Mariya Barch

1.2k total citations · 1 hit paper
9 papers, 621 citations indexed

About

Mariya Barch is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Mariya Barch has authored 9 papers receiving a total of 621 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 3 papers in Atomic and Molecular Physics, and Optics and 2 papers in Cellular and Molecular Neuroscience. Recurrent topics in Mariya Barch's work include Force Microscopy Techniques and Applications (2 papers), Nanoparticle-Based Drug Delivery (2 papers) and Molecular Junctions and Nanostructures (2 papers). Mariya Barch is often cited by papers focused on Force Microscopy Techniques and Applications (2 papers), Nanoparticle-Based Drug Delivery (2 papers) and Molecular Junctions and Nanostructures (2 papers). Mariya Barch collaborates with scholars based in United States, Germany and Luxembourg. Mariya Barch's co-authors include Alan Jasanoff, Satoshi Okada, Peter F. Nielsen, Christian T. Farrar, Nan Li, Wei He, Yue Chen, Ou Chen, Markus Heine and Michael G. Kaul and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Angewandte Chemie International Edition.

In The Last Decade

Mariya Barch

9 papers receiving 610 citations

Hit Papers

Exceedingly small iron oxide nanoparticles as positive MR... 2017 2026 2020 2023 2017 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mariya Barch United States 7 259 248 191 150 96 9 621
Peiwei Yi China 15 609 2.4× 316 1.3× 456 2.4× 271 1.8× 101 1.1× 26 1.1k
Kyung A. Kang United States 19 620 2.4× 147 0.6× 196 1.0× 506 3.4× 315 3.3× 98 1.4k
Jianquan Xu United States 19 228 0.9× 38 0.2× 140 0.7× 616 4.1× 45 0.5× 37 1.1k
Hequn Zhang China 20 1.2k 4.5× 91 0.4× 1.2k 6.5× 248 1.7× 85 0.9× 37 1.8k
Chin‐Tu Chen United States 9 324 1.3× 142 0.6× 150 0.8× 107 0.7× 263 2.7× 40 746
Kiyoung Jeong South Korea 11 206 0.8× 48 0.2× 71 0.4× 80 0.5× 24 0.3× 23 553
Chau‐Hwang Lee Taiwan 18 620 2.4× 52 0.2× 51 0.3× 268 1.8× 20 0.2× 58 1.0k
Martin O. Lenz Germany 16 64 0.2× 53 0.2× 135 0.7× 296 2.0× 42 0.4× 35 800
Junting Liu China 13 190 0.7× 27 0.1× 125 0.7× 102 0.7× 171 1.8× 33 602

Countries citing papers authored by Mariya Barch

Since Specialization
Citations

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

Fields of papers citing papers by Mariya Barch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mariya Barch

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

All Works

9 of 9 papers shown
1.
Chang, Young Hwan, Jeremy W. Linsley, Irina Epstein, et al.. (2020). Single cell tracking based on Voronoi partition via stable matching. 5086–5091. 3 indexed citations
2.
Linsley, Jeremy W., Irina Epstein, Galina Schmunk, et al.. (2019). Automated four-dimensional long term imaging enables single cell tracking within organotypic brain slices to study neurodevelopment and degeneration. Communications Biology. 2(1). 155–155. 24 indexed citations
3.
Yang, Samuel, Marc Berndl, D. Michael Ando, et al.. (2018). Assessing microscope image focus quality with deep learning. BMC Bioinformatics. 19(1). 77–77. 92 indexed citations
4.
He, Wei, Oliver T. Bruns†, Michael G. Kaul, et al.. (2017). Exceedingly small iron oxide nanoparticles as positive MRI contrast agents. Proceedings of the National Academy of Sciences. 114(9). 2325–2330. 396 indexed citations breakdown →
5.
Desai, Mitul, et al.. (2016). Molecular imaging with engineered physiology. Nature Communications. 7(1). 13607–13607. 26 indexed citations
6.
Cocas, Laura, et al.. (2016). Cell Type-Specific Circuit Mapping Reveals the Presynaptic Connectivity of Developing Cortical Circuits. Journal of Neuroscience. 36(11). 3378–3390. 16 indexed citations
7.
Barch, Mariya, Satoshi Okada, Benjamin B. Bartelle, & Alan Jasanoff. (2014). Screen-Based Analysis of Magnetic Nanoparticle Libraries Formed Using Peptidic Iron Oxide Ligands. Journal of the American Chemical Society. 136(36). 12516–12519. 6 indexed citations
8.
Tarsa, Peter B., Ricardo R. Brau, Mariya Barch, et al.. (2007). Detecting Force‐Induced Molecular Transitions with Fluorescence Resonant Energy Transfer. Angewandte Chemie International Edition. 46(12). 1999–2001. 51 indexed citations
9.
Tarsa, Peter B., Ricardo R. Brau, Mariya Barch, et al.. (2007). Detecting Force‐Induced Molecular Transitions with Fluorescence Resonant Energy Transfer. Angewandte Chemie. 119(12). 2045–2047. 7 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026