Yi Dai

2.0k citations
72 papers · 1.6k indexed · 1 hit paper · h-index 18

Impact in

Papers in

Yi Dai

68 papers receiving 1.6k citations

Hit Papers

Stimuli‐Responsive Manganese Single‐Atom Nanozyme for Tumor Therapy via Integrated Cascade Reactions 2021 · 406 citations
4062021202620222024100200300400

Peers

Yi Dai
Comparison fields: 5 of 114
  • Materials Chemistry 573
  • Biomedical Engineering 505
  • Cancer Research 162
  • Radiology, Nuclear Medicine and Imaging 193
  • Molecular Medicine 37
Replace Haitao Sun with:
Haitao Sun China
Saskia von Stillfried Germany
Reza Akhavan‐Sigari Germany
Ruimeng Yang China
Wenjuan Ma China
Yuanbo Feng Belgium
Zhongquan Sun China
Guan Jiang China
Iwona T. Dobrucki United States
Peipei Xu China
Yi Dai relative to Haitao Sun China Haitao Sun's profile →
Citations per field
00.5×
Haitao Sun · 1×
Citations per year

Countries citing papers authored by Yi Dai

Since Specialization
Citations

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

Fields of papers citing papers by Yi Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 20260
2 20250
3 20254
4 20250
5 20242
6 20241
7 20232
8 202377
9 202317
10 20228
11 202117
12 20219
13 20214
14 202117
15 202027
16 201813
17 201723
18 20168
19 201513
20 201323

About Yi Dai

Yi Dai is a scholar working on Obstetrics and Gynecology, Reproductive Medicine, Radiology, Nuclear Medicine and Imaging, Oncology and Cancer Research, having authored 72 papers that have together received 1.6k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), Nanoplatforms for cancer theranostics (6 papers), Advanced Nanomaterials in Catalysis (5 papers), Endometriosis Research and Treatment (5 papers), Nanocluster Synthesis and Applications (4 papers), Ferroptosis and cancer prognosis (4 papers), AI in cancer detection (4 papers) and Parasitic infections in humans and animals (4 papers). The work is most often cited by research in Materials Chemistry (573 citations), Biomedical Engineering (505 citations), Cancer Research (162 citations), Radiology, Nuclear Medicine and Imaging (193 citations) and Molecular Medicine (37 citations). Yi Dai has collaborated with scholars based in China, Singapore and United Kingdom. Frequent co-authors include Yangzhong Liu, Yang Zhu, Manman Liu, Junjie Cheng, Jianing Yu, Chengming Wang, Sicong Wang, Yunteng Qu, Chao Zhao and Huijuan Wang. Their work appears in journals such as Angewandte Chemie International Edition, Antimicrobial Agents and Chemotherapy, Frontiers in Neurology, Journal of Magnetic Resonance Imaging and Frontiers in Oncology.

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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