Gang Chen | Cellulose-based material | Best Researcher Award

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Prof Dr. Gang Chen, Cellulose-based material, Best Researcher Award

Professor at South China University of Technology, China

Summary:

Prof. Dr. Gang Chen is a leading expert in papermaking technologies and special paper, with extensive research contributions and innovations in the field. He serves as the Chief Professor and Ph.D. Supervisor at South China University of Technology, where he leads the Special Paper Research Team. Dr. Chen has been instrumental in advancing new papermaking technologies and developing special paper materials, with over 100 published papers and 36 patents to his name. His collaborative efforts with prominent institutions and active involvement in national and provincial research projects underscore his commitment to innovation and excellence in the papermaking industry.

Professional Profile:

šŸ‘©ā€šŸŽ“Education:

Dr. Gang Chen holds a Ph.D. in Pulp and Paper Making from South China University of Technology.

Professional Experience:

Dr. Gang Chen is a distinguished Chief Professor and Ph.D. Supervisor leading the Special Paper Research Team at South China University of Technology. With a profound expertise in new papermaking technologies and special paper, he has been at the forefront of research and innovation in his field. Over the course of his career, Dr. Chen has successfully led and participated in over 80 research projects, amassing a total funding of 41 million RMB. His prolific academic output includes over 100 published papers, with 58 indexed in major academic databases, alongside authoring a monograph and securing 36 invention patents. Additionally, he has been actively involved in consultancy and industry projects, contributing significantly to advancements in papermaking technologies.

Research and Innovations:

Dr. Chen’s research focuses on special paper-based materials, nanofiber composite materials, and cellulose-based hygroscopic materials. He is dedicated to exploring specialty paper technologies, paper-based functional materials, paper coating technology, fiber mixing and forming mechanisms, and the preparation and application of nanocellulose. His work extends to national key research and development programs, green manufacturing system integration projects, and various foundation and high-tech achievement incubation projects.

Publications Top Noted:Ā 

Enantioselective C(sp3)ā€’H Bond Activation by Chiral Transition Metal Catalysts

  • Authors: T.G. Saint-Denis, R.Y. Zhu, G. Chen, Q.F. Wu, J.Q. Yu
  • Journal: Science
  • Volume: 359, Issue 6377
  • Year: 2018
  • DOI: 10.1126/science.aao4798
  • Citations: 628

All-Perovskite Tandem Solar Cells with Improved Grain Surface Passivation

  • Authors: R. Lin, J. Xu, M. Wei, Y. Wang, Z. Qin, Z. Liu, J. Wu, K. Xiao, B. Chen, S.M. Park, et al.
  • Journal: Nature
  • Volume: 603, Issue 7899
  • Year: 2022
  • DOI: 10.1038/s41586-021-04174-w
  • Citations: 618

Applied Integer Programming: Modeling and Solution

  • Authors: D.S. Chen, R.G. Batson, Y. Dang
  • Publisher: John Wiley & Sons
  • Year: 2011
  • ISBN: 9780470373064
  • Citations: 589

Novel Nanostructured Paper with Ultrahigh Transparency and Ultrahigh Haze for Solar Cells

  • Authors: Z. Fang, H. Zhu, Y. Yuan, D. Ha, S. Zhu, C. Preston, Q. Chen, Y. Li, X. Han, et al.
  • Journal: Nano Letters
  • Volume: 14, Issue 2
  • Year: 2014
  • DOI: 10.1021/nl404101p
  • Citations: 495

Successful Recovery of COVIDā€19 Pneumonia in a Renal Transplant Recipient with Longā€Term Immunosuppression

  • Authors: L. Zhu, X. Xu, K.E. Ma, J. Yang, H. Guan, S. Chen, Z. Chen, G. Chen
  • Journal: American Journal of Transplantation
  • Volume: 20, Issue 7
  • Year: 2020
  • DOI: 10.1111/ajt.15905
  • Citations: 405

Hanqing Bao | Land cover | Best Researcher Award

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Mr. Hanqing Bao, Land cover, Best Researcher Award

Hanqing Bao at Ludwig-Maximilians-UniversitƤt, Germany

Summary:

Mr. Hanqing Bao is an accomplished researcher and Ph.D. candidate specializing in Physical Geography and Environmental Remote Sensing at Ludwig-Maximilians-UniversitƤt (Munich). With extensive experience in remote sensing and deep learning, his work focuses on the intelligent processing and analysis of remote sensing images to enhance urban planning and environmental monitoring.

Professional Profile:

šŸ‘©ā€šŸŽ“Education:

Ph.D. Candidate in Physical Geography and Environmental Remote Sensing

  • Ludwig-Maximilians-UniversitƤt (Munich) (2020 – Present)
  • Research Team: Physical Geography and Environmental Remote Sensing
  • Supervisor: Prof. Dr. Lukas Lehnert

Master’s Degree in Surveying Science and Technology (Remote Sensing)

  • China University of Geosciences (Beijing) (2017 – 2020)
  • Research Team: Intelligent Remote Sensing Image Processing and Analysis
  • Supervisor: Dr. Dongping Ming

Bachelor’s Degree in Surveying and Mapping Engineering

  • Lanzhou Jiaotong University (2013 – 2017)

Professional Experience:

As a Ph.D. candidate at Ludwig-Maximilians-UniversitƤt, Mr. Bao has been integral to research initiatives focusing on the application of deep learning in remote sensing. His research encompasses the development of advanced techniques for land use change detection, high-resolution image processing, and the exploration of urban spatial structures. His work leverages the capabilities of convolutional neural networks (CNN) and graph convolutional networks (GCN) to improve the accuracy and efficiency of remote sensing data analysis.

Research Interest:

Mr. Bao’s research interests lie at the intersection of remote sensing, deep learning, and urban planning. Key areas of focus include:

  • Land Use Change Detection: Utilizing Siamese Networks to detect changes in land use over time, aiding in sustainable urban development.
  • High-Resolution Image Processing: Applying deep learning techniques to process and analyze very high-resolution remote sensing images.
  • Scale Effect Mitigation: Developing methods to reduce the impact of scale effect on remote sensing image information extraction through stratification and spatial statistics.
  • Intelligent Image Understanding: Segmentation, classification, and semantic recognition of remote sensing images to extract meaningful information about natural and built environments.

Mr. Bao’s contributions to the field of remote sensing are aimed at enhancing our understanding of urban environments and supporting effective environmental management practices.

Publications Top Noted:Ā 

Title: DFCNN-based semantic recognition of urban functional zones by integrating remote sensing data and POI data

  • Authors: Hanqing Bao, Dongping Ming, Yuan Guo, et al.
  • Journal: Remote Sensing
  • Year: 2020
  • Volume: 12
  • Issue: 7

Title: SOā€“CNN based urban functional zone fine division with VHR remote sensing image

  • Authors: Wenchao Zhou, Dongping Ming, Xudong Lv, et al.
  • Journal: Remote Sensing of Environment
  • Year: 2020
  • Volume: 236

Title: Farmland extraction from high spatial resolution remote sensing images based on stratified scale pre-estimation

  • Authors: Ling Xu, Dongping Ming, Wenchao Zhou, et al.
  • Journal: Remote Sensing
  • Year: 2019
  • Volume: 11
  • Issue: 2

Title: A new method for region-based majority voting CNNs for very high resolution image classification

  • Authors: Xudong Lv, Dongping Ming, Tian Lu, et al.
  • Journal: Remote Sensing
  • Year: 2018
  • Volume: 10
  • Issue: 12

Title: Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

  • Authors: Wenchao Zhou, Dongping Ming, Ling Xu, et al.
  • Journal: Journal of Spectroscopy
  • Year: 2018
  • Volume: 2018

Edward Reutzel | Additive Manufacturing | Best Researcher Award

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Dr. Edward Reutzel, Additive Manufacturing, Best Researcher Award

Doctorate at Penn State University, Applied Research Laboratory, United States

Summary:

Dr. Edward Reutzel is a distinguished mechanical engineer renowned for his contributions to additive manufacturing, materials processing, and laser systems engineering. He holds a Ph.D. in Mechanical Engineering from Pennsylvania State University, where he also completed his Bachelor of Science degree. Additionally, he earned a Master of Science in Mechanical Engineering from the Georgia Institute of Technology.

Dr. Reutzel’s professional journey encompasses a blend of academia, research, and industry. He has held various significant roles, including serving as Graduate Faculty and directing the Center for Innovative Material Processing thru Direct Digital Deposition at Pennsylvania State University. Dr. Reutzel has also made substantial contributions to additive manufacturing research, with his work focusing on in-situ flaw detection, quality control, and process monitoring using innovative techniques such as machine learning and optical emissions spectroscopy.

Professional Profile:

Scopus Profile

Google Scholar Profile

Orcid Profile

šŸ‘©ā€šŸŽ“Education & Qualification:

Professional experience: Ā 

Dr. Edward Reutzel boasts a distinguished professional career characterized by a diverse array of roles in academia, research, and industry. His journey began with positions at esteemed institutions such as Pennsylvania State University and the Georgia Institute of Technology, where he honed his expertise in mechanical engineering. Over the years, Dr. Reutzel has held pivotal roles at Pennsylvania State University, including serving as Graduate Faculty in the Mechanical Engineering Department and directing the Center for Innovative Material Processing thru Direct Digital Deposition. His contributions to the Additive Manufacturing and Design Program have been invaluable. Furthermore, Dr. Reutzel has made significant strides in research, notably as an Associate Research Professor at ARL Penn State, where he led the Laser System Engineering and Integration Department. Prior to his academic endeavors, he gained hands-on experience as a Test Engineer at GE Fanuc Automation and as a Senior Engineer at Lockheed Martin Technologies, stationed at the Idaho National Laboratory. Dr. Reutzel’s professional journey underscores his commitment to advancing the field of mechanical engineering through a blend of academic rigor, innovative research, and practical application.

Research Interest:

Dr. Edward Reutzel’s research interests encompass a wide range of topics within the field of mechanical engineering, with a particular focus on additive manufacturing, materials processing, and laser systems engineering. Throughout his career, he has been deeply involved in advancing the understanding and application of additive manufacturing technologies, exploring novel methods for direct digital deposition and innovative material processing techniques. Dr. Reutzel’s work often intersects with areas such as design optimization, material characterization, and process control, aiming to enhance the efficiency, reliability, and performance of additive manufacturing processes.

Additionally, he has shown a keen interest in laser systems engineering, leveraging laser technologies for various applications including materials processing, surface modification, and additive manufacturing. His research endeavors seek to push the boundaries of laser-based manufacturing processes, exploring new methods, materials, and applications to drive advancements in both industrial and academic settings.

Overall, Dr. Reutzel’s research interests reflect a commitment to exploring cutting-edge technologies and methodologies to address real-world challenges in mechanical engineering, with a focus on additive manufacturing, materials processing, and laser systems engineering.

Publication Top Noted:

Title: Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

  • Authors: Z Snow, B Diehl, EW Reutzel, A Nassar
  • Journal: Journal of Manufacturing Systems
  • Volume: 59
  • Pages: 12-26
  • Citation Count: 107
  • Year: 2021

Title: Formation processes for large ejecta and interactions with melt pool formation in powder bed fusion additive manufacturing

  • Authors: AR Nassar, MA Gundermann, EW Reutzel, P Guerrier, MH Krane
  • Journal: Scientific reports
  • Volume: 9
  • Issue: 1
  • Pages: 5038
  • Citation Count: 105
  • Year: 2019

Title: Deep learning of variant geometry in layerwise imaging profiles for additive manufacturing quality control

  • Authors: F Imani, R Chen, E Diewald, E Reutzel, H Yang
  • Journal: Journal of Manufacturing Science and Engineering
  • Volume: 141
  • Issue: 11
  • Article Number: 111001
  • Citation Count: 90
  • Year: 2019

Title: Sensing defects during directed-energy additive manufacturing of metal parts using optical emissions spectroscopy

  • Authors: AR Nassar, TJ Spurgeon, EW Reutzel
  • Conference: 2014 International Solid Freeform Fabrication Symposium
  • Citation Count: 84
  • Year: 2014

Title: Layerwise in-process quality monitoring in laser powder bed fusion

  • Authors: F Imani, A Gaikwad, M Montazeri, P Rao, H Yang, E Reutzel
  • Conference: International Manufacturing Science and Engineering Conference
  • Article Number: 51357
  • Citation Count: 78
  • Year: 2018