Simon Song

1.6k citations
78 papers · 1.3k · h-index 20

Impact in

    • Microfluidic and Capillary Electrophoresis Applications
    • Microfluidic and Bio-sensing Technologies
    • Innovative Microfluidic and Catalytic Techniques Innovation
    • Biosensors and Analytical Detection

Papers in

Simon Song

76 papers receiving 1.3k citations

Peers

Simon Song
Comparison fields: 5 of 94
  • Biomedical Engineering 761
  • Microbiology 96
  • Bioengineering 60
  • Biomaterials 105
  • Organic Chemistry 200
Replace Brian J. Nablo with:
Brian J. Nablo United States
Kristin Sott Sweden
Zhen Guo China
Tatiana Schmatko France
Amartya Sengupta India
Milana Vasudev United States
Mengkun Liu United States
Gui‐Shi Liu China
Sara Peeters Belgium
Simon Song relative to Brian J. Nablo United States Brian J. Nablo's profile →
Citations per field
00.5×10.4×
Brian J. Nablo · 1×
Citations per year

Countries citing papers authored by Simon Song

Since Specialization
Citations

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

Fields of papers citing papers by Simon Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 78 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004136
2 2009129
3 200494
4 201486
5 201559
6 200557
7 202152
8 200748
9 201837
10 201236
11 200833
12 201331
13 201731
14 201529
15 200929
16 201127
17 201225
18 201423
19 201921
20 201620

About Simon Song

Simon Song is a scholar working on Biomedical Engineering, Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Organic Chemistry and Computational Mechanics, having authored 78 papers that have together received 1.3k indexed citations. Recurring topics across this work include Microfluidic and Capillary Electrophoresis Applications (17 papers), Microfluidic and Bio-sensing Technologies (13 papers), Polydiacetylene-based materials and applications (12 papers), Photoreceptor and optogenetics research (12 papers), Micro and Nano Robotics (9 papers), Electrowetting and Microfluidic Technologies (9 papers), Innovative Microfluidic and Catalytic Techniques Innovation (8 papers) and Supramolecular Self-Assembly in Materials (7 papers). The work is most often cited by research in Biomedical Engineering (761 citations), Microbiology (96 citations), Bioengineering (60 citations), Biomaterials (105 citations) and Organic Chemistry (200 citations). Simon Song has collaborated with scholars based in South Korea, Japan and United States. Frequent co-authors include Anup K. Singh, Brian J. Kirby, Jong‐Man Kim, Ilhoon Jang, Bora Yoon, Kazunari Ozasa, Mizuo Maeda, Jeesoo Lee, Sungmin Ryu and Timothy J. Shepodd. Their work appears in journals such as Scientific Reports, Journal of Micromechanics and Microengineering, Sensors and Actuators B Chemical, Physics of Fluids and Lab on a Chip.

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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