N. Sanjeeva Murthy

8.7k total citations
231 papers, 6.9k citations indexed

About

N. Sanjeeva Murthy is a scholar working on Polymers and Plastics, Materials Chemistry and Biomaterials. According to data from OpenAlex, N. Sanjeeva Murthy has authored 231 papers receiving a total of 6.9k indexed citations (citations by other indexed papers that have themselves been cited), including 94 papers in Polymers and Plastics, 54 papers in Materials Chemistry and 47 papers in Biomaterials. Recurrent topics in N. Sanjeeva Murthy's work include Polymer crystallization and properties (64 papers), Polymer Nanocomposites and Properties (40 papers) and Fiber-reinforced polymer composites (20 papers). N. Sanjeeva Murthy is often cited by papers focused on Polymer crystallization and properties (64 papers), Polymer Nanocomposites and Properties (40 papers) and Fiber-reinforced polymer composites (20 papers). N. Sanjeeva Murthy collaborates with scholars based in United States, India and France. N. Sanjeeva Murthy's co-authors include H. B. Minor, Ray H. Baughman, Vinod B. Damodaran, David T. Grubb, Joachim Kohn, Edward W. Castner, Cherry S. Santos, L. W. Shacklette, S. M. Aharoni and Robert G. Bray and has published in prestigious journals such as Journal of the American Chemical Society, Advanced Materials and Nature Communications.

In The Last Decade

N. Sanjeeva Murthy

218 papers receiving 6.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
N. Sanjeeva Murthy United States 41 2.9k 1.6k 1.5k 1.1k 1.0k 231 6.9k
Pingchuan Sun China 49 2.0k 0.7× 3.2k 2.0× 1.3k 0.9× 1.8k 1.6× 1.0k 1.0× 202 7.2k
Jeffrey T. Koberstein United States 51 3.6k 1.2× 4.1k 2.6× 1.1k 0.8× 1.9k 1.6× 1.4k 1.4× 139 10.1k
Stephen J. Picken Netherlands 45 2.4k 0.8× 2.7k 1.7× 1.1k 0.7× 1.2k 1.1× 1.1k 1.1× 245 7.5k
Jonathan Sokolov United States 47 1.9k 0.6× 3.3k 2.1× 1.2k 0.8× 2.0k 1.8× 1.0k 1.0× 176 7.5k
Zhong‐Yuan Lu China 48 2.7k 0.9× 4.6k 2.9× 1.5k 1.0× 1.9k 1.7× 1.1k 1.1× 338 9.5k
Christopher D. Easton Australia 45 1.1k 0.4× 2.4k 1.5× 1.0k 0.7× 1.9k 1.7× 2.7k 2.7× 148 7.2k
Sergio Moya Spain 47 1.2k 0.4× 3.1k 2.0× 1.8k 1.2× 2.2k 2.0× 1.1k 1.1× 306 8.9k
Rodney D. Priestley United States 40 1.7k 0.6× 3.3k 2.1× 626 0.4× 1.6k 1.4× 730 0.7× 139 6.2k
Vera Bocharova United States 41 2.3k 0.8× 1.6k 1.0× 331 0.2× 1.0k 0.9× 2.2k 2.2× 122 5.4k
Nigel Kirby Australia 42 719 0.2× 2.1k 1.3× 1.5k 1.0× 1.1k 0.9× 902 0.9× 177 6.6k

Countries citing papers authored by N. Sanjeeva Murthy

Since Specialization
Citations

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

Fields of papers citing papers by N. Sanjeeva Murthy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by N. Sanjeeva Murthy. 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 N. Sanjeeva Murthy. The network helps show where N. Sanjeeva Murthy may publish in the future.

Co-authorship network of co-authors of N. Sanjeeva Murthy

This figure shows the co-authorship network connecting the top 25 collaborators of N. Sanjeeva Murthy. A scholar is included among the top collaborators of N. Sanjeeva Murthy 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 N. Sanjeeva Murthy. N. Sanjeeva Murthy 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.
Murthy, N. Sanjeeva, et al.. (2025). Self‐assembly of short biopeptides onto skin tissue components studied using QCM‐D. International Journal of Cosmetic Science. 47(4). 554–562. 2 indexed citations
2.
3.
Murthy, N. Sanjeeva, et al.. (2025). Martini 3 coarse-grained model of enzymes: Framework with validation by all-atom simulations and x-ray diffraction measurements. The Journal of Chemical Physics. 162(13). 1 indexed citations
4.
Ramírez, César E., James Byrnes, Eman Ahmed, et al.. (2025). SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles. Biophysical Journal. 124(21). 3772–3786.
5.
Cubuk, Jasmine, Arjun Singh, César E. Ramírez, et al.. (2025). Phosphorylation toggles the SARS-CoV-2 nucleocapsid protein between two membrane-associated condensate states. Nature Communications. 16(1). 7970–7970.
6.
Eroğlu, İpek, et al.. (2024). Evaluation of the interactions between human stratum corneum and liposome formulations using QCM-D. Journal of Drug Delivery Science and Technology. 102. 106363–106363.
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Upadhya, Rahul, et al.. (2022). Examining polymer‐protein biophysical interactions with small‐angle x‐ray scattering and quartz crystal microbalance with dissipation. Journal of Biomedical Materials Research Part A. 111(4). 440–450. 8 indexed citations
9.
Tamasi, Matthew, Roshan Patel, Carlos H. Borca, et al.. (2022). Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids. Advanced Materials. 34(30). e2201809–e2201809. 123 indexed citations
10.
Popp, Thomas M. Osborn, et al.. (2021). Control of Drug Release from Microparticles by Tuning Their Crystalline Textures: A Structure–Activity Study. ACS Applied Polymer Materials. 3(12). 6548–6561. 8 indexed citations
11.
Grubb, David T., W. Joshua Kennedy, Hilmar Koerner, & N. Sanjeeva Murthy. (2021). Simulation of SAXS patterns from oriented lamellar structures and their elliptical trajectories. Polymer. 220. 123566–123566. 10 indexed citations
12.
Sommerfeld, Sven D., et al.. (2020). Structural Investigations of Polycarbonates whose Mechanical and Erosion Behavior Can Be Controlled by Their Isomer Sequence. Macromolecules. 53(22). 9878–9889. 3 indexed citations
13.
14.
Upadhya, Rahul, N. Sanjeeva Murthy, Cody L. Hoop, et al.. (2019). PET-RAFT and SAXS: High Throughput Tools To Study Compactness and Flexibility of Single-Chain Polymer Nanoparticles. Macromolecules. 52(21). 8295–8304. 53 indexed citations
15.
Valenzuela, Loreto M., Guojin Zhang, Carol R. Flach, et al.. (2011). Multiscale analysis of water uptake and erosion in biodegradable polyarylates. Polymer Degradation and Stability. 97(3). 410–420. 6 indexed citations
16.
Kashyap, Hemant K., Cherry S. Santos, Harsha V. R. Annapureddy, et al.. (2011). Temperature-dependent structure of ionic liquids: X-ray scattering and simulations. Faraday Discussions. 154. 133–143. 170 indexed citations
17.
Tovar, Nick, Sharon Bourke, Michael Jaffé, et al.. (2009). A comparison of degradable synthetic polymer fibers for anterior cruciate ligament reconstruction. Journal of Biomedical Materials Research Part A. 93A(2). 738–747. 32 indexed citations
18.
Bolikal, Durgadas, Imran Khan, Ramiro Rojas, et al.. (2007). Glass transition temperature prediction of polymers through the mass-per-flexible-bond principle. Polymer. 48(20). 6115–6124. 34 indexed citations
19.
Murthy, N. Sanjeeva, K. Zero, & David T. Grubb. (1996). Full-pattern parameterization of two-dimensional diffraction and scattering data from oriented polmers. Acta Crystallographica Section A Foundations of Crystallography. 52(a1). C480–C480. 1 indexed citations
20.
Murthy, N. Sanjeeva & C.R. Worthington. (1991). X-ray diffraction evidence for the presence of discrete water layers on the surface of membranes. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1062(2). 172–176. 11 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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