Verjesh Kumar Magotra 編集者 at IgMin Research
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バイオグラフィー
Dr. Verjesh Kumar Magotra currently works, as Assitant Professor at Nano Information Technology Academy (NITA) QSRC Dongguk University South Korea. He has done his post-doctorate for 2 years in the same dept before. Dr Magotra does his PhD at Dongguk University South Korea. He has done his graduation, masters and MPhil from Indian universities.
Dr Magotraa starts working with Prof T.W.kang renowned scientist in Korea under his guidance. Working on Green energy, membranes, electrodes, different biofuels, soil-based Fuel cells, Waste Management, Soil pollution, Soil Science soil-based systems Materials Physics, and Environmental Science. Mostly fabrication and designing of MFC fuel cells' single, double and triple chambers including the fabrication of stacks for higher power generation and Researcher in Renewable green energy from the last 8 years
研究の興味
Fuel cells, soil fuel cell, brick type, bio fuels, single, double and triple chamber. Anodes cathodes, stacks
Editor
仕事内容
Assitant Professor
Dongguk University
Department of Nano Information Technology Academy
South Korea
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
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研究論文
- Contamination in Heat Exchangers: Types, Energy Effects and Prevention Methods
- Investigation of Lateral Vibrations in Turbine-generator Unit 5 of the Inga 2 Hydroelectric Power Plant
- The Lukala Cement Plant's Life Cycle Analysis: Towards a More Sustainable Production
- Balancing Act: Exploring the Interplay Between Human Judgment and Artificial Intelligence in Problem-solving, Creativity, and Decision-making
- Improved Energy Valley Optimizer with Levy Flight for Optimization Problems
- Deep Learning-based Multi-class Three-dimensional (3-D) Object Classification using Phase-only Digital Holographic Information
