Jian Qi Editor at IgMin Research
We aim to foster interdisciplinary discussions that contribute to fast-tracking research.
Biography
He received his BS degree from Hebei Normal University in 2005. He received his PhD degree from Beijing Institute of Technology (BIT) in 2010. After this, he went to National Center for Nanoscience and Technology (NCNST) in China for his postdoctoral research (2012).
Then, he went to Beijing Shenwu Environmental Energy Technology Group Co., Ltd. Energy Conservation and Low Carbon Technology Research Institute as associate research fellow. Then, he joined Institute for Process Engineering (IPE), Chinese Academy of Sciences in 2014 and has been working since then.
Research Interest
Fabrication of micro-/nanostructured functional inorganic materials with well-defined structures as well as their application in catalysis, energy and sensing field
Editor
Work Details
Associate Professor
University of Chinese Academy of Sciences
State Key Laboratory of Biochemical Engineering, Institute of Process Engineering
China
0000-0002-0582-1962 Contribution by Topic Area
Why publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
Which articles are now trending?
Research Articles
- Properties of Indium Antimonide Nanocrystals as Nanoelectronic Elements
- The Influence of Low Pesticide Doses on Fusarium Molds
- Abrasive Wear in Some High Fe-Cr-C Alloy in Cement Powder
- Methodology of the Professional-Business Game for the Development of a Cadet Leader in Professional Training Courses (L-1B) of the Tactical Level of Military Education
- Unveiling the Hidden Beat: Heart Rate Variability and the Vagus Nerve as an Emerging Biomarker in Breast Cancer Management
- Enhancing Material Property Predictions through Optimized KNN Imputation and Deep Neural Network Modeling
