A resonant high-pressure microsensor based on a composite pressure-sensitive mechanism of diaphragm bending and volume compression.

In this paper, a composite pressure-sensitive mechanism combining diaphragm bending and volume compression was developed for resonant pressure microsensors to achieve high-pressure measurements with excellent accuracy. The composite mechanism was explained, and the sensor structure was designed based on theoretical analysis and finite element simulation. An all-silicon resonant high-pressure microsensor with multiple miniaturized cavities and dual resonators was developed, where dual resonators positioned in two resonant cavities with suitably different widths are used to perform opposite characteristics in pressure and the same characteristics at different temperatures, which can improve pressure sensitivities and realize temperature self-compensation by differential frequency output. The microsensor was fabricated by microfabrication, and the experimental results showed that the sensor had an accuracy of ±0.015% full scale (FS) in a pressure range of 0.1~100 MPa and a temperature range of -10~50 °C. The pressure sensitivity of the differential frequency was 261.10 Hz/MPa (~2523 ppm/MPa) at a temperature of 20 °C, and the temperature sensitivities of the dual resonators were -1.54 Hz/°C (~-14.5 ppm/°C) and -1.57 Hz/°C (~-15.6 ppm/°C) at a pressure of 2 MPa. The differential output had an outstanding stability within ±0.02 Hz under constant temperature and pressure. Thus, this research provides a convenient solution for high-pressure measurements because of its advantages, namely, large range, excellent accuracy and stability.

A MEMS Electrochemical Angular Accelerometer with Silicon-Based Four-Electrode Structure.

This paper presents a MEMS electrochemical angular accelerometer with a silicon-based four-electrode structure, which was made of thousands of interconnected microchannels for electrolyte flow, anodes uniformly coated on structure surfaces and cathodes located on the sidewalls of flow holes. From the perspective of device fabrication, in this study, the previously reported multi-piece assembly was simplified into single-piece integrative manufacturing, effectively addressing the problems of complex assembly and manual alignment. From the perspective of the sensitive structure, in this study, the silicon-based four-electrode structure featuring with complete insulation layers between anodes and cathodes can enable fast electrochemical reactions with improved sensitivities. Numerical simulations were conducted to optimize the geometrical parameters of the silicon-based four-electrode structure, where increases in fluid resistance and cathode area were found to expand working bandwidths and improve device sensitivity, respectively. Then, the silicon-based four-electrode structure was fabricated by conventional MEMS processes, mainly composed of wafer-level bonding and wafer-level etching. As to device characterization, the MEMS electrochemical angular accelerometer with the silicon-based four-electrode structure exhibited a maximum sensitivity of 1458 V/(rad/s2) at 0.01 Hz and a minimum noise level of -164 dB at 1 Hz. Compared with previously reported electrochemical angular accelerometers, the angular accelerometer developed in this study offered higher sensitivities and lower noise levels, indicating strong potential for applications in the field of rotational seismology.

Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study.

The gold standard of leukocyte differentiation is a manual examination of blood smears, which is not only time and labor intensive but also susceptible to human error. As to automatic classification, there is still no comparative study of cell segmentation, feature extraction, and cell classification, where a variety of machine and deep learning models are compared with home-developed approaches. In this study, both traditional machine learning of K-means clustering versus deep learning of U-Net, U-Net + ResNet18, and U-Net + ResNet34 were used for cell segmentation, producing segmentation accuracies of 94.36% versus 99.17% for the dataset of CellaVision and 93.20% versus 98.75% for the dataset of BCCD, confirming that deep learning produces higher performance than traditional machine learning in leukocyte classification. In addition, a series of deep-learning approaches, including AlexNet, VGG16, and ResNet18, was adopted to conduct feature extraction and cell classification of leukocytes, producing classification accuracies of 91.31%, 97.83%, and 100% of CellaVision as well as 81.18%, 91.64% and 97.82% of BCCD, confirming the capability of the increased deepness of neural networks in leukocyte classification. As to the demonstrations, this study further conducted cell-type classification of ALL-IDB2 and PCB-HBC datasets, producing high accuracies of 100% and 98.49% among all literature, validating the deep learning model used in this study.

Electrical micro flow cytometry with LSTM and its application in leukocyte differential.

This paper developed an electrical micro flow cytometry to realize leukocyte differentials leveraging a constrictional microchannel and a deep neural network. Firstly, purified granulocytes, lymphocytes or monocytes traveled through the constrictional microchannel with a cross-sectional area marginally larger than individual cells and produced large impedance variations by blocking focused electric field lines. By optimizing key elements (e.g., normalization, learning rate, batch size and neuron number) of the recurrent neural network (RNN), electrical results of purified leukocytes were analyzed to establish a leukocyte differential system with a classification accuracy of 95.2%. Then the leukocyte mixtures were forced to travel through the same constrictional microchannel, producing mixed impedance profiles which were classified into granulocytes, lymphocytes and monocytes based on the aforementioned differential system. As to the classification results, two leukocyte mixtures from the same donor were processed, producing comparable classification results, which were 57% versus 59% of granulocytes, 37% versus 34% of lymphocytes and 6% versus 7% of monocytes. These results validated the established classification system based on the constrictional microchannel and the recurrent neural network, providing a new perspective of differentiating white blood cells by electrical flow cytometry.

Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties.

This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels with constricted cross-sectional areas, they effectively blocked concentrated electric field lines, producing large impedance variations. Meanwhile, the traveling cells were confined within the cross-sectional areas of the constrictional microchannels, enabling the capture of high-quality images without losing focuses. Then single-cell features from impedance profiles and optical images were extracted from customized recurrent and convolution networks (RNN and CNN), which were further fused for cell-type classification based on support vector machines (SVM). As a demonstration, two leukemia cell lines (e.g., HL60 vs. Jurkat) were analyzed, producing high-classification accuracies of 99.3% based on electrical features extracted from Long Short-Term Memory (LSTM) of RNN, 96.7% based on structural features extracted from Resnet18 of CNN and 100.0% based on combined features enabled by SVM. The microfluidic flow cytometry developed in this study may provide a new perspective for the field of single-cell analysis.

Leukocyte differential based on an imaging and impedance flow cytometry of microfluidics coupled with deep neural networks.

The differential of leukocytes functions as the first indicator in clinical examinations. However, microscopic examinations suffered from key limitations of low throughputs in classifying leukocytes while commercially available hematology analyzers failed to provide quantitative accuracies in leukocyte differentials. A home-developed imaging and impedance flow cytometry of microfluidics was used to capture fluorescent images and impedance variations of single cells traveling through constrictional microchannels. Convolutional and recurrent neural networks were adopted for data processing and feature extractions, which were then fused by a support vector machine to realize the four-part differential of leukocytes. The classification accuracies of the four-part leukocyte differential were quantified as 95.4% based on fluorescent images plus the convolutional neural network, 90.3% based on impedance variations plus the recurrent neural network, and 99.3% on the basis of fluorescent images, impedance variations, and deep neural networks. Based on single-cell fluorescent imaging and impedance variations coupled with deep neural networks, the four-part leukocyte differential can be realized with almost 100% accuracy.

A novel evolutionary method for parameter-free MEMS structural design and its application in piezoresistive pressure sensors.

In this paper, a novel simulation-based evolutionary method is presented for designing parameter-free MEMS structures with maximum degrees of freedom. This novel design method enabled semiautomatic structure evolution by weighing the attributes of each segment of the structure and yielded an optimal design after multiple iterations. The proposed method was utilized to optimize the pressure-sensitive diaphragm of a piezoresistive pressure sensor (PPS). Finite element method (FEM) simulations revealed that, in comparison to conventional diaphragms without islands and with square islands, the optimized diaphragm increased the stress by 10% and 16% and reduced the nonlinearity by 57% and 77%, respectively. These improvements demonstrate the value of this method. Characterization of the fabricated PPS revealed a high sensitivity of 8.8 mV V-1 MPa-1 and a low nonlinearity of 0.058% FS at 20 °C, indicating excellent sensor performance.

Development of a Microfluidic Flow Cytometer with a Uniform Optical Field (Uni-µFCM) Enabling Quantitative Analysis of Single-Cell Proteins and Its Applications in Leukemia Gating, Tumor Classification, and Hierarchy of Cancer Stem Cells.

Fast and quantitative estimation of single-cell proteins with various distribution patterns remains a technical challenge. Here, a microfluidic flow cytometer with a uniform optical field (Uni-µFCM) was developed, which enabled the translation of multicolor fluorescence signals of bound antibodies into targeted protein numbers with arbitrary distributions of biological cells. As the core of Uni-µFCM, a uniform optical field for optical excitation and fluorescence detection was realized by adopting a microfabricated metal window to shape the optical beam for excitation, which was modeled and validated by both numerical simulation and experimental characterization. After the validation of Uni-µFCM in single-cell protein quantification by measuring single-cell expressions of three transcriptional factors from four cell lines of variable sizes and origins, Uni-µFCM was applied to (1) quantify membrane and cytoplasmic markers of myeloid and lymphocytic leukocytes to classify cell lines and normal and patient blood samples; (2) measure single-cell expressions of key cytokines affiliated with gene stabilities, differentiating paired oral and colon tumor cell lines with varied malignancies, and (3) quantify single-cell stemming markers of liver tumor cell lines, cell subtypes, and liver patient samples to determine a variety of lineage hierarchy. By quantitatively assessing complex cellular phenotypes, Uni-µFCM substantially expanded the phenotypic space accessible to single-cell applications in leukemia gating, tumor classification, and hierarchy determination of cancer stem cells.

Effects of Mud Content on the Setting Time and Mechanical Properties of Alkali-Activated Slag Mortar.

High mud content in the sand has a negative impact on cement mortar but there is little research on Alkali-activated slag (AAS) mortar. In order to explore the impacts of mud content in the sand on the performance of AAS mortar, this paper used sand that contains silt, clay, and a mixture of silt and clay; tested the setting time of AAS with different mud contents of 0%, 2%, 4%, 6%, 8%, and 10%; and measured the unconfined compressive strength and beam flexural strength of 3 d, 7 d, and 28 d AAS mortar specimens. The microstructure of AAS mortar with different kinds of mud was observed by scanning electron microscope (SEM), the elemental composition of the hydration product was tested by energy dispersive spectroscopy (EDS), and the AAS interaction mechanism with different kinds of mud was analyzed. The main conclusions are: the higher the mud content in the sand, the shorter the initial setting time and the longer the final setting time of AAS, mainly because the mud in the sand affects the hydration process; mud content above 4% causes a rapid decrease in the compressive and flexural strengths of AAS mortar, mainly because the mud affects the hydration process and hinders the bonding of the hydration product with the sand. When there is no mud in the sand, the main hydration product of AAS is dense calcium-alumina-silicate-hydrate (C-A-S-H) gel. When the sand contains silt, the hydration product of AAS is loose C-A-S-H gel. When the sand contains clay, the hydration products of AAS contain C-A-S-H gel and a small amount of sodium-aluminum-silicate-hydrate (N-A-S-H), and needle-like crystals. Loose gel and crystals have a negative effect on the AAS mortar strength.

Development of constrictional microchannels and the recurrent neural network in single-cell protein analysis.

Introduction: As the golden approach of single-cell analysis, fluorescent flow cytometry can estimate single-cell proteins with high throughputs, which, however, cannot translate fluorescent intensities into protein numbers. Methods: This study reported a fluorescent flow cytometry based on constrictional microchannels for quantitative measurements of single-cell fluorescent levels and the recurrent neural network for data analysis of fluorescent profiles for high-accuracy cell-type classification. Results: As a demonstration, fluorescent profiles (e.g., FITC labeled ß-actin antibody, PE labeled EpCAM antibody and PerCP labeled ß-tubulin antibody) of individual A549 and CAL 27 cells were firstly measured and translated into protein numbers of 0.56 ± 0.43 × 104, 1.78 ± 1.06 × 106 and 8.11 ± 4.89 × 104 of A549 cells (ncell = 10232), and 3.47 ± 2.45 × 104, 2.65 ± 1.19 × 106 and 8.61 ± 5.25 × 104 of CAL 27 cells (ncell = 16376) based on the equivalent model of the constrictional microchannel. Then, the feedforward neural network was used to process these single-cell protein expressions, producing a classification accuracy of 92.0% for A549 vs. CAL 27 cells. In order to further increase the classification accuracies, as a key subtype of the recurrent neural network, the long short-term memory (LSTM) neural network was adopted to process fluorescent pulses sampled in constrictional microchannels directly, producing a classification accuracy of 95.5% for A549 vs. CAL 27 cells after optimization. Discussion: This fluorescent flow cytometry based on constrictional microchannels and recurrent neural network can function as an enabling tool of single-cell analysis and contribute to the development of quantitative cell biology.

Effects of Borax, Sucrose, and Citric Acid on the Setting Time and Mechanical Properties of Alkali-Activated Slag.

The setting time of alkali-activated slag (AAS) binders is extremely short, while traditional retarders of Portland cement may be invalid for AAS. To find an effective retarder with a less negative impact on strength, borax (B), sucrose (S), and citric acid (CA) were selected as potential retarders. The setting time of AAS with different admixtures dosages of 0%, 2%, 4%, 6%, and 8%, and the unconfined compressive strength and beam flexural strength of 3 d, 7 d, and 28 d AAS mortar specimens were tested. The microstructure of AAS with different additives was observed by scanning using an electron microscope (SEM), and the hydration products were analyzed by energy dispersive spectroscopy (EDS), X-ray diffraction analysis (XRD), and thermogravimetric analysis (DT-TGA) to explain the retarding mechanism of AAS with different additives. The results showed that the incorporation of borax and citric acid could effectively prolong the setting time of AAS more than that of sucrose, and the retarding effect is more and more obvious with the increase in borax and citric acid dosages. However, sucrose and citric acid negatively influence AAS's unconfined compressive strength and flexural stress. The negative effect becomes more evident with the increase in sucrose and citric acid dosages. Borax is the most suitable retarder for AAS among the three selected additives. SEM-EDS analysis showed that the incorporation of borax does three things: produces gels, covers the surface of the slag, and slows down the hydration reaction rate.

An All-Silicon Resonant Pressure Microsensor Based on Eutectic Bonding.

In this paper, an all-Si resonant pressure microsensor based on eutectic bonding was developed, which can eliminate thermal expansion coefficient mismatches and residual thermal stresses during the bonding process. More specifically, the resonant pressure microsensor included an SOI wafer with a pressure-sensitive film embedded with resonators, which was eutectically bonded with a silicon cap for vacuum encapsulation. The all-Si resonant pressure microsensor was carefully designed and simulated numerically, where the use of the silicon cap was shown to effectively address temperature disturbances of the microsensor. The microsensor was then fabricated based on MEMS processes where eutectic bonding was adopted to link the SOI wafer and the silicon cap. The characterization results showed that the temperature disturbances of the resonant pressure microsensor encapsulated with the silicon cap were quantified as -0.82 Hz/°C of the central resonator and -2.36 Hz/°C of the side resonator within a temperature range from -40 °C to 80 °C, which were at least eight times lower than that of the microsensor encapsulated with the glass cap. Compared with the microsensor using the glass cap, the all-silicon microsensor demonstrated an accuracy improvement from 0.03% FS to 0.01% FS and a reduction in short-term frequency fluctuations from 3.2 Hz to 1.5 Hz.

Quantitative flow cytometry leveraging droplet-based constriction microchannels with high reliability and high sensitivity.

This study presented a quantitative flow cytometry leveraging droplet-based constriction microchannels with high reliability and high sensitivity. Droplets encapsulating single cells and even distribution of fluorescein labeled antibodies removed from targeted cells deformed through the constriction microchannel where the excited fluorescent signals were sampled and interpreted into numbers of proteins based on volume equivalence in measurement of droplets and calibration of fluorescence. To improve the detection reliability, a comprehensive analysis and comparison of multiple stripping agents such as proteinase K, guanidine hydrochloride, and urea was conducted. To improve the detection sensitivity, light modulation was used to address electrical noises and quartz microchannels were fabricated to address optical noises. As a demonstration, based on this quantitative flow cytometry of droplet microfluidics, (1) mutant p53 expressions of single cells were quantified as 1.95 ± 0.60 × 105 (ncell  = 2918 of A431) and 1.30 ± 0.70 × 105 (ncell  = 3954 of T47D); (2) single-cell expressions of Ras, c-Myc, and ß-tubulin were quantified as 1.90 ± 0.59 × 105 , 4.39 ± 1.44 × 105 , and 2.97 ± 0.81 × 105 (ncell  = 3298 of CAL 27), 1.83 ± 0.58 × 105 , 2.08 ± 0.13 × 106 , and 1.96 ± 0.74 × 105 (ncell  = 5459 of WSU-HN6). As a microfluidic tool capable of quantitatively estimating single-cell protein expressions, this methodology may provide a new quantitative perspective for the field of flow cytometry.

Toward five-part differential of leukocytes based on electrical impedances of single cells and neural network.

The five-part differential of leukocytes plays key roles in the diagnosis of a variety of diseases and is realized by optical examinations of single cells, which is prone to various artifacts due to chemical treatments. The classification of leukocytes based on electrical impedances without cell treatments has not been demonstrated because of limitations in approaches of impedance acquisition and data processing. In this study, based on treatment-free single-cell impedance profiles collected from impedance flow cytometry leveraging constriction microchannels, two types of neural pattern recognition were conducted for comparisons with the purpose of realizing the five-part differential of leukocytes. In the first approach, 30 features from impedance profiles were defined manually and extracted automatically, and then a feedforward neural network was conducted, producing a classification accuracy of 84.9% in the five-part leukocyte differential. In the second approach, a customized recurrent neural network was developed to process impedance profiles directly and based on deep learning, a classification accuracy of 97.5% in the five-part leukocyte differential was reported. These results validated the feasibility of the five-part leukocyte differential based on label-free impedance profiles of single cells and thus provide a new perspective of differentiating white blood cells based on impedance flow cytometry.

Multiplex fluorescence detection of single-cell droplet microfluidics and its application in quantifying protein expression levels.

This study presented a platform of multiplex fluorescence detection of single-cell droplet microfluidics with demonstrative applications in quantifying protein expression levels. The platform of multiplex fluorescence detection mainly included optical paths adopted from conventional microscopy enabling the generation of three optical spots from three laser sources for multiple fluorescence excitation and capture of multiple fluorescence signals by four photomultiplier tubes. As to platform characterization, microscopic images of three optical spots were obtained where clear Gaussian distributions of intensities without skewness confirmed the functionality of the scanning lens, while the controllable distances among three optical spots validated the functionality of fiber collimators and the reflector lens. As to demonstration, this platform was used to quantify single-cell protein expression within droplets where four-type protein expression of α-tubulin, Ras, c-Myc, and ß-tubulin of CAL 27 (Ncell = 1921) vs WSU-HN6 (Ncell = 1881) were quantitatively estimated, which were (2.85 ± 0.72) × 105 vs (4.83 ± 1.58) × 105, (3.69 ± 1.41) × 104 vs (5.07 ± 2.13) × 104, (5.90 ± 1.45) × 104 vs (9.57 ± 2.85) × 104, and (3.84 ± 1.28) × 105 vs (3.30 ± 1.10) × 105, respectively. Neural pattern recognition was utilized for the classification of cell types, achieving successful rates of 69.0% (α-tubulin), 75.4% (Ras), 89.1% (c-Myc), 65.8% (ß-tubulin), and 99.1% in combination, validating the capability of this platform of multiplex fluorescence detection to quantify various types of single-cell proteins, which could provide comprehensive evaluations on cell status.

A micromachined electrochemical angular accelerometer with highly integrated sensitive microelectrodes.

This paper presents a micromachined electrochemical angular accelerometer with highly integrated sensitive microelectrodes. Theoretical analyses and numerical simulations were conducted to model the angular accelerometer with key geometrical parameters (e.g., electrode spacing, via spacing and via size) optimized. Highly integrated sensitive microelectrodes were manufactured based on microfabrication and assembled to form MEMS-based electrochemical angular accelerometers. Device characterization was conducted, locating a sensitivity of 80 V/(rad/s2), a bandwidth of 0.01-18 Hz and a noise level of 3.98 × 10-8 (rad/s2)/√Hz. In comparison to a previously reported electrochemical angular microaccelerometer, a significant improvement in sensitivity (80 V/(rad/s2) vs. 10 V/(rad/s2)) was achieved due to the new structure of sensitive microelectrodes. These results indicated the potential of the developed MEMS-based electrochemical angular accelerometer in seismology, including natural disaster monitoring and resource exploration.

Effect of Pathogenic Fungal Infestation on the Berry Quality and Volatile Organic Compounds of Cabernet Sauvignon and Petit Manseng Grapes.

The effect of pathogenic fungal infestation on berry quality and volatile organic compounds (VOCs) of Cabernet Sauvignon (CS) and Petit Manseng (PM) were investigated by using biochemical assays and gas chromatography-ion mobility spectrometry. No significant difference in diseases-affected grapes for 100-berry weight. The content of tannins and vitamin C decreased significantly in disease-affected grapes, mostly in white rot-affected PM, which decreased by 71.67% and 66.29%. The reduced total flavonoid content in diseases-affected grape, among which the least and most were anthracnose-affected PM (1.61%) and white rot-affected CS (44.74%). All diseases-affected CS had much higher titratable acid, a maximum (18.86 g/100 ml) was observed in the gray mold-affected grapes, while only anthracnose-affected grapes with a higher titratable acid level (21.8 g/100 mL) were observed in PM. A total of 61 VOCs were identified, including 14 alcohols, 13 esters, 12 aldehydes, 4 acids, 4 ketones, 1 ether, and 13 unknown compounds, which were discussed from different functional groups, such as C6-VOCs, alcohols, ester acetates, aldehydes, and acids. The VOCs of CS changed more than that of Petit Manseng's after infection, while gray mold-affected Cabernet Sauvignon had the most change. C6-VOCs, including hexanal and (E)-2-hexenal were decreased in all affected grapes. Some unique VOCs may serve as hypothetical biomarkers to help us identify specific varieties of pathogenic fungal infestation.

Developments of Conventional and Microfluidic Flow Cytometry Enabling High-Throughput Characterization of Single Cells.

This article first reviews scientific meanings of single-cell analysis by highlighting two key scientific problems: landscape reconstruction of cellular identities during dynamic immune processes and mechanisms of tumor origin and evolution. Secondly, the article reviews clinical demands of single-cell analysis, which are complete blood counting enabled by optoelectronic flow cytometry and diagnosis of hematologic malignancies enabled by multicolor fluorescent flow cytometry. Then, this article focuses on the developments of optoelectronic flow cytometry for the complete blood counting by comparing conventional counterparts of hematology analyzers (e.g., DxH 900 of Beckman Coulter, XN-1000 of Sysmex, ADVIA 2120i of Siemens, and CELL-DYN Ruby of Abbott) and microfluidic counterparts (e.g., microfluidic impedance and imaging flow cytometry). Future directions of optoelectronic flow cytometry are indicated where intrinsic rather than dependent biophysical parameters of blood cells must be measured, and they can replace blood smears as the gold standard of blood analysis in the near future.

A new electrochemical angular microaccelerometer with integrated sensitive electrodes perpendicular to flow channels.

A new electrochemical angular microaccelerometer with integrated sensitive electrodes perpendicular to flow channels was developed in this paper. Based on a liquid inertial mass, an incoming angular acceleration was translated into varied concentrations of reactive ions around sensitive microelectrodes, generating a detection current. Key structural parameters of the sensitive microelectrodes were designed and compared based on theoretical analysis and numerical simulations. An angular microaccelerometer incorporating sensitive microelectrodes was then fabricated, assembled and characterized, producing a sensitivity of 338 V/(rad/s2), a -3 dB bandwidth of 0.01-10 Hz and a noise level of 4.67 × 10-8 (rad/s2)/Hz1/2 @ 1 Hz. These performances were better than their commercial counterparts based on traditional electrodes and previously reported microaccelerometers based on microsensitive electrodes in parallel with flow channels, which can be applied to measure rotational accelerations in earthquakes and buildings.

Inherent single-cell bioelectrical parameters of thousands of neutrophils, eosinophils and basophils derived from impedance flow cytometry.

Single-cell bioelectrical properties are commonly used for blood cell phenotyping in a label-free manner. However, previously reported inherent single-cell bioelectrical parameters (e.g., diameter Dc , specific membrane capacitance Csm and cytoplasmic conductivity σcy ) of neutrophils, eosinophils and basophils were obtained from only tens of individual cells with limited statistical significance. In this study, granulocytes were separated into neutrophils, eosinophils and basophils based on fluorescent flow cytometry, which were further aspirated through a constriction-microchannel impedance flow cytometry for electrical property characterization. Based on this microfluidic impedance flow cytometry, single-cell values of Dc , Csm and σcy were measured as 10.25 ± 0.66 µm, 2.17 ± 0.30 µF/cm2 , and 0.37 ± 0.05 S/m for neutrophils (ncell  = 9442); 9.73 ± 0.51 µm, 2.07 ± 0.19 µF/cm2 , and 0.30 ± 0.04 S/m for eosinophils (ncell  = 2982); 9.75 ± 0.49 µm, 2.06 ± 0.17 µF/cm2 , and 0.31 ± 0.04 S/m for basophils (ncell  = 5377). Based on these inherent single-cell bioelectrical parameters, neural pattern recognition was conducted, producing classification rates of 80.8% (neutrophil vs. eosinophil), 77.7% (neutrophil vs. basophil) and 59.3% (neutrophil vs. basophil). These results indicate that as inherent single-cell bioelectrical parameters, Dc , Csm and σcy can be used to classify neutrophils from eosinophils or basophils to some extent while they cannot be used to effectively distinguish eosinophils from basophils.