Kunal Das

542 citations
43 papers · 341 · h-index 11

Impact in

Papers in

Kunal Das

40 papers receiving 322 citations

Peers

Kunal Das
Comparison fields: 5 of 40
  • Computational Theory and Mathematics 265
  • Electrical and Electronic Engineering 263
  • Artificial Intelligence 89
  • Atomic and Molecular Physics, and Optics 78
  • Hardware and Architecture 11
Replace Angshuman Khan with:
Angshuman Khan India
Jun‐Cheol Jeon South Korea
Trailokya Nath Sasamal India
Shadi Sheikhfaal United States
Seyed‐Sajad Ahmadpour Iran
Saurabh Kotiyal United States
Seiichiro Tani Japan
J.M. Quintana Spain
Marcel Walter Germany
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Citations per field
00.5×3.4×
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Citations per year

Countries citing papers authored by Kunal Das

Since Specialization
Citations

This map shows the geographic impact of Kunal Das'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 Kunal Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kunal Das more than expected).

Fields of papers citing papers by Kunal Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kunal Das. 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 Kunal Das. The network helps show where Kunal Das may publish in the future.

Co-authors

The 25 scholars most cited alongside Kunal Das, 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 Kunal Das Line = papers co-authored together Kunal Das links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201040
2 201134
3 201625
4 201123
5 201322
6 201019
7
A novel approach of And-Or-Inverter (AOI) gate design for QCA
200918
8 200818
9 201917
10 202212
11
QCA Defect and Fault Analysis of Diverse nanostructure for implementing logic gate
201011
12 20199
13 20149
14 20198
15 20216
16 20196
17 20196
18 20166
19 20125
20 20165

About Kunal Das

Kunal Das is a scholar working on Computational Theory and Mathematics, Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Artificial Intelligence and Computer Networks and Communications, having authored 43 papers that have together received 341 indexed citations. Recurring topics across this work include Quantum-Dot Cellular Automata (24 papers), Advanced Memory and Neural Computing (15 papers), Quantum and electron transport phenomena (9 papers), Quantum Computing Algorithms and Architecture (6 papers), Cellular Automata and Applications (5 papers), Advancements in Semiconductor Devices and Circuit Design (4 papers), Advanced Optical Network Technologies (2 papers) and Semiconductor Quantum Structures and Devices (2 papers). The work is most often cited by research in Computational Theory and Mathematics (265 citations), Electrical and Electronic Engineering (263 citations), Artificial Intelligence (89 citations), Atomic and Molecular Physics, and Optics (78 citations) and Hardware and Architecture (11 citations). Kunal Das has collaborated with scholars based in India, Australia and United States. Frequent co-authors include Debashis De, Mallika De, K. Liu, H. Kargupta, Kanishka Bhaduri, Tanmay De, Ashok Kumar Pradhan, Jadav Chandra Das, Farshad Moradi and Brajesh Kumar Kaushik. Their work appears in journals such as Microsystem Technologies, Journal of Visualized Experiments, Computers & Electrical Engineering, Waste and Biomass Valorization and Molecular Simulation.

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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