aspartate metabolism, pentose and glucuronate interconversions, and inhibited energy synthesis. CONCLUSION: Collectively, these findings provide a new perspective for the understanding of toxicity assessment between targeted organisms S. invicta and pesticides. © 2023 Society of Chemical Industry.

Mechanism of Rotenone Toxicity against Plutella xylostella: New Perspective from a Spatial Metabolomics and Lipidomics Study.

The botanical pesticide rotenone can effectively control target pest Plutella xylostella, yet insights into in situ metabolic regulation of P. xylostella toward rotenone remain limited. Herein, we demonstrated metabolic expression levels and spatial distribution of rotenone-treated P. xylostella using spatial metabolomics and lipidomics. Specifically, rotenone significantly affected purine and amino acid metabolisms, indicating that adenosine monophosphate and inosine were distributed in the whole body of P. xylostella with elevated levels, while guanosine 5'-monophosphate and tryptophan were significantly downregulated. Spatial lipidomics results indicated that rotenone may significantly destroy glycerophospholipids in cell membranes of P. xylostella, inhibit fatty acid biosynthesis, and consume diacylglycerol to enhance fat oxidation. These findings revealed that high toxicity of rotenone toward P. xylostella may be ascribed to negative effects on energy production and amino acid synthesis and damage to cell membranes, providing guidelines for the toxicity mechanism of rotenone on target pests and rational development of botanical pesticide candidates.

Metabolic profiling reveals genotype-associated alterations in carotenoid content during banana postharvest ripening.

Banana fruits have attracted considerable attention for health-promoting effects attributed to ubiquitous functional metabolites. However, genotype-dependent accumulation patterns of carotenoids in banana remain largely unclear. Here, we performed a systematic metabolomic investigation of 18 banana cultivars of the AAA, AAB, or ABB genome groups. Our results indicate that the levels of soluble sugars increase during postharvest ripening regardless of genotype, whereas amino acids (AAs) and tricarboxylic acid (TCA) cycle-derived organic acids display genotype-dependent patterns. The levels of AAs derived from the glycolytic pathway increased, whereas those derived from the TCA cycle significantly decreased during ripening. The carotenoid composition in banana pulp was genotype-specific, and the contents of α-carotene were the highest in AAA-genome bananas. Moreover, high α-carotene and ß-carotene contents in banana were correlated with elevated levels of TCA cycle-derived AAs and decreased levels of glycolysis-derived AAs. Taken together, these findings provide a comprehensive understanding of genotype-associated carotenoid accumulation, thereby facilitating the breeding of future high carotenoid banana cultivars.

Monitoring of urban ecological environment including air quality using satellite imagery.

Rapid urbanisation has highlighted problems in the urban ecological environment and stimulated research on the evaluation of urban environments. In previous studies, key factors such as greenness, wetness, and temperature were extracted from satellite images to assess the urban ecological environment. Although air pollution has become increasingly serious as urbanisation proceeds, information on air pollution is not included in existing models. The Sentinel-5P satellite launched by the European Space Agency in 2017 is a reliable data source for monitoring air quality. By making full use of images from Landsat 8, Sentinel-2A, and Sentinel-5P, this work attempts to construct a new remote sensing monitoring index for urban ecology by adding air quality information to the existing remote sensing ecological index. The proposed index was tested in the Beijing metropolitan area using satellite data from 2020. The results obtained using the proposed index differ greatly in the central urban region and near large bodies of water from those obtained using the existing remote sensing monitoring model, indicating that air quality plays a significant role in evaluating the urban ecological environment. Because the model constructed in this study integrates information on vegetation, soil, humidity, heat, and air quality, it can comprehensively and objectively reflect the quality of the urban ecological environment. Consequently, the proposed remote sensing index provides a new approach to effectively monitoring the urban ecological environment.

Spatially resolved metabolomics reveals variety-specific metabolic changes in banana pulp during postharvest senescence.

Banana is one of most popular fruits globally due to health-promoting and disease-preventing effects, yet little is known about in situ metabolic changes across banana varieties. Here, we integrated gold nanoparticle (AuNP)-assisted laser desorption/ionization mass spectrometry imaging (LDI-MSI) and metabolomics to investigate the spatiotemporal distribution and levels of metabolites within Brazil and Dongguan banana pulps during postharvest senescence. Metabolomics results indicated that both postripening stages and banana varieties contribute to metabolite levels. Benefiting from improved ionization efficiency of small-molecule metabolites and less peak interference, we visualized the spatiotemporal distribution of sugars, amino acids (AAs) and monoamines within pulps using AuNP-assisted LDI-MSI for the first time, revealing that AAs and monoamines exclusively accumulated in the middle region near the seed zone. Monosaccharides and di/trisaccharides were generally distributed across entire pulps but exhibited different accumulation patterns. These findings provide a guide for breeding new varieties and improving extraction efficiency of bioactive compounds.

Sample preparation optimization of insects and zebrafish for whole-body mass spectrometry imaging.

Appropriate sample preparation is one of the most critical steps in mass spectrometry imaging (MSI), which is closely associated with reproducible and reliable images. Despite that model insects and organisms have been widely used in various research fields, including toxicology, drug discovery, disease models, and neurobiology, a systematic investigation on sample preparation optimization for MSI analysis has been relatively rare. Unlike mammalian tissues with satisfactory homogeneity, freezing sectioning of the whole body of insects is still challenging because some insect tissues are hard on the outside and soft on the inside, especially for some small and fragile insects. Herein, we systematically investigated the sample preparation conditions of various insects and model organisms, including honeybees (Apis cerana), oriental fruit flies (Bactrocera dorsalis), zebrafish (Danio rerio), fall armyworms (Spodoptera frugiperda), and diamondback moths (Plutella xylostella), for MSI. Three cutting temperatures, four embedding agents, and seven thicknesses were comprehensively investigated to achieve optimal sample preparation protocols for MSI analysis. The results presented herein indicated that the optimal cutting temperature and embedding agent were -20 °C and gelatin, respectively, providing better tissue integrity and less mass spectral interference. However, the optimal thickness for different organisms can vary with each individual. Using this optimized protocol, we exploited the potential of MSI for visualizing the tissue-specific distribution of endogenous lipids in four insects and zebrafish. Taken together, this work provides guidelines for the optimized sample preparation of insects and model organisms, facilitating the expansion of the potential of MSI in the life sciences and environmental sciences.

Stereoselective toxicity mechanism of neonicotinoid dinotefuran in honeybees: New perspective from a spatial metabolomics study.

Development of stereoisomeric neonicotinoid pesticides with lower toxicity is key to preventing global population declines of honeybees, whereas little is known about the in situ metabolic regulation of honeybees in response to stereoisomeric pesticides. Herein, we demonstrate an integrated mass spectrometry imaging (MSI) and untargeted metabolomics method to disclose disturbed metabolic expression levels and spatial differentiation in honeybees (Apis cerana) associated with stereoisomeric dinotefuran. This method affords a metabolic network mapping capability regarding a wide range of metabolites involved in multiple metabolic pathways in honeybees. Metabolomics results indicate more metabolic pathways of honeybees can be significantly affected by S-(+)-dinotefuran than R-(-)-dinotefuran, such as tricarboxylic acid (TCA) cycle, glyoxylate and dicarboxylate metabolism, and various amino acid metabolisms. MSI results demonstrate the cross-regulation and spatial differentiation of crucial metabolites involved in the TCA cycle, purine, glycolysis, and amino acid metabolisms within honeybees. Taken together, the integrated MSI and metabolomics results indicated the higher toxicity of S-(+)-dinotefuran arises from metabolic pathway disturbance and its inhibitory role in the energy metabolism, resulting in significantly reduced degradation rates of detoxification mechanisms. From the view of spatial metabolomics, our findings provide novel perspectives for the development and applications of pure chiral agrochemicals.

Spatiotemporal Visualization of Insecticides and Fungicides within Fruits and Vegetables Using Gold Nanoparticle-Immersed Paper Imprinting Mass Spectrometry Imaging.

Food safety issues caused by pesticide residue have exerted far-reaching impacts on human daily life, yet the available detection methods normally focus on surface residue rather than pesticide penetration to the internal area of foods. Herein, we demonstrated gold nanoparticle (AuNP)-immersed paper imprinting mass spectrometry imaging (MSI) for monitoring pesticide migration behaviors in various fruits and vegetables (i.e., apple, cucumber, pepper, plum, carrot, and strawberry). By manually stamping food tissues onto AuNP-immersed paper, this method affords the spatiotemporal visualization of insecticides and fungicides within fruits and vegetables, avoiding tedious and time-consuming sample preparation. Using the established MSI platform, we can track the migration of insecticides and fungicides into the inner region of foods. The results revealed that both the octanol-water partition coefficient of pesticides and water content of garden stuffs could influence the discrepancy in the migration speed of pesticides into food kernels. Taken together, this nanopaper imprinting MSI is poised to be a powerful tool because of its simplicity, rapidity, and easy operation, offering the potential to facilitate further applications in food analysis. Moreover, new perspectives are given to provide guidelines for the rational design of novel pesticide candidates, reducing the risk of food safety issues caused by pesticide residue.

Insights into the degradation and toxicity difference mechanism of neonicotinoid pesticides in honeybees by mass spectrometry imaging.

Honeybees are essential for the pollination of a wide variety of crops and flowering plants, whereas they are confronting decline around the world due to the overuse of pesticides, especially neonicotinoids. The mechanism behind the negative impacts of neonicotinoids on honeybees has attracted considerable interest, yet it remains unknown due to the limited insights into the spatiotemporal distribution of pesticides in honeybees. Herein, we demonstrated the use of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) for the spatiotemporal visualization of neonicotinoids, such as N-nitroguanidine (dinotefuran) and N-cyanoamidine (acetamiprid) compounds, administered by oral application or direct contact, in the whole-body section of honeybees. The MSI results revealed that both dinotefuran and acetamiprid can quickly penetrate various biological barriers and distribute within the whole-body section of honeybees, but acetamiprid can be degraded much faster than dinotefuran. The degradation rate of acetamiprid is significantly decreased when piperonyl butoxide (PBO) is applied, whereas that of dinotefuran remains almost unchanged. These two factors might contribute to the fact that dinotefuran affords a higher toxicity to honeybees than acetamiprid. Moreover, the toxicity and degradation rate of acetamiprid can be affected by co-application with tebuconazole. Taken together, the results presented here indicate that the discrepant toxicity between dinotefuran and acetamiprid does not lie in the difference in their penetration of various biological barriers of honeybees, but in the degradation rate of neonicotinoid pesticides within honeybee tissues. Moreover, new perspectives are given to better understand the causes of the current decline in honeybee populations posed by insecticides, providing guidelines for the precise use of conventional agrochemicals and the rational design of novel pesticide candidates.

Rapid structural discrimination of IgG antibodies by multicharge-state collision-induced unfolding.

Immunoglobulin G (IgG) antibodies are an important class of biotherapeutics that target various diseases, such as cancers, neurodegenerative disorders, and autoimmune diseases, yet rapid discrimination of IgG antibodies remains a great challenge due to heterogeneity, flexibility, and large size. Herein, we demonstrate a simplified multicharge-state collision-induced unfolding (CIU) method for rapid differentiation of four IgG isotypes that differ in terms of the numbers and patterns of disulfide bonds, bypassing tedious single charge-state selection in advance. The results presented herein reveal that gas-phase unfolding behaviors have a strong dependence on charge states outside IgG surfaces; therefore, multicharge-state CIU analysis of IgG subtypes could offer a great opportunity to gain deeper insights into their gas-phase structural differentiation. The full discrimination of IgG antibody isoforms that possess different disulfide bond numbers and even subtle disulfide bonding patterns can be achieved based on their charge-dependent gas-phase unfolding behaviors and root-mean square deviation in CIU difference spectra. Taken together, the incorporation of all charge states observed in a native ion mobility-mass spectrometry (IM-MS) experiment to CIU analysis could make this strategy sensitive to more subtle structural discrepancies, facilitating the rapid discrimination and evaluation of innovative structurally similar biotherapeutic candidates with unexplored functions.

Identification of urban land use efficiency by indicator-SDG 11.3.1.

Inefficiency in urban land use is one of the problems caused by rapid urbanization. The UN Sustainable Development Goals (SDGs) indicator 11.3.1 is designed to test urban land use efficiency. This study employed geospatial and statistical data to compute land use efficiencies from 1990 to 2015 with five 5-year and ten 15-year intervals in Wukang, center of Deqing County, China. A flowchart was designed to extract the built-up lands from multiple data sources. The produced built-up lands were demonstrated to provide good accuracy by constructing an error matrix between the extracted and manually interpreted built-up lands as classified and reference images, respectively. By using the model provided by UN metadata to calculate SDG 11.3.1, the land use efficiencies from 1990 to 2015 were identified in Wukang. Our results indicate that the land use efficiency in Deqing County center is lower than the average of cities around the world, primarily because our in-situ study focused on a county center with larger rural regions than urban areas. Over the long term, urban land use becomes denser as the population grows, which will have a positive impact on the sustainability of urban development. This work is helpful for the local government to balance urban land consumption and population growth.

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery.

High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent "self-learning ability" of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.

Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery.

Sample size estimation is a key issue for validating land cover products derived from satellite images. Based on the fact that present sample size estimation methods account for the characteristics of the Earth's subsurface, this study developed a model for estimating sample size by considering the scale effect and surface heterogeneity. First, we introduced a watershed with different areas to indicate the scale effect on the sample size. Then, by employing an all-subsets regression feature selection method, three landscape indicators describing the aggregation and diversity of the land cover patches were selected (from 14 indicators) as the main factors for indicating the surface heterogeneity. Finally, we developed a multi-level linear model for sample size estimation using explanatory variables, including the estimated sample size (n) calculated from the traditional statistical model, size of the test region, and three landscape indicators. As reference data for developing this model, we employed a case study in the Jiangxi Province using a 30 meter spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 meter land use/cover change (LUCC) data from 2010 in China. The results showed that the adjusted square coefficient of R2 is 0.79, indicating that the joint explanatory ability of all predictive variables in the model to the sample size is 79%. This means that the predictability of this model is at a good level. By comparing the sample size NsNS obtained by the developed multi-level linear model and n as calculated from the statistics model, we find that NsNsNS is much smaller than n, which mainly contributes to the concerns regarding surface heterogeneity in this study. The validity of the established model is tested and is proven as effective in the Anhui Province. This indicates that the estimated sample size from considering the scale effect and spatial heterogeneity in this study achieved the same accuracy as that calculated from a probability statistical model, while simultaneously saving more time, labour, and money in the accuracy assessment of a land cover dataset.

Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme.

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment.

The exact location of objects, such as infrastructure, is crucial to the systematic understanding of the built environment. The emergence and development of the Internet of Things (IoT) have attracted growing attention to the low-cost location scheme, which can respond to a dramatic increasing amount of public infrastructure in smart cities. Various Radio Frequency IDentification (RFID)-based locating systems and noise mitigation methods have been developed. However, most of them are impractical for built environments in large areas due to their high cost, computational complexity, and low noise detection capability. In this paper, we proposed a novel noise mitigation solution integrating the low-cost localization scheme with one mobile RFID reader. We designed a filter algorithm to remove the influence of abnormal data. Inspired the sampling concept, a more carefully parameters calibration was carried out for noise data sampling to improve the accuracy and reduce the computational complexity. To achieve robust noise detection results, we employed the powerful noise detection capability of the random sample consensus (RANSAC) algorithm. Our experiments demonstrate the effectiveness and advantages of the proposed method for the localization and noise mitigation in a large area. The proposed scheme has potential applications for location-based services in smart cities.

Research on 5-fluorouracil as a drug carrier materials with its in vitro release properties on organic modified magadiite.

The magadiite (MAG) was modified by cetyltrimethyl ammonium-Bromide (CTAB) and then further modified by Chitosan (CS) which is called organic modified-magadiite as magadiite-cetyltrimethyl ammonium bromide (MAG-CTAB) and magadiite-cetyltrimethyl ammonium bromide-Chitosan (MAG-CTAB-CS), respectively, in this research study. The MAG, MAG-CTAB, and MAG-CTAB-CS were used as 5-Fluorouracil (5-FU) drug carrier materials; the drug carrier's materials were marked as magadiite-5-Fluorouracil (MAG/5-FU), magadiite-cetyltrimethyl ammonium bromide-5-Fluorouracil (MAG-CTAB/5-FU), and magadiite-cetyltrimethyl ammonium bromide-Chitosan (MAG-CTAB-CS/5-FU). X-ray diffraction(XRD, Flourier transform infrared spectrometry (FTIR) and scanning electron microscopy (SEM) results were shown that 5-Fluorouracil was combined with carrier materials through physical apparent adsorption, ion exchange, chemical bond, hydrogen bond, and electrostatic interaction. The drug carriers in vitro release behavior in simulated gastric fluids (SGF，pH = 1.35) and intestinal fluids (SIF，pH = 7.40) were investigated. The drug loading capacity and accumulated release ration were as follows the order: MAG-CTAB-CS/5-FU > MAG-CTAB/5-FU > MAG/5-FU. The drug loading capacity of MAG-CTAB-CS/5-FU was 162.29 mg/g, 48 h later the drug accumulated release ratio was 61.24%, and the release amount was 97.52 mg/g for 24 h. Korsmeyer-Peppas model and First order model were found to be suitable to describe the vitro release behavior of 5-Fluorouracil. This would be an economically viable and efficient method for the preparation of advanced drug delivery system.

Effects on the Mechanical Properties of Nacre-Like Bio-Hybrid Membranes with Inter-Penetrating Petal Structure Based on Magadiite.

Rigid biological systems are increasingly becoming a source of inspiration for the fabrication of the advanced functional materials due to their diverse hierarchical structures and remarkable engineering properties. As a bionic biomaterial with a clear layered structure, excellent mechanical properties, and interesting rainbow colors, nacre has become one of the most attractive models for novel artificial materials design. In this research paper, the tough and strong nacre-like bio-hybrid membranes with an interpenetrating petals structure were fabricated from chitosan (CS) and magadiite (MAG) clay nanosheets through the gel-casting self-assembling method. The analyses from X-ray diffraction (XRD), scanning electron microscope (SEM), and observations of water droplets on membranes indicated that the nacre-like hybrid membranes had a layered compact structure. Fourier transforms infrared spectroscopy (FTIR) analyses suggested that the CS molecular chains formed chemical bonds and hydrogen bonds with MAG layers. The inter-penetrating petal layered structure had a good effect on the mechanical properties of a nacre-like bio-hybrid membranes and the tensile strength of the hybrid membranes could reach at 78.6 MPa. However, the transmission analyses of the results showed that the hybrid membranes still had a certain visible light transmittance. Finally, the hybrid membranes possessed an intriguing efficient fire-shielding property during exposure to the flame of alcohol burner. Consequently, the great biocompatibility and excellent mechanical properties of the bio-hybrid membranes with the special interpenetrating petals structure provides a great opportunity for these composites to be widely applied in biomaterial research.

Adsorption Analyses of Phenol from Aqueous Solutions Using Magadiite Modified with Organo-Functional Groups: Kinetic and Equilibrium Studies.

Organically-modified magadiite (MAG⁻CTAB⁻KH550) was synthesized via ion-exchange method and condensation reaction in the presence of pure magadiite (MAG), cetyltrimethylammonium bromide (CTAB) and γ-aminopropyltriethoxysilane (KH550) in aqueous solution in this research. This new adsorbent material was studied using scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), and N2 adsorption/desorption isotherms process. It was found that the MAG⁻CTAB⁻KH550 has high Brunaur-Emmet-Teller (BET) specific surface area and mesoporous pore size distribution which enhanced its ability to remove phenol in aqueous solution; and, the value of pH has a relatively large impact on the adsorption behavior of the sorbent. Finally, the adsorptive behavior of the mesoporous material on phenol was followed pseudo-second-order kinetic adsorption model. In contrast, the adsorption equilibrium isotherm was better performed Langmuir isotherm model than the Freundlich isotherm model; in addition, the results also showed that the MAG⁻CTAB⁻KH550 had a better adsorption capacity and removal efficiency than MAG.