Debashis Panda

2.4k citations
58 papers · 2.0k · h-index 23

Impact in

Papers in

Debashis Panda

56 papers receiving 2.0k citations

Peers

Debashis Panda
Comparison fields: 5 of 49
  • Polymers and Plastics 544
  • Cellular and Molecular Neuroscience 586
  • Electrical and Electronic Engineering 1.8k
  • Materials Chemistry 750
  • Electronic, Optical and Magnetic Materials 184
Replace Wuhong Xue with:
Wuhong Xue China
Seul Ji Song South Korea
Xiang‐Shu Li South Korea
Jae Hyuck Jang South Korea
Minseok Jo South Korea
Hyunsang Hwang South Korea
Tsung‐Ming Tsai Taiwan
Padinhare Cholakkal Harikesh Singapore
Sunghoon Song South Korea
S. Maikap Taiwan
Debashis Panda relative to Wuhong Xue China Wuhong Xue's profile →
Citations per field
00.5×2.7×
Wuhong Xue · 1×
Citations per year

Countries citing papers authored by Debashis Panda

Since Specialization
Citations

This map shows the geographic impact of Debashis Panda's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Debashis Panda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debashis Panda more than expected).

Fields of papers citing papers by Debashis Panda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Debashis Panda. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Debashis Panda. The network helps show where Debashis Panda may publish in the future.

Co-authors

The 25 scholars most cited alongside Debashis Panda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Debashis Panda Line = papers co-authored together Debashis Panda links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013242
2 2016196
3 2013184
4 2019107
5 201496
6 201889
7 201288
8 201487
9 201685
10 201557
11 201157
12 201953
13 201051
14 201550
15 202045
16 201944
17 202038
18 202035
19 202330
20 202129

About Debashis Panda

Debashis Panda is a scholar working on Electrical and Electronic Engineering, Materials Chemistry, Cellular and Molecular Neuroscience, Polymers and Plastics and Electronic, Optical and Magnetic Materials, having authored 58 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (37 papers), Ferroelectric and Negative Capacitance Devices (16 papers), Neuroscience and Neural Engineering (14 papers), Transition Metal Oxide Nanomaterials (14 papers), Semiconductor materials and devices (10 papers), Electronic and Structural Properties of Oxides (9 papers), Photoreceptor and optogenetics research (9 papers) and ZnO doping and properties (7 papers). The work is most often cited by research in Polymers and Plastics (544 citations), Cellular and Molecular Neuroscience (586 citations), Electrical and Electronic Engineering (1.8k citations), Materials Chemistry (750 citations) and Electronic, Optical and Magnetic Materials (184 citations). Debashis Panda has collaborated with scholars based in India, Taiwan and United Kingdom. Frequent co-authors include Tseung‐Yuen Tseng, Firman Mangasa Simanjuntak, Kung‐Hwa Wei, S. K. Ray, A. Dhar, Sridhar Chandrasekaran, Chun-Yang Huang, Muhammad Ismail, Tsung-Ling Tsai and Ejaz Ahmed. Their work appears in journals such as Journal of Applied Physics, Semiconductor Science and Technology, Applied Physics Letters, Nanoscale Research Letters and Scientific Reports.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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