World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
51
Citations
10371
World Ranking
13861
National Ranking
2149

Zheng-Jiang Zhu publication distribution in Chemistry in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Chemistry in 2026. The highlighted bar marks where Zheng-Jiang Zhu sits on this spectrum.

61–80 publications: 66 scientists 81–100 publications: 302 scientists 101–120 publications: 623 scientists 121–140 publications: 918 scientists 141–160 publications: 1,218 scientists 161–180 publications: 1,350 scientists 181–200 publications: 1,344 scientists 201–220 publications: 1,281 scientists 221–240 publications: 1,216 scientists 241–260 publications: 1,100 scientists 261–280 publications: 979 scientists 281–300 publications: 939 scientists 301–320 publications: 764 scientists 321–340 publications: 643 scientists 341–360 publications: 628 scientists 361–380 publications: 522 scientists 381–400 publications: 459 scientists 401–420 publications: 397 scientists 421–440 publications: 327 scientists 441–460 publications: 270 scientists 461–480 publications: 265 scientists 481–500 publications: 252 scientists 501–520 publications: 201 scientists 521–540 publications: 185 scientists 541–560 publications: 148 scientists 561–580 publications: 148 scientists 581–600 publications: 132 scientists 601–620 publications: 114 scientists 621–640 publications: 104 scientists 641–660 publications: 91 scientists 661–680 publications: 92 scientists 681–700 publications: 73 scientists 701–720 publications: 57 scientists 721–740 publications: 54 scientists 741–760 publications: 67 scientists 761–780 publications: 45 scientists 781–800 publications: 46 scientists 801–820 publications: 39 scientists 821–840 publications: 32 scientists 841–860 publications: 36 scientists 861–880 publications: 29 scientists 881–900 publications: 26 scientists 901–920 publications: 24 scientists 921–940 publications: 14 scientists 941–960 publications: 23 scientists 961–980 publications: 28 scientists 981–1,000 publications: 15 scientists 1,001–1,020 publications: 29 scientists 1,021–1,040 publications: 12 scientists 1,041–1,060 publications: 19 scientists 1,061–1,080 publications: 12 scientists 1,081–1,100 publications: 6 scientists 1,101–1,120 publications: 8 scientists 1,121–1,140 publications: 12 scientists 1,141–1,160 publications: 5 scientists 1,161–1,180 publications: 6 scientists 1,181–1,200 publications: 14 scientists 1,201–1,220 publications: 7 scientists 1,221–1,240 publications: 2 scientists 1,241–1,260 publications: 6 scientists 1,261–1,280 publications: 4 scientists 1,281–1,294 publications: 6 scientists 1,295+ publications: 100 scientists
61 publications 1,295+

This scientist: 97 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,295 publications or more.

Zheng-Jiang Zhu D-index placement in Chemistry in 2026

The chart shows the D-index (discipline H-index) distribution of Chemistry scientists ranked by Research.com in 2026. The highlighted bar marks where Zheng-Jiang Zhu sits on this spectrum.

40–41 D-Index: 289 scientists 42–43 D-Index: 612 scientists 44–45 D-Index: 808 scientists 46–47 D-Index: 776 scientists 48–49 D-Index: 835 scientists 50–51 D-Index: 861 scientists 52–53 D-Index: 872 scientists 54–55 D-Index: 933 scientists 56–57 D-Index: 1,051 scientists 58–59 D-Index: 930 scientists 60–61 D-Index: 882 scientists 62–63 D-Index: 834 scientists 64–65 D-Index: 731 scientists 66–67 D-Index: 775 scientists 68–69 D-Index: 683 scientists 70–71 D-Index: 646 scientists 72–73 D-Index: 561 scientists 74–75 D-Index: 501 scientists 76–77 D-Index: 437 scientists 78–79 D-Index: 388 scientists 80–81 D-Index: 354 scientists 82–83 D-Index: 292 scientists 84–85 D-Index: 275 scientists 86–87 D-Index: 254 scientists 88–89 D-Index: 235 scientists 90–91 D-Index: 185 scientists 92–93 D-Index: 192 scientists 94–95 D-Index: 155 scientists 96–97 D-Index: 163 scientists 98–99 D-Index: 125 scientists 100–101 D-Index: 105 scientists 102–103 D-Index: 105 scientists 104–105 D-Index: 112 scientists 106–107 D-Index: 88 scientists 108–109 D-Index: 68 scientists 110–111 D-Index: 69 scientists 112–113 D-Index: 65 scientists 114–115 D-Index: 79 scientists 116–117 D-Index: 61 scientists 118–119 D-Index: 44 scientists 120–121 D-Index: 37 scientists 122–123 D-Index: 40 scientists 124–125 D-Index: 33 scientists 126–127 D-Index: 26 scientists 128–129 D-Index: 34 scientists 130–131 D-Index: 35 scientists 132–133 D-Index: 25 scientists 134–135 D-Index: 27 scientists 136–137 D-Index: 17 scientists 138–139 D-Index: 16 scientists 140–141 D-Index: 20 scientists 142–143 D-Index: 20 scientists 144–145 D-Index: 15 scientists 146–147 D-Index: 9 scientists 148–149 D-Index: 9 scientists 150–151 D-Index: 16 scientists 152–153 D-Index: 11 scientists 154–155 D-Index: 9 scientists 156–157 D-Index: 3 scientists 158 D-Index: 3 scientists 159+ D-Index: 98 scientists
40 D-Index 159+

This scientist: 51 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 159 D-Index or more.

Best Publications

  • A lipidome atlas in MS-DIAL 4.

    Hiroshi Tsugawa;Kazutaka Ikeda;Mikiko Takahashi;Aya Satoh

  • An accelerated workflow for untargeted metabolomics using the METLIN database.

    Ralf Tautenhahn;Kevin Cho;Winnie Uritboonthai;Zhengjiang Zhu

  • Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

    Zheng-Jiang Zhu;Andrew W Schultz;Junhua Wang;Caroline H Johnson

  • Effect of Surface Charge on the Uptake and Distribution of Gold Nanoparticles in Four Plant Species

    Zheng-Jiang Zhu;Huanhua Wang;Bo Yan;Hao Zheng

  • Entrapment of Hydrophobic Drugs in Nanoparticle Monolayers with Efficient Release into Cancer Cells

    Chae Kyu Kim;Partha Ghosh;Chiara Pagliuca;Zheng-Jiang Zhu

  • Colorimetric bacteria sensing using a supramolecular enzyme-nanoparticle biosensor.

    Oscar R. Miranda;Xiaoning Li;Limary Garcia-Gonzalez;Zheng-Jiang Zhu

  • Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics.

    Unknown

  • Serum metabolomics for early diagnosis of esophageal squamous cell carcinoma by UHPLC-QTOF/MS

    Unknown

  • Recognition-mediated activation of therapeutic gold nanoparticles inside living cells

    Chaekyu Kim;Sarit S. Agasti;Zhengjiang Zhu;Lyle Isaacs

  • Toward ‘Omic Scale Metabolite Profiling: A Dual Separation–Mass Spectrometry Approach for Coverage of Lipid and Central Carbon Metabolism

    Julijana Ivanisevic;Zheng-Jiang Zhu;Lars Plate;Ralf Tautenhahn

  • The Role of Surface Functionality on Acute Cytotoxicity, ROS Generation and DNA Damage by Cationic Gold Nanoparticles

    Apiwat Chompoosor;Krishnendu Saha;Partha S. Ghosh;Dylan J. Macarthy

  • Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics

    Unknown

  • Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

    Unknown

  • Intracellular delivery of a membrane-impermeable enzyme in active form using functionalized gold nanoparticles.

    Partha Ghosh;Xiaochao Yang;Rochelle Arvizo;Zheng-Jiang Zhu

  • Direct Delivery of Functional Proteins and Enzymes to the Cytosol Using Nanoparticle-Stabilized Nanocapsules

    Rui Tang;Chang Soo Kim;David J. Solfiell;Subinoy Rana

  • LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision to Support Ion Mobility-Mass Spectrometry based Lipidomics

    Unknown

  • Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry

    Unknown

  • Multiplexed Screening of Cellular Uptake of Gold Nanoparticles Using Laser Desorption/Ionization Mass Spectrometry

    Zheng-Jiang Zhu;Partha S. Ghosh;Oscar R. Miranda;Richard W. Vachet

  • An integrated targeted metabolomic platform for high-throughput metabolite profiling and automated data processing

    Unknown

  • Normalization and integration of large-scale metabolomics data using support vector regression

    Xiaotao Shen;Xiaoyun Gong;Yuping Cai;Yuan Guo

Frequent Co-Authors

Vincent M. Rotello
Vincent M. Rotello University of Massachusetts Amherst
Richard W. Vachet
Richard W. Vachet University of Massachusetts Amherst
Junying Yuan
Junying Yuan Harvard University
Gary Siuzdak
Gary Siuzdak Scripps Research Institute
Huiyong Yin
Huiyong Yin Chinese Academy of Sciences
Fan Zhang
Fan Zhang Chinese Academy of Sciences
Huiru Tang
Huiru Tang Fudan University
Michael Snyder
Michael Snyder Stanford University
Hong Zhu
Hong Zhu Zhejiang University

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