Jun Dai

1.4k citations
39 papers · 713 · h-index 13

Impact in

    • Analytical Chemistry and Chromatography
    • Mass Spectrometry Techniques and Applications
    • Chromatography in Natural Products

Papers in

    • Protein purification and stability 8
    • Microfluidic and Capillary Electrophoresis Applications 12
    • Innovative Microfluidic and Catalytic Techniques Innovation 2

Jun Dai

37 papers receiving 675 citations

Peers

Jun Dai
Comparison fields: 5 of 94
  • Spectroscopy 423
  • Analytical Chemistry 171
  • Biomedical Engineering 335
  • Filtration and Separation 13
  • Molecular Biology 287
Replace Mary Ann Rounds with:
Mary Ann Rounds United States
Jack Cazes United States
Fu‐Yung Lin Taiwan
Alberto Méndez Spain
Nik P. Chetwyn United States
G.J. de Jong Netherlands
Meire Ribeiro da Silva Brazil
Wojciech Piątkowski Poland
Cécile Danel France
Nicole Thuaud France
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Citations per field
00.5×9.7×
Mary Ann Rounds · 1×
Citations per year

Countries citing papers authored by Jun Dai

Since Specialization
Citations

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

Fields of papers citing papers by Jun Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Dai, 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 Jun Dai Line = papers co-authored together Jun Dai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200397
2 201795
3 200586
4 200554
5 200349
6 201044
7 201840
8 200928
9 201324
10 200323
11 200921
12 200620
13 201715
14 200212
15 200712
16 201512
17 200712
18 201611
19 20098
20 20176

About Jun Dai

Jun Dai is a scholar working on Molecular Biology, Biomedical Engineering, Spectroscopy, Analytical Chemistry and Radiology, Nuclear Medicine and Imaging, having authored 39 papers that have together received 713 indexed citations. Recurring topics across this work include Microfluidic and Capillary Electrophoresis Applications (12 papers), Analytical Chemistry and Chromatography (11 papers), Protein purification and stability (8 papers), Chromatography in Natural Products (6 papers), Monoclonal and Polyclonal Antibodies Research (4 papers), Polysaccharides Composition and Applications (2 papers), Innovative Microfluidic and Catalytic Techniques Innovation (2 papers) and Proteins in Food Systems (2 papers). The work is most often cited by research in Spectroscopy (423 citations), Analytical Chemistry (171 citations), Biomedical Engineering (335 citations), Filtration and Separation (13 citations) and Molecular Biology (287 citations). Jun Dai has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Peter W. Carr, Yingru Zhang, Xiqin Yang, Qiangwei Xia, Jared Lamp, Xiqin Yang, Charles A. Lucy, Shaun D. Mendonsa, Michael T. Bowser and David V. McCalley. Their work appears in journals such as Journal of Chromatography A, Analytical Chemistry, Artificial Organs, International Journal of Biological Macromolecules and Analytica Chimica Acta.

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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