Search

Two-dimensional nanomaterials are used in biosensors due to their unique physical and chemical properties, which have many advantages in the development of biosensors, such as improving the response speed, improving the detection sensitivity, shortening the detection time and simplifying the operation procedure. In recent years, they have shown great application prospects in the field of food safety detection and have gradually become a hot research topic. Based on an analysis of the application of two-dimensional nanomaterials including graphene, graphene alkyne, and graphene-like materials in the field of analytical sensing, this paper reviews the progress that has been made in the past five year in the application of two-dimensional nanomaterials in the detection of organic pollutants in foods, food additives, foodborne pathogens and heavy metals, and also presents an outlook on future prospects. This review is expected to provide ideas and theoretical references for researchers.

This study aimed to obtain a lentinan-specific fluorescent probe for the in situ visualization of lentinan. A carbohydrate-binding module with potential lentinan-binding capacity was discovered in a β-1,3-D-glucanase by using the bioinformatics techniques, and further recombinantly expressed. The binding specificity of the expressed protein, which was named as LBM was determined by the microtiter plate assays. The results indicated that LBM displayed a desired specificity for lentinan. The protein exhibited positive binding signals to lentinan, while could not bind to several examined polysaccharides including barley β-glucan, curdlan, or konjac glucomannan. Furthermore, the first lentinan-specific fluorescent probe was successfully constructed by fusing LBM with a green fluorescent protein EmGFP, based on which the in situ visualization of lentinan in Lentinula edodes was realized. It was shown that lentinan was not only presented in the mycelial cell wall, but also amorphously distributed in the interstitial space and inside the cell. The construction of the EmGFP-LBM provided a promising tool for the in situ visualization of lentinan. [ABSTRACT FROM AUTHOR]

Microfluidic chips hold vast potential for applications in fields such as DNA analysis, drug screening, cell culture and biosensing. However, microfluidic chips are susceptible to disruption and damage from bubbles. Current bubble removal methods suffer from limitations including narrow applicability, poor biocompatibility, limited bubble containment volume, low degassing rates, manufacturing complexity and integration challenges. Addressing these issues, this study presents the development of a bubble filtration microfluidic chip inspired by the stomatal pore structure in plant leaves, which prevents air propagation between conduits. Surface wettability theory and surface tension principles were utilized to investigate the mechanism of bubble filtration by plant stomatal pores. A cellulose/PVA hydrogel was introduced to enable the microfluidic chip to effectively filter bubbles. The design and in situ formation method of the bubble filtration chip were developed, and the chip was fabricated using micro-nanofabrication processes. Performance evaluation of the bubble filtration microfluidic chip demonstrated that with a gel film thickness of 20 μm, the microfluidic chip exhibits high gas leak threshold and minimal flow loss. The resulting biomimetic bubble filtration microfluidic chip features a simple structure, compact size, and independence from auxiliary equipment, making it suitable for portable microfluidic devices. [ABSTRACT FROM AUTHOR]

Intelligent electric shovels are being developed for intelligent mining in open‐pit mines. Complex environment detection and target recognition based on image recognition technology are prerequisites for achieving intelligent electric shovel operation. However, there is a large amount of sand–dust in open‐pit mines, which can lead to low visibility and color shift in the environment during data collection, resulting in low‐quality images. The images collected for environmental perception in sand–dust environment can seriously affect the target detection and scene segmentation capabilities of intelligent electric shovels. Therefore, developing an effective image processing algorithm to solve these problems and improve the perception ability of intelligent electric shovels has become crucial. At present, methods based on deep learning have achieved good results in image dehazing, and have a certain correlation in image sand–dust removal. However, deep learning heavily relies on data sets, but existing data sets are concentrated in haze environments, with significant gaps in the data set of sand–dust images, especially in open‐pit mining scenes. Another bottleneck is the limited performance associated with traditional methods when removing sand–dust from images, such as image distortion and blurring. To address the aforementioned issues, a method for generating sand–dust image data based on atmospheric physical models and CIELAB color space features is proposed. The impact mechanism of sand–dust on images was analyzed through atmospheric physical models, and the formation of sand–dust images was divided into two parts: blurring and color deviation. We studied the blurring and color deviation effect generation theories based on atmospheric physical models and CIELAB color space, and designed a two‐stage sand–dust image generation method. We also constructed an open‐pit mine sand–dust data set in a real mining environment. Last but not least, this article takes generative adversarial network (GAN) as the research foundation and focuses on the formation mechanism of sand–dust image effects. The CIELAB color features are fused with the discriminator of GAN as basic priors and additional constraints to improve the discrimination effect. By combining the three feature components of CIELAB color space and comparing the algorithm performance, a feature fusion scheme is determined. The results show that the proposed method can generate clear and realistic images well, which helps to improve the performance of target detection and scene segmentation tasks in heavy sand–dust open‐pit mines. [ABSTRACT FROM AUTHOR]

Activating transcription factor 4 (ATF4) is a fundamental basic region/leucine zipper transcription factor, responds to various stress signals, and plays crucial roles in normal metabolic and stress response processes. Although its functions in human health and disease are not completely understood, compelling evidence underscores ATF4 is indispensable for multiple stages and lineages of erythroid development, including the regulation of fetal liver hematopoietic stem cells, induction of terminal erythroid differentiation, and maintenance of erythroid homeostasis. β -Thalassemia is a blood disorder arising from mutations in the β -globin gene. Reactivating the expression of the γ -globin gene in adult patients has emerged as a promising therapeutic strategy for ameliorating clinical symptoms associated with β -thalassemia. Recent research has suggested that ATF4 contributes to decreased fetal hemoglobin (HbF) level through its binding to potent negative regulators of HbF, such as BCL11A and MYB. Notably, evidence also suggests a contrasting outcome where increased ATF4 protein levels are associated with enhanced HbF at the transcriptional level. Consequently, the identification of mechanisms that modulate ATF4-mediated γ -globin transcription and its effects on erythroid development may unveil novel targets for β -thalassemia treatment. [ABSTRACT FROM AUTHOR]

Nuclear batteries, a novel energy device in microelectromechanical systems (MEMS), have garnered significant attention from academia and industry due to their promising application prospects. They possess high energy density and reliable operation without human intervention and offer unique advantages in the case of long-term stable power supply. Among these, thermal conversion nuclear batteries (RTGs) represent the most mature technology and the earliest application, while betavoltaic nuclear batteries have entered commercialization. Challenges in betavoltaic nuclear batteries research include energy wastage due to the self-absorption effect of radioactive sources, low conversion efficiency, and significant radiation damage to transducer devices. These issues are attributable not only to the inherent properties of the radioactive source but also to the material and structural design of transducers. A 3D interface structure design scheme based on the wide bandgap semiconductor material GaN and the radioactive isotope 63Ni nuclear microbatteries is proposed. In the scheme, Geant4 and COMSOL Multiphysics were used to simulate the GaN-based betavoltaic nuclear battery of 63Ni source, and the PN junction 3D interface structure of the transducer was designed and optimized. The effects of the surface area, number of micropillars, thickness, and doping concentration of each region on the battery performance were analyzed. Results indicate that with P- and N- region thicknesses and doping concentrations at 0.1, 9.9 µm, 1 × 1018, and 1 × 1014 cm−3, respectively, the nuclear battery can achieve a conversion efficiency of 7.57%, a short-circuit current density of 0.3959 µA/cm2, an open-circuit voltage of 2.3074 V, and maximum output power of 0.7795 µW/cm2. In addition, discussion regarding the surface area and quantity of P-layer micropillars confirms the hypothesis that these variables are positively correlated with the output performance of the transducer. [ABSTRACT FROM AUTHOR]

Aerosols play an important role in the Earth system, but their impact on cloud properties and the resulting radiative forcing of climate remains highly uncertain. The large temporal and spatial variability of a number of aerosols properties and the choice of different 'pre-industrial' reference years prevent a concise understanding of basic underlying patterns and trends in aerosols and their impacts on clouds and radiation. In this study, we characterize the spatial patterns and long-term evolution of lower tropospheric aerosols (in terms of regimes) by clustering multiple aerosol properties from preindustrial times to the year 2050 under three different Shared Socioeconomic Pathway (SSP) scenarios, based on a combination of global aerosol model simulations and statistic-based machine learning algorithms. Our analysis suggests that in comparison with the present-day case, lower tropospheric aerosol regimes during preindustrial times are mostly represented by regimes of comparatively clean conditions whereby marked differences between the years 1750 and 1850 emerge due to the growing influence of agriculture and other anthropogenic activities in 1850. Key aspects of the spatial distribution and extent of the aerosol regimes identified in year 2050 differ compared to pre-industrial and present-day, with significant variations resulting from the emission scenario investigated. In 2050, the low emission SSP1-1.9 scenario is the only scenario where the spatial distribution and extent of the aerosol regimes very closely resembles preindustrial conditions whereby the similarity is greater compared to 1850 than 1750. The aerosol regimes for 2050 under SSP3-7.0 closely resemble present-day conditions, but there are some notable regional differences: developed countries tend to shift towards cleaner conditions in future, while the opposite is the case for developing countries. The aerosol regimes for 2050 under SSP2-4.5 represent an intermediate stage between preindustrial times and present-day. Further analysis indicates a north/south difference in the background regime during preindustrial times, and close resemblance of pre-industrial aerosol conditions in the marine regime to present-day conditions in the Southern Hemispheric ocean. Overall, our study allows to extract a clear and condensed picture of the spatial extent and distribution of aerosols for different time periods and emission scenarios and to summarize these in terms of aerosol regimes. The approach and findings of this study can be used for designing targeted measurements of different preindustrial-like conditions, and for tailored air pollution mitigation measures in specific regions. [ABSTRACT FROM AUTHOR]