S. Maya

1.6k total citations
26 papers, 1.2k citations indexed

About

S. Maya is a scholar working on Biomaterials, Molecular Biology and Biomedical Engineering. According to data from OpenAlex, S. Maya has authored 26 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Biomaterials, 8 papers in Molecular Biology and 8 papers in Biomedical Engineering. Recurrent topics in S. Maya's work include Nanoparticle-Based Drug Delivery (12 papers), Nanoplatforms for cancer theranostics (8 papers) and Advanced Drug Delivery Systems (6 papers). S. Maya is often cited by papers focused on Nanoparticle-Based Drug Delivery (12 papers), Nanoplatforms for cancer theranostics (8 papers) and Advanced Drug Delivery Systems (6 papers). S. Maya collaborates with scholars based in India, Portugal and Switzerland. S. Maya's co-authors include R. Jayakumar, Shantikumar V. Nair, N. Sanoj Rejinold, Bruno Sarmento, Amal J. Sivaram, Deepthy Menon, A. Anitha, M. Sabitha, Rajitha Panonnummal and S. M. Sunoj and has published in prestigious journals such as Carbohydrate Polymers, RSC Advances and International Journal of Biological Macromolecules.

In The Last Decade

S. Maya

25 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Maya India 19 642 335 298 285 206 26 1.2k
Swati Gupta India 22 318 0.5× 178 0.5× 280 0.9× 231 0.8× 48 0.2× 64 1.4k
Annabelle Gèze France 18 377 0.6× 145 0.4× 342 1.1× 324 1.1× 58 0.3× 38 1.1k
İmran Vural Türkiye 28 619 1.0× 263 0.8× 831 2.8× 543 1.9× 83 0.4× 77 1.9k
Raj Kumar Narang India 17 287 0.4× 227 0.7× 253 0.8× 268 0.9× 56 0.3× 66 1.4k
Laura Thoma United States 12 418 0.7× 225 0.7× 331 1.1× 528 1.9× 38 0.2× 27 1.2k
Vamshi Krishna Rapalli India 28 244 0.4× 157 0.5× 1.0k 3.5× 429 1.5× 151 0.7× 34 2.0k
Himanshu Bhatt India 21 263 0.4× 243 0.7× 290 1.0× 348 1.2× 67 0.3× 58 1.2k
Ashwni Verma India 27 456 0.7× 393 1.2× 411 1.4× 774 2.7× 88 0.4× 65 2.0k
Pedram Ebrahimnejad Iran 22 517 0.8× 318 0.9× 364 1.2× 364 1.3× 167 0.8× 82 1.3k
Rohidas Arote South Korea 27 420 0.7× 291 0.9× 185 0.6× 1.5k 5.3× 67 0.3× 61 2.3k

Countries citing papers authored by S. Maya

Since Specialization
Citations

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

Fields of papers citing papers by S. Maya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Maya

This figure shows the co-authorship network connecting the top 25 collaborators of S. Maya. A scholar is included among the top collaborators of S. Maya 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 S. Maya. S. Maya 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.
2.
Maya, S., et al.. (2021). A comparison of in vitro cytotoxicity of undoped and doped surface modified CaS nanoparticles. Materials Letters. 311. 131578–131578. 1 indexed citations
3.
Unnikrishnan, B. S., et al.. (2020). Folic acid-appended galactoxyloglucan-capped iron oxide nanoparticles as a biocompatible nanotheranostic agent for tumor-targeted delivery of doxorubicin. International Journal of Biological Macromolecules. 168. 130–142. 31 indexed citations
4.
Unnikrishnan, B. S., et al.. (2020). Self-assembled drug loaded glycosyl-protein metal nanoconstruct: Detailed synthetic procedure and therapeutic effect in solid tumor treatment. Colloids and Surfaces B Biointerfaces. 193. 111082–111082. 15 indexed citations
5.
Maya, S., et al.. (2019). Galactoxyloglucan-Modified Gold Nanocarrier of Doxorubicin for Treating Drug-Resistant Brain Tumors. ACS Applied Nano Materials. 2(10). 6287–6299. 12 indexed citations
6.
Joseph, Manu M., et al.. (2018). Galactomannan endowed biogenic silver nanoparticles exposed enhanced cancer cytotoxicity with excellent biocompatibility. International Journal of Biological Macromolecules. 118(Pt A). 1174–1182. 46 indexed citations
7.
8.
Pillarisetti, Shameer, S. Maya, S. Sathianarayanan, & R. Jayakumar. (2017). Tunable pH and redox-responsive drug release from curcumin conjugated γ-polyglutamic acid nanoparticles in cancer microenvironment. Colloids and Surfaces B Biointerfaces. 159. 809–819. 27 indexed citations
9.
Maya, S., et al.. (2015). Comparative efficacy of chloramphenicol loaded chondroitin sulfate and dextran sulfate nanoparticles to treat intracellular Salmonella infections. Colloids and Surfaces B Biointerfaces. 127. 33–40. 39 indexed citations
10.
Nair, Nisha, et al.. (2014). Delivery of rifampicin-chitin nanoparticles into the intracellular compartment of polymorphonuclear leukocytes. International Journal of Biological Macromolecules. 74. 36–43. 34 indexed citations
11.
Maya, S., Bruno Sarmento, Vinoth‐Kumar Lakshmanan, et al.. (2014). Chitosan cross-linked docetaxel loaded EGF receptor targeted nanoparticles for lung cancer cells. International Journal of Biological Macromolecules. 69. 532–541. 39 indexed citations
12.
Maya, S., et al.. (2013). Smart Stimuli Sensitive Nanogels in Cancer Drug Delivery and Imaging: A Review. Current Pharmaceutical Design. 19(41). 7203–7218. 131 indexed citations
13.
Maya, S., Bruno Sarmento, N. Sanoj Rejinold, et al.. (2013). Cetuximab conjugated O-carboxymethyl chitosan nanoparticles for targeting EGFR overexpressing cancer cells. Carbohydrate Polymers. 93(2). 661–669. 82 indexed citations
14.
Maya, S., et al.. (2012). Efficacy of tetracycline encapsulated O-carboxymethyl chitosan nanoparticles against intracellular infections of Staphylococcus aureus. International Journal of Biological Macromolecules. 51(4). 392–399. 129 indexed citations
15.
Anitha, A., S. Maya, N. Deepa, et al.. (2012). Curcumin-LoadedN,O-Carboxymethyl Chitosan Nanoparticles for Cancer Drug Delivery. Journal of Biomaterials Science Polymer Edition. 23(11). 1381–1400. 119 indexed citations
16.
Jayakumar, R., et al.. (2011). Doxorubicin-loaded pH-responsive chitin nanogels for drug delivery to cancer cells. Carbohydrate Polymers. 87(3). 2352–2356. 83 indexed citations
17.
Menon, Deepthy, Reny Thankam Thomas, Sreeja Narayanan, et al.. (2010). A novel chitosan/polyoxometalate nano-complex for anti-cancer applications. Carbohydrate Polymers. 84(3). 887–893. 65 indexed citations
18.
Sunoj, S. M., P. G. Sankaran, & S. Maya. (2009). Characterizations of Life Distributions Using Conditional Expectations of Doubly (Interval) Truncated Random Variables. Communication in Statistics- Theory and Methods. 38(9). 1441–1452. 35 indexed citations
19.
Sunoj, S. M. & S. Maya. (2008). The role of lower partial moments in stochastic modeling. METRON. 223–242. 6 indexed citations
20.
Sunoj, S. M., P. G. Sankaran, & S. Maya. (2007). Characterizations of distributions using log odds rate. Statistics. 41(5). 443–451. 6 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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