Yushan Zhang

Northwestern Polytechnical University

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Mao‐Cheng Liu

Lanzhou University of Technology

Bin‐Mei Zhang

Harbin Medical University

Ling‐Bin Kong

Lanzhou University of Technology

Yuxia Hu

Northwestern Polytechnical University

Lu Chun

Nanjing Medical University

Chen‐Yang Tian

National Center for Nanoscience and Technology

Dong‐Ting Zhang

Lanzhou University of Technology

Chang‐Qing Xu

King Abdullah University of Science and Technology

Jin Yue

Institute of Apiculture Research

Tiantian Min

Zhengzhou University

Cooperative Institutions

Chinese Academy of Sciences

Lanzhou University of Technology

Fudan University

Northwestern Polytechnical University

Central South University

Nanjing Tech University

University of Chinese Academy of Sciences

Tsinghua University

Shanghai Jiao Tong University

Zhejiang University

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A Systematic Study on Transit Time and Its Impact on Accuracy of Concentration Measured by Microfluidic Devices

Sensors (2019)

Yushan Zhang Tianyi Guo Chang‐Qing Xu

Gating or threshold selection is very important in analyzing data from a microflow cytometer, which is especially critical in analyzing weak signals from particles/cells with small sizes. It has been reported that using the amplitude gating alone may result in false positive events in analyzing data with a poor signal-to-noise ratio. Transit time (τ) can be set as a gating threshold along with side-scattered light or fluorescent light signals in the detection of particles/cells using a microflow cytometer. In this study, transit time of microspheres was studied systematically when the microspheres passed through a laser beam in a microflow cytometer and side-scattered light was detected. A clear linear relationship between the inverse of the average transit time and total flow rate was found. Transit time was used as another gate (other than the amplitude of side-scattering signals) to distinguish real scattering signals from noise. It was shown that the relative difference of the measured microsphere concentration can be reduced significantly from the range of 3.43%–8.77% to the range of 8.42%–111.76% by employing both amplitude and transit time as gates in analysis of collected scattering data. By using optimized transit time and amplitude gate thresholds, a good correlation with the traditional hemocytometer-based particle counting was achieved (R2 > 0.94). The obtained results suggest that the transit time could be used as another gate together with the amplitude gate to improve measurement accuracy of particle/cell concentration for microfluidic devices.

Transit time

10.3390/s20010014

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Citations (11)

Modulate 1O2 by passivate oxygen vacancy to boosting the photocatalytic performance of Z-scheme Mo2S3/BiOCl heterostructure

Separation and Purification Technology (2021)

Huan Xia Hailan Qin Yushan Zhang Yin Hang Qiang Li

Passivation

10.1016/j.seppur.2021.118547

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Citations (41)

Reactive Deposition of Palladium Nanoparticles onto Zeolite Membranes in Supercritical CO₂

Industrial & Engineering Chemistry Research (2010)

Xiufeng Liu Wei Liu Jian Li Yushan Zhang Lin Lang

Pd/Si-MFI membranes are fabricated by depositing palladium nanoparticles onto Si-MFI membranes via opposing reactants chemical fluid deposition in supercritical carbon dioxide. Palladium hexafluoroacetylacetonate [Pd(hfac)2] and ethanol are used as the Pd precursor and the reducing agent, respectively, in the deposition process. The resulting Pd/Si-MFI membrane is characterized using SEM, XRD, EPMA, TEM, and EDX techniques. It is demonstrated that a continuous layer of Pd particles with size of 10 nm is covered on the matrix surface while 30-nm Pd particles are plugged into the defects within the matrix. The H2/N2 permselectivity has been significantly increased due to the deposition of Pd nanoparticles in the Si-MFI membrane, which could keep stable below the critical temperature. This bifunctional membrane and similar membranes possess the application potential to achieve various one-step syntheses.

Deposition

10.1021/ie100655m

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Electrospun polyvinyl alcohol/chitosan nanofibers incorporated with 1,8-cineole/cyclodextrin inclusion complexes: Characterization, release kinetics and application in strawberry preservation

Food Chemistry (2023)

Chuanxiang Cheng Tiantian Min Yuwan Luo Yushan Zhang Jin Yue

Polyvinyl Alcohol

Electrospinning

10.1016/j.foodchem.2023.135652

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Citations (42)

CCDC 1959969: Experimental Crystal Structure Determination

The Cambridge Structural Database (2020)

Mei Xue Yong Xiang Jian Wang Yushan Zhang Yao Yuehua

10.5517/ccdc.csd.cc23sht7

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Interpretation of Reflection and Colorimetry Characteristics of Indium-Particle Films by Means of Ellipsometric Modeling

Nanomaterials (2023)

Haotian Zhang Rong He Lei Peng Yu-Ting Yang Xiao-Jie Sun

It is of great technological importance in the field of plasmonic color generation to establish and understand the relationship between optical responses and the reflectance of metallic nanoparticles. Previously, a series of indium nanoparticle ensembles were fabricated using electron beam evaporation and inspected using spectroscopic ellipsometry (SE). The multi-oscillator Lorentz–Drude model demonstrated the optical responses of indium nanoparticles with different sizes and size distributions. The reflectance spectra and colorimetry characteristics of indium nanoparticles with unimodal and bimodal size distributions were interpreted based on the SE analysis. The trends of reflectance spectra were explained by the transfer matrix method. The effects of optical constants n and k of indium on the reflectance were demonstrated by mapping the reflectance contour lines on the n-k plane. Using oscillator decomposition, the influence of different electron behaviors in various indium structures on the reflectance spectra was revealed intuitively. The contribution of each oscillator on the colorimetry characteristics, including hue, lightness and saturation, were determined and discussed from the reflectance spectral analysis.

Colorimetry

Reflection

Particle (ecology)

10.3390/nano13030383

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Citations (4)

Interlayer Engineering Construction of 2D Nb₂CT_x with Enlarged Interlayer Spacing Towards High Capacity and Rate Capability for Lithium‐Ion Storage

Batteries & Supercaps (2021)

Mao‐Cheng Liu Bin‐Mei Zhang Yushan Zhang Dong‐Ting Zhang Chen‐Yang Tian

Abstract The Li + storage rate capability and diffusion dynamics in two‐dimensional (2D) materials are mainly determined by the interlayer spacing of materials. Investigating the effects of interlayer spacing on Li + diffusion rate in 2D materials can provide a theoretical guidance for developing the high rate 2D materials for Li + storage. Herein, a novel approach that P‐phenylenediamine (PPDA) electrostatically intercalated into Nb 2 CT x layers is employed to facilitate fast Li + diffusion dynamics and improve diffusion rate for Li + storage. The PPDA molecules existed between Nb 2 CT x layers have “support and dragline” effects on layers structure during Li + insertion/exaction. The PPDA‐Nb 2 CT x not only enlarges interlayer spacing ( d =1.27 nm) to accelerate the Li + diffusion rate, but also enhances the layered structure stability due to the “support and dragline” effects of PPDA molecules. The PPDA‐Nb 2 CT x exhibits the excellent capacity of 400 mAh g −1 at a current density of 0.1 A g −1 and displays a superior capacity retention as 70.2 % at 5.0 A g −1 compared with that of 0.5 A g −1 . The PPDA‐Nb 2 CT x //AC lithium ions hybrid capacitor (LIHC) delivers an excellent power density of 2754.8 W kg −1 at an energy density of 58.3 Wh kg −1 and a capacity retention with 80.0 % at 1.0 A g −1 after 1000 cycles. The interlayer engineering based on electrostatic intercalation provides a novel perspective to expand interlayer spacing, possessing a theoretical guidance for developing the Li + storage materials with high‐rate capability.

10.1002/batt.202100083

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Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks

IEEE Access (2019)

Jingxia Chen Ping Zhang Zijing Mao Yunran Huang Dongmei Jiang

In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotional feature learning and classification method using deep convolution neural network (CNN) was proposed based on temporal features, frequential features, and their combinations of EEG signals in DEAP dataset. The shallow machine learning models including bagging tree (BT), support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian linear discriminant analysis (BLDA) models and deep CNN models were used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results showed that the deep CNN models which require no feature engineering achieved the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension.

Binary classification

10.1109/access.2019.2908285

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Citations (207)

Large inorganic monolithic nanomaterials with a significant rigid hierarchical pore structure

Microporous and Mesoporous Materials (2021)

Huan Zhang Jianzhong Zhu Qiuzi Zhu Cunshi Wang Liang Chen

Nanomaterials

Shrinkage

Nanometre

10.1016/j.micromeso.2021.111099

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