Sam Chen

5.9k total citations · 5 hit papers
33 papers, 4.7k citations indexed

About

Sam Chen is a scholar working on Molecular Biology, Biomaterials and Cancer Research. According to data from OpenAlex, Sam Chen has authored 33 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 6 papers in Biomaterials and 5 papers in Cancer Research. Recurrent topics in Sam Chen's work include RNA Interference and Gene Delivery (22 papers), Advanced biosensing and bioanalysis techniques (18 papers) and Lipid Membrane Structure and Behavior (8 papers). Sam Chen is often cited by papers focused on RNA Interference and Gene Delivery (22 papers), Advanced biosensing and bioanalysis techniques (18 papers) and Lipid Membrane Structure and Behavior (8 papers). Sam Chen collaborates with scholars based in Canada, Netherlands and United States. Sam Chen's co-authors include Pieter R. Cullis, Jayesh A. Kulkarni, Yuen Yi C. Tam, Roy van der Meel, Dominik Witzigmann, Ying K. Tam, Paulo J.C. Lin, Sarah B. Thomson, Blair R. Leavitt and Alex K. K. Leung and has published in prestigious journals such as Accounts of Chemical Research, ACS Nano and Nature Nanotechnology.

In The Last Decade

Sam Chen

32 papers receiving 4.6k citations

Hit Papers

The current landscape of nucle... 2012 2026 2016 2021 2021 2012 2018 2019 2020 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sam Chen Canada 23 3.9k 715 627 577 455 33 4.7k
Yuen Yi C. Tam Canada 32 3.5k 0.9× 444 0.6× 489 0.8× 478 0.8× 326 0.7× 44 4.2k
Muthusamy Jayaraman United States 21 4.8k 1.2× 606 0.8× 662 1.1× 550 1.0× 443 1.0× 34 5.7k
Steven M. Ansell United States 24 3.7k 1.0× 559 0.8× 988 1.6× 703 1.2× 505 1.1× 47 4.9k
Xucheng Hou United States 21 2.9k 0.7× 610 0.9× 486 0.8× 905 1.6× 468 1.0× 37 4.1k
Martin A. Maier United States 38 6.9k 1.8× 666 0.9× 746 1.2× 753 1.3× 709 1.6× 88 8.0k
Qiang Cheng China 37 5.4k 1.4× 1.2k 1.6× 1.2k 1.9× 850 1.5× 973 2.1× 86 6.9k
Jayesh A. Kulkarni Canada 28 5.8k 1.5× 1.1k 1.5× 1.0k 1.6× 995 1.7× 729 1.6× 47 7.2k
Lei Miao United States 20 2.9k 0.7× 934 1.3× 763 1.2× 1.4k 2.4× 501 1.1× 30 4.2k
Sean C. Semple Canada 25 3.2k 0.8× 647 0.9× 1.4k 2.2× 690 1.2× 308 0.7× 37 4.4k
Xinyao Du United States 6 2.3k 0.6× 304 0.4× 407 0.6× 411 0.7× 284 0.6× 6 2.7k

Countries citing papers authored by Sam Chen

Since Specialization
Citations

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

Fields of papers citing papers by Sam Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sam Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Sam Chen. A scholar is included among the top collaborators of Sam Chen 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 Sam Chen. Sam Chen 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.
Jakubek, Zygmunt J., Sam Chen, Josh Zaifman, Yuen Yi C. Tam, & Shan Zou. (2023). Lipid Nanoparticle and Liposome Reference Materials: Assessment of Size Homogeneity and Long-Term −70 °C and 4 °C Storage Stability. Langmuir. 39(7). 2509–2519. 22 indexed citations
3.
Jakubek, Zygmunt J., et al.. (2022). Development of lipid nanoparticles and liposomes reference materials (II): cytotoxic profiles. Scientific Reports. 12(1). 18071–18071. 37 indexed citations
4.
Kulkarni, Jayesh A., Dominik Witzigmann, Sarah B. Thomson, et al.. (2021). Author Correction: The current landscape of nucleic acid therapeutics. Nature Nanotechnology. 16(7). 841–841. 16 indexed citations
5.
Kulkarni, Jayesh A., Dominik Witzigmann, Sarah B. Thomson, et al.. (2021). The current landscape of nucleic acid therapeutics. Nature Nanotechnology. 16(6). 630–643. 934 indexed citations breakdown →
6.
Meel, Roy van der, Sam Chen, Josh Zaifman, et al.. (2021). Modular Lipid Nanoparticle Platform Technology for siRNA and Lipophilic Prodrug Delivery. Small. 17(37). e2103025–e2103025. 42 indexed citations
7.
Witzigmann, Dominik, Jayesh A. Kulkarni, Jerry Leung, et al.. (2020). Lipid nanoparticle technology for therapeutic gene regulation in the liver. Advanced Drug Delivery Reviews. 159. 344–363. 273 indexed citations breakdown →
9.
Chen, Sam, Josh Zaifman, Jayesh A. Kulkarni, et al.. (2018). Dexamethasone prodrugs as potent suppressors of the immunostimulatory effects of lipid nanoparticle formulations of nucleic acids. Journal of Controlled Release. 286. 46–54. 47 indexed citations
10.
Neumann, Ursula, et al.. (2017). Lipid nanoparticle delivery of glucagon receptor siRNA improves glucose homeostasis in mouse models of diabetes. Molecular Metabolism. 6(10). 1161–1172. 26 indexed citations
11.
Zhang, Kaixin, Yuen Yi C. Tam, Ying K. Tam, et al.. (2016). A Glu-urea-Lys Ligand-conjugated Lipid Nanoparticle/siRNA System Inhibits Androgen Receptor Expression In Vivo. Molecular Therapy — Nucleic Acids. 5. e348–e348. 49 indexed citations
12.
Kulkarni, Jayesh A., et al.. (2016). Design of lipid nanoparticles for in vitro and in vivo delivery of plasmid DNA. Nanomedicine Nanotechnology Biology and Medicine. 13(4). 1377–1387. 176 indexed citations
13.
Wang, Haitang, Yuen Yi C. Tam, Sam Chen, et al.. (2016). The Niemann-Pick C1 Inhibitor NP3.47 Enhances Gene Silencing Potency of Lipid Nanoparticles Containing siRNA. Molecular Therapy. 24(12). 2100–2108. 44 indexed citations
14.
Chen, Sam, Yuen Yi C. Tam, Paulo J.C. Lin, et al.. (2016). Influence of particle size on the in vivo potency of lipid nanoparticle formulations of siRNA. Journal of Controlled Release. 235. 236–244. 282 indexed citations
15.
Walsh, Colin, Nathan M. Belliveau, Jens Huft, et al.. (2014). Microfluidic-Based Manufacture of siRNA-Lipid Nanoparticles for Therapeutic Applications. Methods in molecular biology. 1141. 109–120. 47 indexed citations
16.
Chen, Sam, Yuen Yi C. Tam, Paulo J.C. Lin, et al.. (2014). Development of lipid nanoparticle formulations of siRNA for hepatocyte gene silencing following subcutaneous administration. Journal of Controlled Release. 196. 106–112. 127 indexed citations
17.
Mui, Barbara L., Ying K. Tam, Muthusamy Jayaraman, et al.. (2013). Influence of Polyethylene Glycol Lipid Desorption Rates on Pharmacokinetics and Pharmacodynamics of siRNA Lipid Nanoparticles. Molecular Therapy — Nucleic Acids. 2. e139–e139. 390 indexed citations
18.
Tam, Yuen Yi C., Sam Chen, & Pieter R. Cullis. (2013). Advances in Lipid Nanoparticles for siRNA Delivery. Pharmaceutics. 5(3). 498–507. 168 indexed citations
19.
Tam, Yuen Yi C., Sam Chen, Josh Zaifman, et al.. (2012). Small molecule ligands for enhanced intracellular delivery of lipid nanoparticle formulations of siRNA. Nanomedicine Nanotechnology Biology and Medicine. 9(5). 665–674. 36 indexed citations
20.
Belliveau, Nathan M., Jens Huft, Paulo J.C. Lin, et al.. (2012). Microfluidic Synthesis of Highly Potent Limit-size Lipid Nanoparticles for In Vivo Delivery of siRNA. Molecular Therapy — Nucleic Acids. 1. e37–e37. 548 indexed citations breakdown →

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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