Microplastics are a kind of new organic pollutant in the environment. In this study, the adsorption of tetracyclines (TCs), including tetracycline hydrochloride (TC), chlortetracycline hydrochloride ...(CTC) and oxytetracycline hydrochloride (OTC) onto polyethylene (PE) microplastics in aqueous solutions were investigated. The mechanism of the adsorption behavior was preliminarily explored by adsorption kinetics, isotherms, and thermodynamics, in combination with scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). In addition, molecular dynamics (MD) simulation was applied to investigate the adsorption processes of TCs on PE at a molecular level. It was found that the adsorption behaviors of TCs reached an equilibrium state within 30 h. The experimental data showed that adsorption capacities of TCs onto PE were as follows: OTC (64.40 ± 2.38 μg/g)>CTC (63.36 ± 4.92 μg/g)>TC (53.52 ± 3.43 μg/g). TC sorption onto PE increased with pH, peaking at around pH 6 and then decreased. The increase of ionic strength in the solution led to the reduced adsorption capacity of TC onto PE. The results indicated that the experimental data were well fitted by the pseudo-second-order model and the Freundlich isotherm model, indicating both monolayer and multilayer coverage of TCs onto the surface of PE. The results of MD simulation showed that PE can effectively adsorb the TCs molecule mainly through non-bond interactions, and PE exhibited the highest affinity for CTC and OTC, followed by TC.
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•TCs adsorption on PE microplastics investigated by experiments and molecular dynamics simulation.•TCs adsorption onto PE fitted well with Freundlich and pseudo-second-order model.•TCs adsorption onto PE could be influenced by pH and salinity.•TCs adsorption onto PE was mainly controlled by intermolecular van der Waals force.
Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are ...becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.
Multivariate data analysis (MVDA) is a powerful tool for analyzing complex datasets in biotechnology and bio-processing. The rapidly growing field of biotechnology involves the study and manipulation ...of living organisms for a range of applications, including healthcare, agriculture, and energy. Bio-processing, which involves the use of living organisms to manufacture products, is a critical component of biotechnology. This abstract explores the use of multivariate data analysis in biotechnology and bio-processing. The study highlights the various techniques used in MVDA, such as principal component analysis, cluster analysis, and partial least squares regression, and their applications in bioprocessing. The study reviews the potential of MVDA to improve the design and optimization of bioprocessing systems, including fermentation and downstream processing. It also discusses the benefits of using MVDA in bioprocessing, such as the ability to identify key process variables, reduce experimental time, and optimize product quality. The study concludes that MVDA is a powerful tool for the analysis of complex datasets in biotechnology and bio-processing, enabling the efficient and effective design and optimization of bioprocessing systems. The integration of MVDA techniques in bioprocessing can improve product quality, reduce costs, and accelerate the development of new biotechnology applications.
Drought stress is the most pervasive threat to plant growth, which predominantly encumbers turf grass growth by causing alterations in plant functions. This study appraised the role of nitrogen ...isotopes in providing a theoretical basis for developing and improving Kentucky bluegrass cultivar performance under drought stress. Nitrogen isotopes labelled
NH
Cl and K
NO
were prepared to replace KNO
in Hoagland's solution at concentrations of
NH
and
NO
at 1.5, 15, and 30 mM; the solutions were imposed on stressed plants under glasshouse conditions. Nitrogenous nutrition reduced oxidative stress by elevating the enzymatic activities and proline contents of all three clonal ramet leaves, particularly under stress conditions. Apart from nitrogen content, nitrogen isotope abundance, relative water content and water potential within controls were enhanced in treated with
NH
than in with
NO
in both the roots and leaves of Kentucky bluegrass. Nevertheless, an application of
NH
Cl and K
NO
at 30 mM had a positive influence to some extent on these attributes under drought stress. Overall, our results suggested that nitrogen isotopes contributed to drought tolerance in all three clonal ramets of Kentucky bluegrass by maintaining a better osmoprotectant and antioxidant defence system, which helped the plants eliminate reactive oxygen species.
Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean ...clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify and detect the position of corn seedlings and weeds, to reduce the harm of weeds on corn growth, and to achieve accurate fertilization, thereby realizing precise weeding or fertilizing. First, the image dataset for weed and corn seedling classification in the corn seedling stage was established. Second, many different features of corn seedlings and weeds were extracted, and dimensionality was reduced by principal component analysis, including the histogram of oriented gradient feature, rotation invariant local binary pattern (LBP) feature, Hu invariant moment feature, Gabor feature, gray level co-occurrence matrix, and gray level-gradient co-occurrence matrix. Then, the classifier training based on SVM was conducted to obtain the recognition model for corn seedlings and weeds. The comprehensive recognition performance of single feature or different fusion strategies for six features is compared and analyzed, and the optimal feature fusion strategy is obtained. Finally, by utilizing the actual corn seedling field images, the proposed weed and corn seedling detection method effect was tested. LAB color space and K-means clustering were used to achieve image segmentation. Connected component analysis was adopted to remove small objects. The previously trained recognition model was utilized to identify and label each connected region to identify and detect weeds and corn seedlings. The experimental results showed that the fusion feature combination of rotation invariant LBP feature and gray level-gradient co-occurrence matrix based on SVM classifier obtained the highest classification accuracy and accurately detected all kinds of weeds and corn seedlings. It provided information on weed and crop positions to the spraying herbicide robot for accurate spraying or to the precise fertilization machine for accurate fertilizing.
Nanocellulose products derived from different forms of biomass have significant importance in the modern era. This is due to its extraordinary physical characteristics, wide surface area as well as ...its biodegradability, which lead to being promising reinforcements as a nanomaterial. The nanomaterials which represent the cellulosic structures comprise nanocellulose reinforcements with biodegradable characteristics and tremendous ability to be used in eco-friendly applications to supplant fossil-based products. Meanwhile, the syntheses approach of such nanoscale structures still possesses challenging tasks at nanoscales. In addition, the virtuous distribution of nanocellulose in the hydrophobic polymer matrix has still difficulties to produce high-performance nanomaterials. Consequently, this study concludes many approaches and techniques to structural alteration of cellulosic materials to improve the distribution of nanocellulose to enhance the characteristics and features of nanocomposites. The macroscale and nanoscale cellulosic structures get popularity because of their high strength, stiffness, biodegradability, renewability, and use in the preparation of nanocomposites. Application of cellulose nanofibres for the production of nanocomposites is a relatively recent research field. Cellulose macro- and nanofibres can be used as insulation nanocomposite materials because of the improved mechanical, thermal, and biodegradation properties of nanocomposites. Cellulose fibres are hydrophilic, so it became important to improve their surface roughness for the production of nanocomposites with improved properties. This article includes the surface modifications of cellulose fibres by different methods as well as production processes, properties, and various applications of nanocellulose and cellulosic nanocomposites. A high thermal conductivity of cellulosic nanocomposite material for electronic devices can be obtained by combining cellulose nanofibrils (CNF) as the framework material with carbon nanotubes, graphene, and inorganic nitrides. Additionally, the research developments in this field with prospective applications of CNF-based materials for supercapacitors, lithium-ion batteries, and solar cells are emphasized. Moreover, the emerging challenges of different cellulosic nanofibrils-based energy storage devices have been discussed in this review paper.
Graphical abstract
The flame-retardant rigid polyurethane foams (RPUFs) with bis(2-hydroxyethyl)amino-methyl-phosphonic acid dimethyl ester (BH)/expandable graphite (EG) were prepared via box-foaming in our laboratory. ...The flame retardancy of RPUFs with BH and EG was characterized using the limiting oxygen index (LOI), cone calorimeter test. The results show that BH/EG system obviously increased the LOI value, decreased the heat release rate and mass loss rate, and enhanced the char yields of RPUFs. The results reveal that the addition flame-retardant effects from BH and EG. The flame-retardant mechanism of RPUFs was also detected using scan electronic microscopic and pyrolysis-gas chromatography/mass spectroscopy. According to the test results, BH promoted forming the firm phosphorus-containing char layer, which adhered the loose and worm-like expanded graphite in condense phase. Then, the compacter and thicker char layer was obtained. It will exert well obstructing property to fire in condensed phase. Moreover, BH also will generate dimethyl methylphosphonate (DMMP) gas and then be pyrolyzed to PO and PO2 free radicals in gaseous phase during combustion, which can quench the flammable free radicals from the matrix and terminate the free radical chain reaction of combustion. After the combination of the bi-phase flame-retardant effects, BH/EG flame-retardant system brought addition flame-retardant effects, and thus providing the better flame retardancy to matrix than one of them does. Moreover, the results of compressive strength, thermal conductivity and apparent density reveal that BH/EG/RPUF can meet the demand for the application in reality.
The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research ...status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology.
Circular RNAs (circRNAs), a subclass of non-coding RNAs, play essential roles in tumorigenesis and aggressiveness. Our previous study has identified that circAGO2 drives gastric cancer progression ...through activating human antigen R (HuR), a protein stabilizing AU-rich element-containing mRNAs. However, the functions and underlying mechanisms of circRNAs derived from HuR in gastric cancer progression remain elusive.
CircRNAs derived from HuR were detected by real-time quantitative RT-PCR and validated by Sanger sequencing. Biotin-labeled RNA pull-down, mass spectrometry, RNA immunoprecipitation, RNA electrophoretic mobility shift, and in vitro binding assays were applied to identify proteins interacting with circRNA. Gene expression regulation was observed by chromatin immunoprecipitation, dual-luciferase assay, real-time quantitative RT-PCR, and western blot assays. Gain- and loss-of-function studies were performed to observe the impacts of circRNA and its protein partner on the growth, invasion, and metastasis of gastric cancer cells in vitro and in vivo.
Circ-HuR (hsa_circ_0049027) was predominantly detected in the nucleus, and was down-regulated in gastric cancer tissues and cell lines. Ectopic expression of circ-HuR suppressed the growth, invasion, and metastasis of gastric cancer cells in vitro and in vivo. Mechanistically, circ-HuR interacted with CCHC-type zinc finger nucleic acid binding protein (CNBP), and subsequently restrained its binding to HuR promoter, resulting in down-regulation of HuR and repression of tumor progression.
Circ-HuR serves as a tumor suppressor to inhibit CNBP-facilitated HuR expression and gastric cancer progression, indicating a potential therapeutic target for gastric cancer.
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