Two-stage particle separation channel based on standing surface acoustic wave.

Microfluidic technology has great advantages in the precise manipulation of micro and nano particles, and the collection method of micro and nano particles based on ultrasonic standing waves has attracted much attention for its high efficiency and simplicity of structure. This article proposes a two-stage particle separation channel using ultrasound. In the microfluidic channel, two different sound pressure regions are used to achieve the separation of particles with positive acoustic contrast factors. Through numerical simulation, the performance of three common piezoelectric substrate materials was compared qualitatively and quantitatively, and it was found that the output sound pressure intensity of 128°YX-LiNbO3 was high and the output was stable. At the same time, the influence of the number of electrode pairs of the interdigital transducer and the electrode voltage on the output sound wave is studied. Finally, 15 pairs of electrode pairs are selected, and the electrode voltages of the two sound pressure regions are 2.0 V and 3.0 V, respectively. After selecting the corresponding parameters, the separation process was numerically simulated, and the separation of three kinds of particles was successfully achieved. This work has laid a certain theoretical foundation for rapid disease diagnosis and real-time monitoring of the environment in practical applications.

A Novel Bayes Approach to Impervious Surface Extraction from High-Resolution Remote Sensing Images.

Impervious surface as an evaluation indicator of urbanization is crucial for urban planning and management. It is necessary to obtain impervious surface information with high accuracy and resolution to meet dynamic monitoring under rapid urban development. At present, the methods of impervious surface extraction are primarily based on medium-low-resolution images. Therefore, it is of theoretical and application value to construct an impervious surface extraction method that applies to high-resolution satellite images and can solve the shadow misclassification problem. This paper builds an impervious surface extraction model by Bayes discriminant analysis (BDA). The Gaussian prior model is incorporated into the Bayes discriminant analysis to establish a new impervious surface extraction model (GBDA) applicable to high-resolution remote sensing images. Using GF-2 and Sentinel-2 remote sensing images as experimental data, we discuss and analyze the applicability of BDA and GBDA in impervious surface extraction of high-resolution remote sensing images. The results showed that the four methods, SVM, RF, BDA and GBDA, had OA values of 91.26%, 94.91%, 94.64% and 97.84% and Kappa values of 0.825, 0.898, 0.893 and 0.957, respectively, in the extraction results of GF-2. In the results of effective Sentinel-2 extraction, the OA values of the four methods were 87.94%, 91.79%, 92.19% and 93.51% and the Kappa values were 0.759, 0.836, 0.844 and 0.870, respectively. Compared with the support vector machine (SVM), random forest (RF) and BDA methods, GBDA has significantly improved the extraction accuracy. GBDA enhances the robustness and generalization ability of the model and can improve the shadow misclassification phenomenon of high-resolution images. The model constructed in this paper is highly reliable for extracting impervious surfaces from high-resolution remote sensing images, exploring the application value of Bayes discriminant analysis in impervious surface extraction and providing technical support for impervious surface information of high spatial resolution and high quality.

Polarization enhancement of linearly polarized light through foggy environments at UV-NIR wavelengths.

Wavelength is an essential factor affecting polarization propagation. We investigate the polarization persistence of linearly polarized light from ultraviolet to near-IR in foggy environments. Certain spectral bands, from ultraviolet to IR wavelengths that exhibit lower path loss, were initially selected. Using polarization-tracking Monte Carlo simulations for varying particle size, wavelength, refractive index and detection range, it is shown that linear polarization exhibits different persistence performance at different wavelengths in various foggy environments. For wet haze of 0.6 µm or 1 µm droplets, parallel polarization increases persistently as the wavelength increases, and has superior persistence in the near-IR region. For radiation fog of 5 µm or 7.5 µm droplets, parallel polarization shows superior persistence in the ultraviolet region. For advection fog of 15 µm or 45 µm droplets, parallel polarization shows a superior persistence in the ultraviolet region. It is therefore shown that changing the wavelength can improve linear polarization persistence in foggy environments.

Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area.

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet-, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models' type, layers, and sample set, based on tests with a large number of samples.

Light extraction enhancement and directional control of scintillator by using microlens arrays.

The total internal reflection restricts light extraction efficiency of scintillator, leading to reduced detection efficiency and signal-to-noise ratio in the field of scintillator-based radiation detection system. This research presents the method of applying microlens arrays to improve the light extraction efficiency as well as achieve directional control of emission for scintillators. For BGO (Bi4Ge3O12) scintillator covered with PMMA (polymethyl-methacrylate) hemispherical microlens array, the 2.59-fold in particular angle (θem = 45°) and overall 1.94-fold angle-integrated enhancement ratios have been obtained. Furthermore, we analyze and optimize some parameters of microlens arrays such as the packing arrangement, duty ratio, size, refractive index, and shape. As a result, when the refractive index of microlens is slightly larger than that of scintillator, a maximum 6.23-fold angle-integrated enhancements can be achieved. It can be concluded that the microlens array covered on scintillator has considerable value for practical applications on radiation detection.

Enhanced light extraction of plastic scintillator using large-area photonic crystal structures fabricated by hot embossing.

Plastic scintillators are widely used in various radiation measurement systems. However, detection efficiency and signal-to-noise are limited due to the total internal reflection, especially for weak signal detection situations. In the present investigation, large-area photonic crystals consisting of an array of periodic truncated cone holes were prepared based on hot embossing technology aiming at coupling with the surface of plastic scintillator to improve the light extraction efficiency and directionality control. The experimental results show that a maximum enhancement of 64% at 25° emergence angle along Γ-M orientation and a maximum enhancement of 58% at 20° emergence angle along Γ-K orientation were obtained. The proposed fabrication method of photonic crystal scintillator can avoid complicated pattern transfer processes used in most traditional methods, leading to a simple, economical method for large-area preparation. The photonic crystal scintillator demonstrated in this work is of great value for practical applications of nuclear radiation detection.

The microfabrication of mold for polymer microfluidic devices with Zr-based metallic glass.

Polymer microfluidic devices are used for many purposes such as microarrays and biochips. The key tool for manufacturing these chips in bulk is an appropriate mold. However, the popular material for making molds is nickel or nickel alloys, which have low stiffness and wear out easily. Zr-based metallic glass is a promising material for micro- or nanomolds because it has good mechanical properties and can be easily formed with high precision. In this paper, Zr-based metallic glass is proposed for use as micromold insert to make poly-(methyl methacrylate) (PMMA) microfluidic devices. Our experiments show that they have good feature integrity and replication quality. Microchannels we fabricated using these replicas did not leak and had good flow performance. Zr-based metallic glass can greatly ease the manufacture of plastic microfluidic devices for research and commercial applications.

Applications and theory of electrokinetic enrichment in micro-nanofluidic chips.

This review reports the progress on the recent development of electrokinetic enrichment in micro-nanofluidic chips. The governing equations of electrokinetic enrichment in micro-nanofluidic chips are given. Various enrichment applications including protein analysis, DNA analysis, bacteria analysis, viruses analysis and cell analysis are illustrated and discussed. The advantages and difficulties of each enrichment method are expatiated. This paper will provide a particularly convenient and valuable reference to those who intend to research the electrokinetic enrichment based on micro-nanofluidic chips.

A novel design for passive misscromixers based on topology optimization method.

In this paper, a series of novel passive micromixers, called topological micromixers with reversed flow (TMRFX), are proposed. The reversed flow in the microchannels can enhance chaotic advection and produce better mixing performance. Therefore the maximum of reversed flow is chosen as the objective function of the topology optimization problem. Because the square-wave unit is easier to fabricate and have better mixing performance than many other serpentine micromixers, square-wave structure becomes the original geometry structure. By simulating analysis, the series of TMRFX, namely TMRF, TMRF0.75, TMRF0.5, TMRF0.25, mix better than the square-wave micromixer at various Reynolds numbers (Re), but pressure drops of TMRFX are much higher. Lots of intensive numerical simulations are conducted to prove that TMRF and TMRF0.75 have remarkable advantages on mixing over other micromixers at various Re. The mixing performance of TMRF0.75 is similar to TMRF's. What's more, TMRF have a larger pressure drop than TMRF0.75, which means that TMRF have taken more energy than TMRF0.75. For a wide range of Re (Re ≤ 0.1 and Re ≥ 10), TMRF0.75 delivers a great performance and the mixing efficiency is greater than 95 %. Even in the range of 0.1-10 for the Re, the mixing efficiency of TMRF0.75 is higher than 85 %.

Manufacturing methods and applications of membranes in microfluidics.

Applications of membranes in microfluidics solved many thorny problems for analytical chemistry and bioscience, so that the use of membranes in microfluidics has been a topic of growing interest. Many different examples have been reported, demonstrating the versatile use of membranes. This work reviews a lot of applications of membranes in microfluidics. Membranes in microfluidics for applications including chemical reagents detection, gas detection, drug screening, cell, protein, microreactor, electrokinetical fluid, pump and valve and fluid transport control and so on, have been analyzed and discussed. In addition, the definition and basic concepts of membranes are summed up. And the methods of manufacturing membranes in microfluidics are discussed. This paper will provide a helpful reference to researchers who want to study applications of membranes in microfluidics.

PMMA microreactor for chemiluminescence detection of Cu (II) based on 1,10-Phenanthroline-hydrogen peroxide reaction.

A microreactor for the chemiluminescence detection of copper (II) in water samples, based on the measurement of light emitted from the copper (II) catalysed oxidation of 1,10-phenanthroline by hydrogen peroxide in basic aqueous solution, is presented. Polymethyl methacrylate (PMMA) was chose as material for fabricating the microreactor with mill and hot bonding method. Optimized reagents conditions were found to be 6.3 × 10(-5)mol/L 1,10-phenanthroline, 1.5 × 10(-3)mol/L hydrogen peroxide, 7.0 × 10(-2)mol/L sodium hydroxide and 2.4 × 10(-5)mol/L Hexadecyl trimethyl ammonium Bromide (CTMAB). In the continuous flow injection mode the system can perform fully automated detection with a reagent consumption of only 3.5 µL each time. The linear range of the Cu (II) ions concentration was 1.5 × 10(-8) mol/L to 1.0 × 10(-4) mol/L, and the detection limit was 9.4 × 10(-9)mol/L with the S/N ratio of 4. The relative standard deviation was 3.0 % for 2.0 × 10(-6) mol/L Cu (II) ions (n = 10). The most obvious features of the detection method are simplicity, rapidity and easy fabrication of the microreactor.

A novel electrophoretic assisted hydrophobic microdevice for enhancing blood cell sorting: design and numerical simulation.

Microfluidic technology has great advantages in the precise manipulation of micro-nano particles, and the hybrid microfluidic separation technology has attracted much attention due to the advantages of both active and passive separation technology at the same time. In this paper, the hydrophoresis sorting technique is combined with the dielectrophoresis technique, and a dielectrophoresis-assisted hydrophoresis microdevice is studied to separate blood cells. By using the dielectrophoresis force to change the suspension position of the cells in the channel, the scope of the hydrophoresis device for sorting particles is expanded. At the same time, the effects of microchannel width, fluid velocity, and electrode voltage on cell sorting were discussed, and the cell separation process was simulated. This work has laid a certain theoretical foundation for the rapid diagnosis of diseases in practical applications.

Bionic Spider Web Flexible Strain Sensor Based on CF-L and Machine Learning.

At present, the preparation of laser-induced graphene (LIG) has become an important technology in sensor manufacturing. In the conventional preparation process, the CO2 laser is widely used; however, its experimental period is long and its efficiency needs to be improved. We propose an innovative strategy to improve the experimental efficiency. We use the machine learning method to accurately predict the preparation parameters of LIG, so as to optimize the experimental process. Different structures can lead to different sensor performances. The structure constructed by the CO2 laser is rough and has a large size, which can affect the performance of the sensor. Therefore, we propose for the first time an innovative method for intramembrane structure construction that combines the advantages of the CO2 laser and fiber laser (CF-L). With this CF-L method, we have successfully prepared a biomimetic, flexible strain sensor. This sensor not only maintains a high degree of sensitivity, but also has a more refined and optimized structure. The manufacturing process of the whole sensor is simple, economical, and durable and can be prepared in large quantities and can be used to detect the extension and bending of human joints.

Assessment of network structure characteristics and factors of corporate flows in Guangdong Province.

With the rapid development of the world city network, the traditional location theory has gradually been disproven, and the advantages of the flow space over the traditional vertical organizational structure are gradually being revealed. Therefore, from corporate branch networks and corporate investment networks, 21 cities in urban agglomerations of Guangdong are taken as case studies for this paper. Furthermore, in this paper, 5 representative types of corporate contact data (catering service, financial service, life service, sports and leisure and accommodation service) are selected, the social network analysis (SNA) method is used to quantitatively analyze the network structure characteristics of urban agglomerations, and a spatial interaction model is constructed to explore the factors influencing. The results indicate that secondary networks have developed in Guangdong. The financial service network is the most complex, followed by the life services, sports and leisure and catering networks. The accommodation service network structure is the simplest. Among all kinds of networks, Guangzhou and Shenzhen have the highest status. The catering and accommodation corporations in Yangjiang in the west have a relatively major external development. Shantou in the east has many branches of various types, while most of the capital exchange in the region is concentrated in Heyuan and Qingyuan in the north. The coefficients of geographical proximity and the urban development level play a significant role in promoting the development of networks. However, administrative capacity limits the attractiveness of origin cities to a certain extent.

Correction: Assessment of network structure characteristics and factors of corporate flows in Guangdong Province.

[This corrects the article DOI: 10.1371/journal.pone.0293870.].

New insights into the micromixer with Cantor fractal obstacles through genetic algorithm.

This work is mainly to combine fractal principle with multi-objective genetic algorithm, and the multi-objective optimization of the Cantor fractal baffle micromixer is carried out. At different Reynolds numbers (Res), the three-dimensional Navier-Stokes equation is employed to numerically analyze the fluid flow and mixing in the microchannel. We choose the ratio of the three parameters associated with the geometry of the micromixer as design variables, and take the mixing index and pressure drop at the outlet of the micromixer as two objective functions for optimization. For the parameter study of the design space, the Latin hypercube sampling (LHS) method is used as an experimental design technique, and it is used to select design points in the design space. We use the proxy modeling of the response surface analysis (RSA) to approximate the objective function. The genetic algorithm is used to get the Pareto optimal frontier of the micromixer. K-means clustering is used to classify the optimal solution set, and we select representative design variables from it. Through multi-objective optimization, when Re = 1 and 10, the optimized mixing efficiency of the micromixer increased by 20.59% and 14.07% compared with the reference design, respectively. And we also prove that this multi-objective optimization method is applicable to any Res.

Intelligent control of nanoparticle synthesis through machine learning.

The synthesis of nanoparticles is affected by many reaction conditions, and their properties are usually determined by factors such as their size, shape and surface chemistry. In order for the synthesized nanoparticles to have functions suitable for different fields (for example, optics, electronics, sensor applications and so on), precise control of their properties is essential. However, with the current technology of preparing nanoparticles on a microreactor, it is time-consuming and laborious to achieve precise synthesis. In order to improve the efficiency of synthesizing nanoparticles with the expected functionality, the application of machine learning-assisted synthesis is an intelligent choice. In this article, we mainly introduce the typical methods of preparing nanoparticles on microreactors, and explain the principles and procedures of machine learning, as well as the main ways of obtaining data sets. We have studied three types of representative nanoparticle preparation methods assisted by machine learning. Finally, the current problems in machine learning-assisted nanoparticle synthesis and future development prospects are discussed.

Numerical simulation of heat transfer and flow of Al2O3-water nanofluid in microchannel heat sink with cantor fractal structure based on genetic algorithm.

The heat generated during operation of microelectronic devices often adversely affects product performance. A remedy for this problem can be through the incorporation of nanofluidic microchannel heat sinks. Advanced working fluids and channel structures can be used to improve the heat transfer performance of the microchannel heat sink. First, thermal resistance is treated as a single objective function, the geometry of the microchannel was optimized using a genetic algorithm. The flow and heat transfer characteristics of the optimized microchannel are analyzed. The effect of different nanofluid volume fractions and geometric parameters on the inlet and outlet pressure drop, flow resistance coefficient, substrate temperature, Nusselt number (Nu), and system thermal resistance in the fractal microchannel are investigated. The thermal resistance of Al2O3 nanofluid with a volume fraction of 5% is 12.5-14.7% lower than that of deionized water, and the microchannel substrate temperature is 6.26 °C lower than that of deionized water.

Modeling of spatial pattern and influencing factors of cultivated land quality in Henan Province based on spatial big data.

The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I≈0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I≈-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province.

Spatio-temporal evolution characteristics analysis and optimization prediction of urban green infrastructure: a case study of Beijing, China.

The reasonable layout of green infrastructure is conducive to the low-carbon, livable and high-quality sustainable development of cities. The framework of spatio-temporal evolution characteristics and prediction analysis of Urban Green Infrastructure (UGI) was constructed by integrating morphological spatial pattern analysis (MSPA) and CA-Markov in the study. We analyzed the spatio-temporal evolution characteristics of UGI in Beijing from 1990 to 2019, predicted its future change trend in 2030, and put forward the optimization scheme for the ecological network of UGI. The area change of UGI presented a "V" shape from 1990 to 2019 in Beijing, and the turning point was around 2009. Its spatial distribution revealed a significant heterogeneity. The comprehensive change rate index showed a "rising and then falling" trend from 1990 to 2019. Core with an area of over 1000 km2 had inclined "C" shape, connecting the north, west and south of the study area. Among the three prediction scenarios for 2030, the area of UGI under the ecological conservation priority scenario is the largest, accounting for 86.35% of the total area. The area of UGI under the economic development priority scenario is the smallest, accounting for 76.85%. The optimization of zoning and road network are effective measures to improve the connectivity of UGI in Beijing. This study is beneficial to extend the research ideas of UGI and promote sustainable urban development.