Fangmeng Liu

State Key Laboratory on Integrated Optoelectronics

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Peng Sun

State Key Laboratory on Integrated Optoelectronics

113

Xu Yan

Jilin University

Geyu Lu

State Key Laboratory on Integrated Optoelectronics

Geyu Lu

State Key Laboratory on Integrated Optoelectronics

Tianshuang Wang

State Key Laboratory on Integrated Optoelectronics

Chenguang Wang

Jilin University

Xishuang Liang

State Key Laboratory on Integrated Optoelectronics

Liupeng Zhao

State Key Laboratory on Integrated Optoelectronics

Fengmin Liu

State Key Laboratory on Integrated Optoelectronics

Yuan Gao

Zhejiang University of Science and Technology

Cooperative Institutions

Jilin University

226

State Key Laboratory on Integrated Optoelectronics

179

Jilin Medical University

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Tsinghua University

Zhejiang University

Sichuan University

State Key Laboratory of Automotive Simulation and Control

University of Science and Technology of China

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Au₃₉Rh₆₁ Alloy Nanocrystal-Decorated W₁₈O₄₉ for Enhanced Detection of n-Butanol

ACS Sensors (2019)

Jihao Bai Yueyue Li Yueying Liu Hongtao Wang Fengmin Liu

Here, AuxRh1–x alloy nanocrystals (NCs) were used to decorate W18O49 for enhanced detection of n-butanol vapor. Monodisperse AuxRh1–x alloy NCs with a tunable composition and urchinlike W18O49 were synthesized by a simple solvothermal method. AuxRh1–x alloy NCs were loaded onto the W18O49 surface by the impregnation method. A series of material characterization methods were employed to characterize the structure and morphology of as-synthesized materials. The performance of AuxRh1–x–W18O49-based sensors to n-butanol was investigated. The results demonstrated that the Au39Rh61–W18O49-based sensor had the highest response, short response time, good selectivity, excellent repeatability, and stability to n-butanol. The gas sensing mechanism was supposed, the excellent catalytic capability of the AuxRh1–x alloy NCs were believed to be a major factor in enhancing the detection of n-butanol.

Dispersity

n-Butanol

Morphology

10.1021/acssensors.9b01073

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Citations (54)

High-performance acetone gas sensor based on Ru-doped SnO2 nanofibers

Sensors and Actuators B Chemical (2020)

Xueying Kou Fanqi Meng Ke Chen Tianshuang Wang Peng Sun

Electrospinning

Ultraviolet photoelectron spectroscopy

10.1016/j.snb.2020.128292

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Citations (184)

Polyaniline @ porous nanosphere SnO2/Zn2SnO4 nanohybrid for selective room temperature flexible NH3 sensor

Sensors and Actuators B Chemical (2020)

Ao Liu Yinglin Wang Xueli Yang Fangmeng Liu Siqi Li

Polyaniline nanofibers

10.1016/j.snb.2020.128218

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Citations (52)

Synthesis, characterization and gas sensing properties of porous flower-like indium oxide nanostructures

RSC Advances (2015)

Xiumei Xu Xin Li Haijiao Zhang Changhao Feng Chong Wang

Porous flower-like indium oxide nanostructures were synthesized and exhibited remarkably enhanced response to NO₂.

Characterization

10.1039/c4ra16632a

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Citations (22)

Ti3C2Tx Composite Aerogels Enable Pressure Sensors for Dialect Speech Recognition Assisted by Deep Learning

Nano-Micro Letters (2024)

Yanan Xiao He Li Tianyi Gu Xiaoteng Jia Shixiang Sun

Wearable pressure sensors capable of adhering comfortably to the skin hold great promise in sound detection. However, current intelligent speech assistants based on pressure sensors can only recognize standard languages, which hampers effective communication for non-standard language people. Here, we prepare an ultralight Ti

10.1007/s40820-024-01605-z

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Citations (1)

Enhancing SO₂ Sensing Performance of a Fuel Cell-Type Sensor with N-Doped Cu/CNT Aerogel Electrode and COF/Nafion Electrolyte

ACS Sensors (2024)

Lingchu Huang Huaiyuan Sun Weijia Li Jianyu Zhang Sitong Feng

Sulfur dioxide (SO

Nafion

10.1021/acssensors.4c02253

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Citations (0)

Embedding Proteins within Spatially Controlled Hierarchical Nanoarchitectures for Ultrasensitive Immunoassay

Analytical Chemistry (2022)

Dandan Su Hongxia Li Ri Zhou Lianjing Zhao Aixin Li

Modulating the precise self-assembly of functional biomacromolecules is a critical challenge in biotechnology. Herein, functional biomacromolecule-assembled hierarchical hybrid nanoarchitectures in a spatially controlled fashion are synthesized, achieving the biorecognition behavior and signal amplification in the immunoassay simultaneously. Biomacromolecules with sequential assembly on the scaffold through the biomineralization process show significantly enhanced stability, bioactivity, and utilization efficiency, allowing tuning of their functions by modifying their size and composition. The hierarchically hybrid nanoarchitectures show great potential in construction of ultrasensitive immunoassay platforms, achieving a three order-of-magnitude increase in sensitivity. Notably, the well-designed HRP@Ab2 nanoarchitectures allow for optical immunoassays with a detection range from picogram mL–1 to microgram mL–1 on demand, providing great promise for quantitative analysis of both low-abundance and high-residue targets for biomedical applications.

10.1021/acs.analchem.2c00269

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Citations (10)

The facile synthesis of MoO₃microsheets and their excellent gas-sensing performance toward triethylamine: high selectivity, excellent stability and superior repeatability

New Journal of Chemistry (2018)

Wenhao Jiang Dongdong Wei Sufang Zhang Xiaohong Chuai Peng Sun

In this work, MoO₃microsheets were successfully prepared by thermally oxidizing the MoO₂nanospheres synthesized by a hydrothermal method.

Triethylamine

Oxidizing agent

Repeatability

10.1039/c8nj03539c

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Citations (87)

Pattern Recognition with Temperature Regulation: A Single YSZ-Based Mixed Potential Sensor Classifies Multiple Mixtures of Isoprene, n-Propanol, and Acetone

ACS Sensors (2023)

Siyuan Lv Tianyi Gu Jing Wang Si Hui Pan Fangmeng Liu

Gas sensors integrated with machine learning algorithms have aroused keen interest in pattern recognition, which ameliorates the drawback of poor selectivity on a sensor. Among various kinds of gas sensors, the yttria-stabilized zirconia (YSZ)-based mixed potential-type sensor possesses advantages of low cost, simple structure, high sensitivity, and superior stability. However, as the number of sensors increases, the increased power consumption and more complicated integration technology may impede their extensive application. Herein, we focus on the development of a single YSZ-based mixed potential sensor from sensing material to machine learning for effective detection and discrimination of unary, binary, and ternary gas mixtures. The sensor that is sensitive to isoprene, n-propanol, and acetone is manufactured with the MgSb2O6 sensing electrode prepared by a simple sol-gel method. Unique response patterns for specific gas mixtures could be generated with temperature regulation. We chose seven algorithm models to be separately trained for discrimination. In order to realize more accurate discrimination, we further discuss the selection of suitable feature parameters and its reasons. With temperature regulation coefficients which are easily available as feature input to model, a single sensor is verified to achieve elevated accuracy rates of 95 and 99% for the discrimination of seven gases (three unary gases, three binary gas mixtures, and one ternary gas mixture) and redefined six gas mixtures. This article provides a potential new approach via a mixed potential sensor instead of a sensor array that could provide a wide application prospect in the field of electronic nose and artificial olfaction.

Isoprene

Propanol

10.1021/acssensors.3c01698

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Citations (4)

Machine learning‐assisted wearable sensor array for comprehensive ammonia and nitrogen dioxide detection in wide relative humidity range

InfoMat (2024)

Yiwen Li Shuai Guo Boyi Wang Jianguo Sun Liupeng Zhao

Abstract The fast booming of wearable electronics provides great opportunities for intelligent gas detection with improved healthcare of mining workers, and a variety of gas sensors have been simultaneously developed. However, these sensing systems are always limited to single gas detection and are highly susceptible to the inference of ubiquitous moisture, resulting in less accuracy in the analysis of gas compositions in real mining conditions. To address these challenges, we propose a synergistic strategy based on sensor integration and machine learning algorithms to realize precise NH 3 and NO 2 gas detections under real mining conditions. A wearable sensing array based on the graphene and polyaniline composite is developed to largely enhance the sensitivity and selectivity under mixed gas conditions. Further introduction of backpropagation neural network (BP‐NN) and partial least squares (PLS) algorithms could improve the accuracy of gas identification and concentration prediction and settle the inference of moisture, realizing over 99% theoretical prediction level on NH 3 and NO 2 concentrations within a wide relative humidity range, showing great promise in real mining detection. As proof of concept, a wireless wearable bracelet, integrated with sensing arrays and machine‐learning algorithms, is developed for wireless real‐time warning of hazardous gases in mines under different humidity conditions. image

Nitrogen dioxide

10.1002/inf2.12544

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Citations (31)