Poly Karmakar

897 total citations · 1 hit paper
33 papers, 671 citations indexed

About

Poly Karmakar is a scholar working on Biomedical Engineering, Computational Mechanics and Electrical and Electronic Engineering. According to data from OpenAlex, Poly Karmakar has authored 33 papers receiving a total of 671 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Biomedical Engineering, 11 papers in Computational Mechanics and 11 papers in Electrical and Electronic Engineering. Recurrent topics in Poly Karmakar's work include Nanofluid Flow and Heat Transfer (21 papers), Electrohydrodynamics and Fluid Dynamics (7 papers) and Lattice Boltzmann Simulation Studies (6 papers). Poly Karmakar is often cited by papers focused on Nanofluid Flow and Heat Transfer (21 papers), Electrohydrodynamics and Fluid Dynamics (7 papers) and Lattice Boltzmann Simulation Studies (6 papers). Poly Karmakar collaborates with scholars based in India, Malaysia and Italy. Poly Karmakar's co-authors include Sanatan Das, Asgar Ali, Rabindra Nath Jana, Soumitra Sarkar, Puja Paul, R. N. Jana, Sayan Das, Saeed Dinarvand, Oluwole Daniel Makinde and Adeeb Noor and has published in prestigious journals such as International Journal of Heat and Mass Transfer, Physics of Fluids and Sensors and Actuators A Physical.

In The Last Decade

Poly Karmakar

31 papers receiving 632 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A neural network approach to explore bioelectromagnetics aspects of blood circulation conveying tetra-hybrid nanoparticles and microbes in a ciliary artery with an endoscopy span

2024 65 citations Poly Karmakar, Sanatan Das Engineering Applications of Artificial Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Poly Karmakar India	16	528	286	184	133	93	33	671
Rabindra Nath Jana India	18	828 1.6×	601 2.1×	534 2.9×	55 0.4×	60 0.6×	59	923
Satya Deo India	19	574 1.1×	681 2.4×	147 0.8×	141 1.1×	142 1.5×	85	950
B. S. Tilley United States	10	175 0.3×	327 1.1×	100 0.5×	110 0.8×	55 0.6×	47	500
Fengqin Li China	14	437 0.8×	91 0.3×	208 1.1×	123 0.9×	18 0.2×	26	564
Mushtaq Ahmad Pakistan	14	475 0.9×	245 0.9×	282 1.5×	42 0.3×	58 0.6×	33	636
Juan-Cheng Yang China	13	509 1.0×	312 1.1×	294 1.6×	173 1.3×	82 0.9×	62	791
Adeel Ahmad Pakistan	16	746 1.4×	557 1.9×	530 2.9×	43 0.3×	104 1.1×	54	876
Muhammad Yousuf Rafiq Pakistan	17	813 1.5×	600 2.1×	439 2.4×	29 0.2×	185 2.0×	86	883
Tahir Saeed Khan Pakistan	12	277 0.5×	166 0.6×	214 1.2×	53 0.4×	18 0.2×	21	446
A.S. Alqahtani Saudi Arabia	19	843 1.6×	578 2.0×	635 3.5×	26 0.2×	79 0.8×	68	973

Countries citing papers authored by Poly Karmakar

Since Specialization

Citations

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

Fields of papers citing papers by Poly Karmakar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Poly Karmakar

This figure shows the co-authorship network connecting the top 25 collaborators of Poly Karmakar. A scholar is included among the top collaborators of Poly Karmakar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Poly Karmakar. Poly Karmakar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Karmakar, Poly, et al.. (2026). Deep learning model for nano-blood transport in a rotating EM-actuated microchannel with thermal modulation: Implications for atherectomy devices. International Communications in Heat and Mass Transfer. 172. 110513–110513.

Das, Sanatan & Poly Karmakar. (2025). AI modeling of Ag-ZnO milk dynamics in a squarely elevated electromagnetic tunnel with dynamic thermal modulation. Computational Biology and Chemistry. 120(Pt 1). 108668–108668. 3 indexed citations

Paul, Puja, Sanatan Das, & Poly Karmakar. (2025). Neural computing approach in simulating electrodynamics of magnetized blood enhanced with penta-hybrid nanoparticles in a multi-stenosed artery. International Journal of Heat and Mass Transfer. 246. 127092–127092. 11 indexed citations

Kundu, D., Sanatan Das, & Poly Karmakar. (2025). Intelligent predictive biomechanics of intensely magnetized microbial penta-hybrid nano-blood in an electrified endoscopic ciliated artery. Computational Biology and Chemistry. 120(Pt 1). 108766–108766.

Noor, Adeeb, et al.. (2025). AI-based testing of urine containing penta hybrid nanoparticles within a charged bioactive rotational channel under strong magnetic fields: Implications for bioengineering. International Communications in Heat and Mass Transfer. 164. 108852–108852. 7 indexed citations

Karmakar, Poly. (2025). Dynamic prediction of nano-milk transport in a parabolically elevated electromagnetic channel with parabolic thermal ramping: A Python-based machine learning approach. Physics of Fluids. 37(7). 4 indexed citations

Karmakar, Poly & Sanatan Das. (2025). Integration of artificial intelligence (AI) to predict the behavior of milk enhanced with silver and zinc oxide nanoparticles when subjected to rapid heating in a movable electromagnetic channel. Modern Physics Letters B. 39(26). 12 indexed citations

Das, Sanatan, et al.. (2025). Electrothermal flow of CNT-blood on a sensor plate with ramping shear and thermal flux: Electromagnetic biosensor applications. Chinese Journal of Physics. 98. 1084–1102. 1 indexed citations

Karmakar, Poly, et al.. (2025). AI-based prediction of flow dynamics of blood blended with gold and maghemite nanoparticles in an electromagnetic microchannel under abruptly changes in pressure gradient. Electromagnetic Biology and Medicine. 44(3). 294–324. 9 indexed citations

10.

Karmakar, Poly, et al.. (2024). Dynamics prediction using an artificial neural network for a weakly conductive ionized fluid streamed over a vibrating electromagnetic plate. The European Physical Journal Plus. 139(5). 44 indexed citations

11.

Karmakar, Poly & Sanatan Das. (2024). A neural network approach to explore bioelectromagnetics aspects of blood circulation conveying tetra-hybrid nanoparticles and microbes in a ciliary artery with an endoscopy span. Engineering Applications of Artificial Intelligence. 133. 108298–108298. 65 indexed citations breakdown →

12.

Das, Sanatan, et al.. (2024). Shear‐driven flow of an ionic fluid in a narrow vertical channel under a Hall electric field. ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. 104(9). 13 indexed citations

13.

Karmakar, Poly, Sanatan Das, Rabindra Nath Jana, & Oluwole Daniel Makinde. (2024). Dynamic response of a weakly ionized fluid in a vibrating Riga channel exposed to intense electromagnetic rotation. Microfluidics and Nanofluidics. 28(10). 20 indexed citations

14.

Paul, Puja, et al.. (2024). Demonstration of angioplasty using a balloon catheter in tetra-hybrid nano-bloodstream within an electrified stenotic arterial cavity under a magnetic field: Artificial neural network analysis. Biomedical Signal Processing and Control. 96. 106549–106549. 29 indexed citations

15.

Das, Sanatan, Poly Karmakar, Sayan Das, & Saeed Dinarvand. (2024). AI-led study of dynamic changes in milk containing hybrid nanoparticles in an electromagnetically vibrated channel subjected to thermal oscillations and rapid pressure changes: Implications for dairy industry. Chinese Journal of Physics. 93. 271–309. 22 indexed citations

16.

Karmakar, Poly & Sanatan Das. (2024). AI-powered computational analysis of dynamic responses in a vibrating Riga sensor within a reactive platinum -cerium oxide-water mixture. Sensors and Actuators A Physical. 381. 116028–116028. 22 indexed citations

17.

Das, Sanatan, et al.. (2024). Modeling temporal dynamics of a radiant Casson fluid near a steeped plate emitting heat and mass fluxes. ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. 104(11). 21 indexed citations

18.

Karmakar, Poly, et al.. (2023). EDL transport of blood-infusing tetra-hybrid nano-additives through a cilia-layered endoscopic arterial path. Materials Today Communications. 36. 106772–106772. 47 indexed citations

19.

Karmakar, Poly & Sanatan Das. (2023). EDL Induced Electro-magnetized Modified Hybrid Nano-blood Circulation in an Endoscopic Fatty Charged Arterial Indented Tract. Cardiovascular Engineering and Technology. 15(2). 171–198. 22 indexed citations

20.

Das, Sanatan, Poly Karmakar, & Asgar Ali. (2022). Electrothermal blood streaming conveying hybridized nanoparticles in a non-uniform endoscopic conduit. Medical & Biological Engineering & Computing. 60(11). 3125–3151. 54 indexed citations

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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