Dr. Yue Wang | Sensor development Award | Best Researcher Award

Dr. Yue Wang | Sensor development Award | Best Researcher Award 

Dr. Yue Wang, University of Science and Technology Liaoning, China

Dr. Yue Wang is an Associate Professor at the School of Chemical Engineering at the University of Science and Technology Liaoning in China. He earned his Bachelor’s degree from the University of Science and Technology Anshan and both his Master’s and Doctorate degrees from the University of Science and Technology Liaoning and Saitama Institute of Technology, Japan, respectively. Since joining the University of Science and Technology Liaoning in 2006, Dr. Wang has focused his research on sensors and biosensors, biofuel cells, supercapacitors, energy harvesting, and artificial muscles. His work has resulted in over 60 published scientific papers, garnering approximately 600 citations, reflecting his significant contributions to the field. Dr. Wang has secured multiple research grants from various institutions, including the Education Department of Liaoning Province and the Natural Science Foundation of Liaoning Province, to advance his projects on conductive sensors, pesticide sensors, electrochemical biosensors, and wearable smart sensing technologies. Additionally, he completed a visiting scholarship at the University of Texas at Dallas in 2019-2020, further enhancing his academic and research expertise.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award: Yue Wang

Yue Wang is an exemplary candidate for the Best Researcher Award, primarily due to his substantial academic qualifications, extensive research contributions, and impactful work in the field of Material Science, specifically within sensor and biosensor technologies.

Education

  • Bachelor’s Degree in Material Science
    University of Science and Technology Anshan, China
    September 1998 – July 2002
  • Master’s Degree in Material Science
    University of Science and Technology Liaoning, China
    September 2003 – March 2006
  • Ph.D. in Material Science
    Saitama Institute of Technology, Japan
    April 2008 – March 2011

Work Experience

  • Associate Professor
    University of Science and Technology Liaoning, China
    April 2006 – Present
  • Visiting Scholar
    University of Texas at Dallas
    April 2019 – March 2020

Publication top Notes:

A carbon black–doped chalcopyrite–based electrochemical sensor for determination of hydrogen peroxide

Glucose oxidase, horseradish peroxidase and phenothiazine dyes-co-adsorbed carbon felt-based amperometric flow-biosensor for glucose

Crab gill–derived nanorod-like carbons as bifunctional electrochemical sensors for detection of hydrogen peroxide and glucose

Cellulose-derived hierarchical porous carbon based electrochemical sensor for simultaneous detection of catechol and hydroquinone

A triphenylamine based fluorescent probe for Zn2+ detection and its applicability in live cell imaging

1,8-naphthalimide-triphenylamine-based red-emitting fluorescence probes for the detection of hydrazine in real water samples and applications in bioimaging in vivo

Prof. Yankun Peng | Smart Monitoring Award | Best Researcher Award

Prof. Yankun Peng | Smart Monitoring Award | Best Researcher Award 

Prof. Yankun Peng, China Agricultural University, China

Dr. Peng is a distinguished researcher and professor in the field of Agricultural Engineering with a focus on intelligent detection systems and automated devices for evaluating agricultural product quality and safety. He holds a Ph.D. in Biological and Agricultural Engineering from Tokyo University of Agriculture and Technology, Japan, and has extensive academic and professional experience in both China and the United States. Since 2007, Dr. Peng has served as a Professor and PhD supervisor at the College of Engineering, China Agricultural University (CAU), where he also holds key leadership roles including Director of the National R&D Center for Agro-Processing Technology and Equipment and the National Technical Center for Nondestructive Evaluation, Identification, Instrument, and Equipment of Famous Agro-foods.

Professional Profile:

 

Summary of Suitability for Best Researcher Award 

Dr. Peng has authored 293 peer-reviewed journal articles and 257 conference proceedings, showcasing his prolific research output.He holds 107 patents (including a US patent), with 22 patents industrialized, reflecting his significant contributions to applied science and technology. Additionally, he has developed 18 series of equipment for agro-food quality inspection and grading. Dr. Peng has established 14 standards and authored 4 books and 17 book chapters, demonstrating his leadership in setting benchmarks and contributing to scientific literature.

Education

  • Ph.D. in Biological and Agricultural Engineering
    Tokyo University of Agriculture and Technology, Tokyo, Japan
    Apr. 1993 – Mar. 1996
    Major: Agricultural Engineering, Specialty in Biological Production Science
    Dissertation Title: Active Noise Control on Agricultural/Biological Production Machinery

    • Developed and designed a new type of Active Noise Control (ANC) system/equipment.
    • Proposed a Recurrent Least Squares (RLS) algorithm for noise reduction.
    • Conducted computer simulations of noise reduction effects using C/C++ programming language.
    • Constructed an Adaptive Digital Filter (ADF) system with digital signal processors (DSP) and C/C++ programming.
    • Evaluated the control system on actual machinery and simplified the control algorithm using matrix theory.
  • M.S. in Engineering in Agricultural Electrification & Automation
    Graduate School of Northeast Agricultural University, Harbin, China
    Sep. 1985 – Dec. 1988
    Major: Agricultural Electrification & Automation
    Thesis Title: A Microcomputer Control System for Livestock Granulated Feed Processing

    • Developed a PID feedback control system using a microcomputer.
    • Proposed a new control method for the rotation speed of a servomechanism.
    • Designed a controller using a microcomputer and assembly programming language.
    • Invented a grain flow sensor and applied the control system to livestock feed production.
    • Proposed a method for judging the stability of linear time-invariant systems.

Professional Experience

  • Professor and Ph.D. Supervisor
    Department of Agricultural Engineering, College of Engineering, China Agricultural University (CAU)
    Beijing, China
    Mar. 2007 – Present

    • Research in nondestructive measurement and instrumentation for agricultural product quality and safety.
    • Development of hyperspectral/multispectral and Raman spectral imaging methods for meat microbial contamination detection.
    • Development of rapid real-time inspection/detection systems and NIR optical instruments for agricultural product contaminants.
    • Teaching courses on nondestructive measurement technology and hyperspectral imaging techniques for agro-food quality attributes.
    • Supervised over 60 graduate students in agricultural engineering research.
  • Director, National R&D Center for Agro-Processing Technology and Equipment
    Ministry of Agriculture and Rural Affairs, China
    Nov. 2009 – Present

    • Oversight of national research and development projects related to agro-processing technology and equipment.
  • Director, National Technical Center for Nondestructive Evaluation, Identification, Instrument and Equipment of Famous, Special, Excellent and New Agro-foods
    Ministry of Agriculture and Rural Affairs, China
    Dec. 2019 – Present

    • Leadership in the development and evaluation of nondestructive techniques and equipment for agro-food quality assessment.

Publication top Notes:

Real-time lettuce-weed localization and weed severity classification based on lightweight YOLO convolutional neural networks for intelligent intra-row weed control

Tailored Au@Ag NPs for rapid ractopamine detection in pork: Optimizing size for enhanced SERS signals

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion

SERS characterization and concentration prediction of Salmonella in pork

Rapid Quantitative detection of Ractopamine using Raman scattering features combining with Deep Learning

Non-destructive detection of TVC in pork by machine learning techniques based on spectral information