Camelia Prodan

1.3k total citations
28 papers, 994 citations indexed

About

Camelia Prodan is a scholar working on Atomic and Molecular Physics, and Optics, Biomedical Engineering and Cell Biology. According to data from OpenAlex, Camelia Prodan has authored 28 papers receiving a total of 994 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Atomic and Molecular Physics, and Optics, 13 papers in Biomedical Engineering and 4 papers in Cell Biology. Recurrent topics in Camelia Prodan's work include Topological Materials and Phenomena (12 papers), Microfluidic and Bio-sensing Technologies (8 papers) and Quantum Mechanics and Non-Hermitian Physics (5 papers). Camelia Prodan is often cited by papers focused on Topological Materials and Phenomena (12 papers), Microfluidic and Bio-sensing Technologies (8 papers) and Quantum Mechanics and Non-Hermitian Physics (5 papers). Camelia Prodan collaborates with scholars based in United States and China. Camelia Prodan's co-authors include Emil Prodan, John H. Miller, Kai Qian, J. R. Claycomb, Kai Chen, Frank R. Mayo, Matthew Weiner, Xiang Ni, Alexander B. Khanikaev and Andrea Alù and has published in prestigious journals such as Physical Review Letters, Nature Communications and Journal of Applied Physics.

In The Last Decade

Camelia Prodan

26 papers receiving 973 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Camelia Prodan United States 13 614 323 171 157 119 28 994
Weining Man United States 13 466 0.8× 217 0.7× 322 1.9× 139 0.9× 301 2.5× 36 979
Eliane Trepagnier United States 6 272 0.4× 417 1.3× 110 0.6× 33 0.2× 185 1.6× 6 689
F. Falo Spain 21 538 0.9× 132 0.4× 145 0.8× 85 0.5× 83 0.7× 61 1.3k
Yoshihiro Murayama Japan 14 201 0.3× 185 0.6× 105 0.6× 38 0.2× 192 1.6× 54 735
Kranthi K. Mandadapu United States 17 197 0.3× 209 0.6× 213 1.2× 26 0.2× 36 0.3× 47 859
Alexey Yamilov United States 20 948 1.5× 291 0.9× 285 1.7× 136 0.9× 548 4.6× 77 1.5k
Carl P. Goodrich United States 16 151 0.2× 176 0.5× 498 2.9× 53 0.3× 31 0.3× 26 911
Carolyn L. Phillips United States 17 134 0.2× 143 0.4× 533 3.1× 96 0.6× 55 0.5× 26 930
I-Min Jiang Taiwan 12 119 0.2× 109 0.3× 95 0.6× 193 1.2× 82 0.7× 64 517
M. Sammon United States 17 366 0.6× 127 0.4× 265 1.5× 547 3.5× 87 0.7× 36 1.1k

Countries citing papers authored by Camelia Prodan

Since Specialization
Citations

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

Fields of papers citing papers by Camelia Prodan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Camelia Prodan

This figure shows the co-authorship network connecting the top 25 collaborators of Camelia Prodan. A scholar is included among the top collaborators of Camelia Prodan 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 Camelia Prodan. Camelia Prodan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Liu, Xiaoyu, et al.. (2024). Observation of D-class topology in an acoustic metamaterial. Science Bulletin. 69(7). 893–900. 8 indexed citations
2.
Cerjan, Alexander, et al.. (2023). Revealing topology in metals using experimental protocols inspired by K-theory. Nature Communications. 14(1). 17 indexed citations
3.
Prodan, Emil, et al.. (2020). Demonstration of Dynamic Topological Pumping Across Incommensurate Acoustic Meta-Crystals. arXiv (Cornell University). 4 indexed citations
4.
Prodan, Emil, et al.. (2020). Experimental Demonstration of Dynamic Topological Pumping across Incommensurate Bilayered Acoustic Metamaterials. Physical Review Letters. 125(22). 224301–224301. 71 indexed citations
5.
Qian, Kai, et al.. (2020). Observation of Flat Frequency Bands at Open Edges and Antiphase Boundary Seams in Topological Mechanical Metamaterials. Physical Review Letters. 125(22). 225501–225501. 6 indexed citations
6.
Ni, Xiang, Kai Chen, Matthew Weiner, et al.. (2019). Observation of Hofstadter butterfly and topological edge states in reconfigurable quasi-periodic acoustic crystals. Communications Physics. 2(1). 101 indexed citations
7.
Prodan, Camelia, et al.. (2019). Experimentally measured phonon spectrum of microtubules. Journal of Physics D Applied Physics. 53(2). 25401–25401. 1 indexed citations
8.
Prodan, Emil, et al.. (2019). Observation of Topological Edge Modes in a Quasiperiodic Acoustic Waveguide. Physical Review Letters. 122(9). 95501–95501. 75 indexed citations
9.
Prodan, Emil, et al.. (2019). Topological Phonons in Microtubules: The Link between Local Structure and Dynamics of Microtubules. Biophysical Journal. 116(3). 258a–258a.
10.
Prodan, Emil, et al.. (2017). Dynamical Majorana edge modes in a broad class of topological mechanical systems. Nature Communications. 8(1). 14587–14587. 48 indexed citations
11.
Thomas, G. A., et al.. (2013). Dynamic, Nano-Probe Measurement of Complex Impedance near Single Yeast Cells. Biophysical Journal. 104(2). 529a–529a. 1 indexed citations
12.
Prodan, Camelia & Emil Prodan. (2013). Topological Phonon Modes and their Role in Dynamic Instability of Microtubules. Biophysical Journal. 104(2). 145a–145a. 13 indexed citations
13.
Kanwal, Alokik, Shanmugamurthy Lakshmanan, Anitha Patlolla, et al.. (2013). Scalable nano-bioprobes with sub-cellular resolution for cell detection. Biosensors and Bioelectronics. 45. 267–273. 4 indexed citations
14.
Deek, Matthew P., et al.. (2012). Extracellular fluid conductivity analysis by dielectric spectroscopy forin vitrodetermination of cortical tissue vitality. Physiological Measurement. 33(7). 1249–1260. 2 indexed citations
15.
Zhang, Chi, Lee Slater, & Camelia Prodan. (2012). Complex Dielectric Properties of Sulfate-Reducing Bacteria Suspensions. Geomicrobiology Journal. 30(6). 490–496. 11 indexed citations
16.
Deek, Matthew P., et al.. (2010). Design and implementation of a novel superfusion system forex vivocharacterization of neural tissue by dielectric spectroscopy (DS). Physiological Measurement. 32(2). 195–205. 2 indexed citations
17.
Prodan, Emil & Camelia Prodan. (2009). Topological Phonon Modes and Their Role in Dynamic Instability of Microtubules. Physical Review Letters. 103(24). 248101–248101. 275 indexed citations
18.
Prodan, Camelia, et al.. (2009). Quantifying the membrane potential during E. coli growth stages. Biophysical Chemistry. 146(2-3). 133–137. 62 indexed citations
19.
Prodan, Emil, Camelia Prodan, & John H. Miller. (2008). The Dielectric Response of Spherical Live Cells in Suspension: An Analytic Solution. Biophysical Journal. 95(9). 4174–4182. 65 indexed citations
20.
Prodan, Camelia & Emil Prodan. (1999). The dielectric behaviour of living cell suspensions. Journal of Physics D Applied Physics. 32(3). 335–343. 42 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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