Maya Kleiman

924 citations
26 papers · 682 · h-index 9

Impact in

Papers in

    • 3D Printing in Biomedical Research 6
    • Innovative Microfluidic and Catalytic Techniques Innovation 3
    • Plant-Microbe Interactions and Immunity 3
    • Plant Molecular Biology Research 3
    • Polysaccharides and Plant Cell Walls 2
    • Plant Surface Properties and Treatments 2

Maya Kleiman

25 papers receiving 653 citations

Peers

Maya Kleiman
Comparison fields: 5 of 100
  • Discrete Mathematics and Combinatorics 111
  • Algebra and Number Theory 61
  • Surfaces, Coatings and Films 54
  • Organic Chemistry 213
  • Biomaterials 62
Replace Jie Cui with:
Jie Cui China
Chenhua Zhang China
Haijing Cao China
Peter J. Holden Australia
Delin Sun United States
Vishal Maingi India
Katarzyna Wegner Poland
Saeed Najafi United States
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Citations per year

Countries citing papers authored by Maya Kleiman

Since Specialization
Citations

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

Fields of papers citing papers by Maya Kleiman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Maya Kleiman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Maya Kleiman Line = papers co-authored together Maya Kleiman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016302
2 1978125
3 202152
4 201138
5 201536
6 201734
7 202113
8 20239
9 20198
10 20238
11 20157
12 20227
13 20247
14 20146
15 20095
16 20185
17 20185
18 20153
19 20143
20 20242

About Maya Kleiman

Maya Kleiman is a scholar working on Biomedical Engineering, Plant Science, Molecular Biology, Cell Biology and Mechanical Engineering, having authored 26 papers that have together received 682 indexed citations. Recurring topics across this work include 3D Printing in Biomedical Research (6 papers), Plant-Microbe Interactions and Immunity (3 papers), Plant Molecular Biology Research (3 papers), Innovative Microfluidic and Catalytic Techniques Innovation (3 papers), Polysaccharides and Plant Cell Walls (2 papers), Mathematical and Theoretical Epidemiology and Ecology Models (2 papers), Plant Surface Properties and Treatments (2 papers) and Advanced Polymer Synthesis and Characterization (2 papers). The work is most often cited by research in Discrete Mathematics and Combinatorics (111 citations), Algebra and Number Theory (61 citations), Surfaces, Coatings and Films (54 citations), Organic Chemistry (213 citations) and Biomaterials (62 citations). Maya Kleiman has collaborated with scholars based in Israel, United States and Canada. Frequent co-authors include Aaron P. Esser‐Kahn, Hemakesh Mohapatra, Ronald Graham, Fan Chung, V. E. Hoggatt, Keun Ah Ryu, Arunima Bhattacharjee, Mughees Khan, Allon I. Hochbaum and Rupali Gupta. Their work appears in journals such as ACS Applied Materials & Interfaces, Chemistry of Materials, Frontiers in Plant Science, Theory in Biosciences and Molecular Biology of the Cell.

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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