N.K. Vail

883 citations
13 papers · 653 · h-index 8

Impact in

Papers in

N.K. Vail

12 papers receiving 614 citations

Peers

N.K. Vail
Comparison fields: 5 of 99
  • Developmental Neuroscience 247
  • Automotive Engineering 228
  • Cellular and Molecular Neuroscience 158
  • Orthodontics 35
  • Ceramics and Composites 40
Replace Xiaoxuan Yang with:
Xiaoxuan Yang China
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Esra Karaca Türkiye
David J. Schreyer Canada
Huan Zhao China
Xiaosong Gu China
Aixue Liu China
Fanny Joubert France
Jason Walker United States
N.K. Vail relative to Xiaoxuan Yang China Xiaoxuan Yang's profile →
Citations per field
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Citations per year

Countries citing papers authored by N.K. Vail

Since Specialization
Citations

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

Fields of papers citing papers by N.K. Vail

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside N.K. Vail, 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 N.K. Vail Line = papers co-authored together N.K. Vail links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 2001293
2 1995124
3 199970
4 199559
5 199428
6 199626
7 200825
8
Experimental induction of reduced ovarian reserve in a nonhuman primate model (Macaca fascicularis).
201015
9 20185
10 20184
11 20182
12 20182
13 20180

About N.K. Vail

N.K. Vail is a scholar working on Automotive Engineering, Mechanical Engineering, Computational Mechanics, General Materials Science and Polymers and Plastics, having authored 13 papers that have together received 653 indexed citations. Recurring topics across this work include Additive Manufacturing and 3D Printing Technologies (6 papers), Injection Molding Process and Properties (4 papers), Material Properties and Applications (2 papers), Laser Material Processing Techniques (2 papers), Additive Manufacturing Materials and Processes (2 papers), Advanced biosensing and bioanalysis techniques (1 paper), Advanced Polymer Synthesis and Characterization (1 paper) and Photopolymerization techniques and applications (1 paper). The work is most often cited by research in Developmental Neuroscience (247 citations), Automotive Engineering (228 citations), Cellular and Molecular Neuroscience (158 citations), Orthodontics (35 citations) and Ceramics and Composites (40 citations). N.K. Vail has collaborated with scholars based in United States and Germany. Frequent co-authors include Elizabeth Gould, Charles G. Gross, Matthew Wagers, Joel W. Barlow, Harris L. Marcus, J. W. Barlow, Wendy Fox, L.D. Swain, Joseph J. Beaman and David L. Bourell. Their work appears in journals such as Rapid Prototyping Journal, Journal of Applied Polymer Science, Journal of Fluorescence, Industrial & Engineering Chemistry Research and Proceedings of the National Academy of Sciences.

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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