Prof. Qinghong Yuan l data analysis | Best Researcher Award | 2066

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Prof. Qinghong Yuan | Data Analysis | Best Researcher Award

šŸ‘©ā€āš•ļøProf. Qinghong Yuan East China Normal University, china

Prof. Qinghong Yuan a distinguished academic and researcher in the field of renewable energy, holds a PhD in Biosystems Engineering from Kangwon National University, South Korea. His academic journey has been marked by a profound dedication to advancing solar energy technologies, specifically in solar thermal harvesting and its integration into agricultural and architectural applications.

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šŸŽ“ Education

Doctor of Philosophy in Physical Chemistry The Chinese University of Hong Kong, Hong Kong, China Ā Master of Science in Physical Chemistry University, Xiamen, China Bachelor of Science in Chemistry Hunan Normal University, Hunan, China

šŸ‘Øā€šŸ”¬ Professional Experience

present Research ProfessorEast China Normal University, Shanghai, P.R. ChinaARC DECRA Research Fellow The University of Queensland, Queensland, Australia Research Professor East China Normal University, Shanghai, P.R. China Junior Researcher Professor East China Normal University, Shanghai, P.R. China Ā Visiting Scholar Rice University, Houston, USA Ā Associate Professor East China Normal University, Shanghai, P.R. China/2012: Postdoctoral Researcher Hong Kong Polytechnic University, Hong Kong, P.R. China

Researcher focus

Qinghong Yuan, an accomplished researcher, has contributed significantly to the field of materials science and nanotechnology. His work encompasses diverse topics, including the growth mechanisms of graphene and hexagonal boron nitride, the design of novel carbon-based structures, and the exploration of two-dimensional materials. Yuan’s research has not only advanced our fundamental understanding of nanomaterials but has also paved the way for practical applications in energy, catalysis, and electronic devices. His innovative approaches, highlighted by the controlled growth of graphene and the design of carbon-nitride structures, demonstrate his commitment to pushing the boundaries of materials science. šŸŒšŸš€šŸ”¬

šŸ“ŠĀ Citation Metrics (Google Scholar):

Citations by: All ā€“ 4190, Since 2018 ā€“ 2718

h-index: All ā€“ 33, Since 2018 ā€“ 28

i10 index: All ā€“ 51, Since 2018 ā€“ 27

šŸ“– PublicationsĀ  Top Note :

Fast growth of inch-sized single-crystalline graphene from a controlled single nucleus on Cuā€“Ni alloysĀ  paper publication in June 2016 cite by 611

Synthesis of large single-crystal hexagonal boron nitride grains on Cuā€“Ni alloyĀ  Ā  Ā Ā  paper publication inĀ  2015 cite by 357

Seamless stitching of graphene domains on polished copper (111) foilĀ Ā  paper publication in January 2015 cite by 344

Magic carbon clusters in the chemical vapor deposition growth of grapheneĀ  Ā  paper publication in 2012 cite by 167

Regulating infrared photoresponses in reduced graphene oxide phototransistors by defect and atomic structure control paper publication in 2013 cite by 145

Evaluating the catalytic efficiency of paired, single-atom catalysts for the oxygen reduction reactionĀ  Ā  paper publication in 2019Ā  cite by 124

Efficient defect healing in catalytic carbon nanotube growthĀ  paper publication in 2012 cite by 121

How graphene islands are unidirectionally aligned on the Ge (110) surface Ā  Ā  Ā  paper publication in 2016 cite by 103

Phosphorus and oxygen dualā€doped porous carbon spheres with enhanced reaction kinetics as anode materials for highā€performance potassiumā€ion hybrid capacitorsĀ  Ā  Ā paper publication in 2021 cite by 94

Global Innovator Excellence Award

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Introduction:

Welcome to the Global Innovator Excellence Awardā€”an accolade celebrating groundbreaking achievements in statistical engineering methods. This award recognizes trailblazing contributions across academia, industries, business, and research, fostering a collaborative spirit in advancing engineering data analytics on a global scale.

About the Award:

The Global Innovator Excellence Award acknowledges individuals or teams displaying exceptional innovation, demonstrating forward-thinking solutions, and driving transformative changes in statistical methods for engineering data analysis.

Eligibility & Requirements:

Eligibility extends globally to professionals, researchers, and innovators involved in statistical methodologies applicable to engineering data analysis. There are no age restrictions. Candidates must showcase significant contributions through publications, technological advancements, or impactful initiatives.

Evaluation Criteria:

Entries are evaluated based on innovation, practical application, transformative impact, interdisciplinary collaboration, and the advancement of statistical methods in engineering data analysis.

Submission Guidelines:

Submissions should include a comprehensive biography, an abstract highlighting the innovation’s significance, and supporting files showcasing the practical application of the methodology. Submission format guidelines are detailed in the application portal.

Recognition & Community Impact:

Winners receive global recognition for their groundbreaking work, highlighting their achievements in publications and conferences. The award aims to foster a community of innovators, encouraging knowledge exchange and collaboration.

Biography & Abstract:

Candidates are required to submit a detailed biography outlining their contributions to statistical engineering methods. The abstract should succinctly highlight the innovation’s impact and significance in the field.