Todd Sulchek

6.9k total citations · 1 hit paper
118 papers, 5.3k citations indexed

About

Todd Sulchek is a scholar working on Biomedical Engineering, Atomic and Molecular Physics, and Optics and Molecular Biology. According to data from OpenAlex, Todd Sulchek has authored 118 papers receiving a total of 5.3k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Biomedical Engineering, 34 papers in Atomic and Molecular Physics, and Optics and 32 papers in Molecular Biology. Recurrent topics in Todd Sulchek's work include Force Microscopy Techniques and Applications (32 papers), Microfluidic and Bio-sensing Technologies (30 papers) and 3D Printing in Biomedical Research (22 papers). Todd Sulchek is often cited by papers focused on Force Microscopy Techniques and Applications (32 papers), Microfluidic and Bio-sensing Technologies (30 papers) and 3D Printing in Biomedical Research (22 papers). Todd Sulchek collaborates with scholars based in United States, Türkiye and China. Todd Sulchek's co-authors include Wenwei Xu, Roman Mezencev, John F. McDonald, Byung Kyu Kim, Lijuan Wang, Thomas H. Barker, Vincent F. Fiore, S. C. Minne, Jonathan D. Adams and Andrés J. Garcı́a and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Todd Sulchek

114 papers receiving 5.2k citations

Hit Papers

Cell Stiffness Is a Biomarker of the Metastatic Potential... 2012 2026 2016 2021 2012 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Todd Sulchek United States 40 2.0k 1.3k 1.2k 1.0k 622 118 5.3k
Robert Ros United States 34 1.6k 0.8× 1.2k 0.9× 1.2k 1.0× 563 0.5× 793 1.3× 92 3.9k
Paul J. Campagnola United States 45 3.6k 1.8× 1.7k 1.3× 1.5k 1.3× 884 0.9× 297 0.5× 126 8.3k
K. Kilian Australia 46 3.5k 1.7× 1.6k 1.2× 685 0.6× 1.6k 1.6× 803 1.3× 185 7.4k
Małgorzata Lekka Poland 32 1.5k 0.8× 1.3k 1.0× 1.4k 1.1× 1.9k 1.8× 220 0.4× 160 4.9k
Khalid Salaita United States 48 2.7k 1.3× 2.8k 2.1× 2.1k 1.7× 1.8k 1.7× 838 1.3× 145 7.0k
Ralf P. Richter Germany 46 2.0k 1.0× 4.2k 3.2× 1.5k 1.3× 1.1k 1.0× 1.0k 1.6× 139 7.8k
Wolfgang H. Goldmann Germany 42 1.6k 0.8× 1.2k 0.9× 739 0.6× 2.1k 2.0× 250 0.4× 138 5.0k
Motomu Tanaka Germany 37 1.9k 0.9× 2.8k 2.1× 1.1k 0.9× 562 0.5× 1.0k 1.7× 244 6.2k
Karsten König Germany 55 4.8k 2.4× 1.9k 1.4× 744 0.6× 514 0.5× 502 0.8× 294 10.3k
Stephan Sylvest Keller Denmark 36 1.4k 0.7× 1.2k 0.9× 781 0.6× 285 0.3× 1.1k 1.8× 176 5.0k

Countries citing papers authored by Todd Sulchek

Since Specialization
Citations

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

Fields of papers citing papers by Todd Sulchek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Todd Sulchek

This figure shows the co-authorship network connecting the top 25 collaborators of Todd Sulchek. A scholar is included among the top collaborators of Todd Sulchek 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 Todd Sulchek. Todd Sulchek 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.
Sulchek, Todd, et al.. (2024). Adhesion-based high-throughput label-free cell sorting using ridged microfluidic channels. Soft Matter. 20(8). 1913–1921. 1 indexed citations
2.
Young, Katherine, et al.. (2023). Correlating mechanical and gene expression data on the single cell level to investigate metastatic phenotypes. iScience. 26(4). 106393–106393. 7 indexed citations
3.
Kiru, Louise, Aimen Zlitni, Aidan M. Tousley, et al.. (2022). In vivo imaging of nanoparticle-labeled CAR T cells. Proceedings of the National Academy of Sciences. 119(6). 96 indexed citations
4.
5.
Nazemi, Mohammadreza, Pengfei Ou, Luke Soule, et al.. (2020). Electrosynthesis of Ammonia Using Porous Bimetallic Pd–Ag Nanocatalysts in Liquid- and Gas-Phase Systems. ACS Catalysis. 10(17). 10197–10206. 41 indexed citations
6.
Stone, Edwin M., Andrew P. Voigt, Joseph C. Giacalone, et al.. (2020). Label-free microfluidic enrichment of photoreceptor cells. Experimental Eye Research. 199. 108166–108166. 7 indexed citations
7.
Young, Katherine, et al.. (2020). Correlating Mechanical and Gene Expression Data on the Single Cell Level to Investigate Metastasis. Biophysical Journal. 118(3). 189a–189a. 1 indexed citations
9.
Fiore, Vincent F., Simon S. Wong, Chunting Tan, et al.. (2018). αvβ3 Integrin drives fibroblast contraction and strain stiffening of soft provisional matrix during progressive fibrosis. JCI Insight. 3(20). 91 indexed citations
10.
Wang, Ke, Guorong Li, A. Thomas Read, et al.. (2018). The relationship between outflow resistance and trabecular meshwork stiffness in mice. Scientific Reports. 8(1). 5848–5848. 55 indexed citations
11.
Sulchek, Todd, et al.. (2017). Reconstructing Multiwell Potentials with Steep Gradients Using Stochastically Excited Spring Probes. The Journal of Physical Chemistry C. 121(13). 7248–7258.
12.
Waller, Edmund K., Anna B. Morris, Ashley D. Staton, et al.. (2017). Optimization of Ex Vivo Activation and Expansion of T Cells for Therapy of Lymphoma Via Antagonism of PI3K δ and Vasoactive Intestinal Peptide Signaling. Blood. 130. 3195–3195. 1 indexed citations
13.
Lam, Wilbur A., et al.. (2017). Enhancing size based size separation through vertical focus microfluidics using secondary flow in a ridged microchannel. Scientific Reports. 7(1). 17375–17375. 15 indexed citations
14.
Suo, Jin, et al.. (2016). Force and torque on spherical particles in micro-channel flows using computational fluid dynamics. Royal Society Open Science. 3(7). 160298–160298. 5 indexed citations
15.
Chojnowski, Jena L., et al.. (2016). Cellular Stiffness as a Novel Stemness Marker in the Corneal Limbus. Biophysical Journal. 111(8). 1761–1772. 28 indexed citations
16.
Enemchukwu, Nduka, Ricardo Cruz‐Acuña, Christopher Johnson, et al.. (2015). Synthetic matrices reveal contributions of ECM biophysical and biochemical properties to epithelial morphogenesis. The Journal of Cell Biology. 212(1). 113–124. 104 indexed citations
17.
Sulchek, Todd, et al.. (2013). Stiffness Dependent Separation of Cells in a Microfluidic Device. Bulletin of the American Physical Society. 2012. 3 indexed citations
18.
Xu, Wenwei, Roman Mezencev, Byung Kyu Kim, et al.. (2013). Cell stiffness is a biomarker of the metastatic potential of ovarian cancer cells. Bulletin of the American Physical Society. 2013.
19.
Xu, Wenwei, Roman Mezencev, Byung Kyu Kim, et al.. (2012). Cell Stiffness Is a Biomarker of the Metastatic Potential of Ovarian Cancer Cells. PLoS ONE. 7(10). e46609–e46609. 603 indexed citations breakdown →
20.
Blanchette, Craig, Jenny A. Cappuccio, Edward A. Kuhn, et al.. (2008). Atomic force microscopy differentiates discrete size distributions between membrane protein containing and empty nanolipoprotein particles. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1788(3). 724–731. 35 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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