Zhichun Bi

Jilin University

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i-10 index

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Yongxin Li

Jilin University

Hui Huang

Jilin University

Xiaoyu Yang

Wuhan University of Technology

Chenghui Yin

Jilin University

Linxue Zhen

Jilin University

N. Loubet

IBM (United States)

Jie Li

Sichuan University

Yu Ma

Shanghai University

Donghui Song

Jilin Medical University

H. Jagannathan

University at Albany, State University of New York

Cooperative Institutions

IBM (United States)

Albany Molecular Research (United States)

GlobalFoundries (United States)

IBM Research - Thomas J. Watson Research Center

Jilin University

Samsung (United States)

GlobalFoundries (Germany)

Applied Materials (United States)

IBM Research - Austin

University at Albany, State University of New York

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Paper-Based Colorimetric Sensor Array Integrated with Smartphone App for the Identification of Tea Polyphenol and Longjing Tea

Xiaoyu Yang Jie Li Zhichun Bi Le Wang Yongxin Li

Identification

10.2139/ssrn.5096871

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Citations (0)

A Novel Hybrid Sensor Array Based on the Polyphenol Oxidase and its Nanozymes Combined with the Machine Learning Based Dual Output Model to Identify Tea Polyphenols and Chinese Teas

Xiaoyu Yang Zhichun Bi Chenghui Yin Hui Huang Yongxin Li

A novel sensor array was developed based on the enzyme/nanozyme hybridization for the identification of tea polyphenols (TPs) and Chinese teas. The enzyme/nanozyme with polyphenol oxidase activity can catalyze the reaction between TPs and 4-aminoantipyrine(4-AAP) to produce differences in color, and the sensor array was thus constructed to accurately identify TPs mixed in different species, concentrations, or ratios. In addition, a machine learning based dual output model was further used to effectively predict the classes and concentrations of unknown samples. Therefore, the qualitative and quantitative detection of TPs can be realized continuously and quickly. Furthermore, the sensor array combining the machine learning based dual output model was also utilized for the identification of Chinese teas. The method can distinguish the six teas series in China, and then precisely differentiate the more specific tea varieties. This study provides an efficient and facile strategy for the identification of teas and tea products.

10.2139/ssrn.4596114

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Modified Metal–Organic Framework (MOF-818) inspired by natural enzymes for intelligent detection of total antioxidants and bisphenol A in infant beverages

Analytica Chimica Acta (2024)

Jie Li Luwei Wang Yu Ma Zhichun Bi Yongxin Li

10.1016/j.aca.2024.342897

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A colorimetric sensor array based on peroxidase activity nanozyme for the highly efficient differential sensing of tea polyphenols and Tieguanyin adulteration

Food Chemistry (2023)

Xiaoyu Yang Zhichun Bi Chenghui Yin Shuyi Zhang Donghui Song

10.1016/j.foodchem.2023.137265

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Citations (15)

A Novel Hybrid Sensor Array Based On The Polyphenol Oxidase And Its Nanozymes Combined With The Machine Learning Based Dual Output Model to Identify Tea Polyphenols and Chinese Teas

Xiaoyu Yang Zhichun Bi Chenghui Yin Hui Huang Yongxin Li

10.2139/ssrn.4662008

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Citations (0)

Three-Channel Sensor Array Based on Switchable Nanozymatic Reactions of a Novel Trimetallic Nanozyme for Anti-Interference Identification of Tea Adulteration

Jie Li Linxue Zhen Zhichun Bi Yu Ma Hui Huang

Identification

10.2139/ssrn.5145074

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Citations (0)

A Colorimetric Sensor Array Based on Peroxidase Activity Nanozyme for the Highly Efficient Differential Sensing of Tea Polyphenols and Tieguanyin Adulteration

Xiaoyu Yang Zhichun Bi Chenghui Yin Shuyi Zhang Donghui Song

Due to the unscrupulous traders' use of substandard products, Tieguanyin (TGY) has become unpopular in market. To solve the adulterate problem of TGY, a colorimetric sensor array (CSA) based on peroxidase activity of nanozyme was constructed. In the presence of H2O2, nanozymes can catalyze colorless TMB to blue oxTMB, while the tea polyphenols (TPs) present in TGY can inhibit this process, and the degree of inhibition varies significantly with the reaction time for TPs. We selected two nanozymes and three reaction time points to construct a CSA. It can successfully distinguish 8 TPs in TGY. Besides, the discriminative analysis by chemometric algorithms can achieve: (1) distinction between TGY and adulterated tea, (2) discrimination of TGY in various seasons and seasonal adulteration in different degrees. The method constructed in this work is promising for both the class and quality differentiation of TGY and other teas with TPs as the main activity.

Discriminative model

Colorimetry

10.2139/ssrn.4531530

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Citations (2)

A Sensor Array Based on a Nanozyme with Polyphenol Oxidase Activity for the Identification of Tea Polyphenols and Chinese Green Tea

Xiaoyu Yang Bin Zou Xinjian Zhang Jie Yang Zhichun Bi

A novel sensor array was developed based on a nanozyme with polyphenol oxidase(PPO) activity for the identification of tea polyphenols (TPs) and Chinese green tea. The absorption spectra changes of the nanozyme and its substrate in the presence of different TPs were first investigated. The feature spectra were scientifically selected using genetic algorithm (GA), and then a 5×3 sensor array was constructed. Combined with the support vector machine (SVM) discriminative model, the discriminative rate of this sensor array was 100% for different concentrations of typical TPs in Chinese green tea. Based on the above study, we further developed a facile and efficient new method for the category differentiation and adulteration identification of green tea, and the accuracy of this array was 96.88% and 100% for eight types of green teas and different adulteration ratios of Biluochun, respectively. This work has significance for the rapid discrimination of green tea brands and adulteration.

Identification

10.2139/ssrn.4654211

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Citations (0)

A Novel Hybrid Sensor Array Based on the Polyphenol Oxidase and its Nanozymes Combined with the Machine Learning Based Dual Output Model to Identify Tea Polyphenols and Chinese Teas

Xiaoyu Yang Zhichun Bi Chenghui Yin Hui Huang Yongxin Li

A novel sensor array was developed based on the enzyme/nanozyme hybridization for the identification of tea polyphenols and Chinese teas. The enzyme/nanozyme with polyphenol oxidase activity can catalyze the reaction between tea polyphenols and 4-aminoantipyrine(4-AAP) to produce differences in color, and the sensor array was thus constructed to accurately identify tea polyphenols(TPs) mixed in different species, concentrations, or ratios. In addition, a machine learning based dual output model was further used to effectively predict the classes and concentrations of unknown samples. Therefore, the qualitative and quantitative detection of tea polyphenols can be realized continuously and quickly. Furthermore, the sensor array combining the machine learning based dual output model was also utilized for the identification of Chinese teas. The method can first distinguish the six major teas series in China, and then precisely differentiate the more specific tea varieties. This study provides an efficient and facile strategy for the identification of teas and tea products.

10.2139/ssrn.4529338

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Citations (1)

Advanced Arsenic Doped Epitaxial Growth for Source Drain Extension Formation in Scaled FinFET Devices

2021 IEEE International Electron Devices Meeting (IEDM) (2018)

Shinichi Mochizuki B. Colombeau Yu Lan Abhishek Dube S. Choi

In this paper, we demonstrate a novel Source Drain Extension (SDE) approach to enable NMOS device scaling along with improved performance. For the first time, SDE formation with epitaxially grown As doped Si (Si:As) has been examined and compared to the current state-of-the-art SDE formation in FinFET at 10nm logic ground rules. It is found that a Si:As layer based SDE provides a clear improvement in the short channel effect and a significant device performance increase. It is also shown that a careful co-optimization of the Si:As layer and Source / Drain (S/D) lateral recess is required to achieve the optimum device gain. This paves the way for the ultimate nSDE formation for current and next generation CMOS devices.

10.1109/iedm.2018.8614543

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Citations (6)