Sheng Song

1.7k citations
63 papers · 1.3k indexed · h-index 21

Impact in

Papers in

Sheng Song

59 papers receiving 1.3k citations

Peers

Sheng Song
Comparison fields: 5 of 136
  • Neurology 218
  • Biological Psychiatry 53
  • Biomaterials 129
  • Surfaces, Coatings and Films 60
  • Biomedical Engineering 371
Replace Jin Su Kim with:
Jin Su Kim South Korea
Hojjat‐Allah Abbaszadeh Iran
Bing Xue China
Xin Qi China
Richard P. Tan Australia
Lanfen Chen China
Qichuan Zhuge China
Alexander Schwarz Germany
Joseph Park United States
Shaohua Xu United States
Sheng Song relative to Jin Su Kim South Korea Jin Su Kim's profile →
Citations per field
00.5×3.5×
Jin Su Kim · 1×
Citations per year

Countries citing papers authored by Sheng Song

Since Specialization
Citations

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

Fields of papers citing papers by Sheng Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 20256
2 20249
3 20240
4 202325
5 20233
6 202249
7 20211
8 202120
9 20216
10 202027
11 202016
12 202016
13 202043
14 201828
15 201717
16 20168
17 201412
18 20125
19
On Approximation by Non-Periodic Neural and Translation Networks in L~p-w, Spaces
20031
20
A new analytical expression of current waveform in standard IEC61000-4-2
20034

About Sheng Song

Sheng Song is a scholar working on Neurology, Endocrine and Autonomic Systems, Physiology, Biomedical Engineering and Surfaces, Coatings and Films, having authored 63 papers that have together received 1.3k indexed citations. Recurring topics across this work include Neuroinflammation and Neurodegeneration Mechanisms (14 papers), Nanoplatforms for cancer theranostics (7 papers), Parkinson's Disease Mechanisms and Treatments (5 papers), Photoacoustic and Ultrasonic Imaging (5 papers), Nanoparticle-Based Drug Delivery (4 papers), Advanced MRI Techniques and Applications (4 papers), Circadian rhythm and melatonin (4 papers) and Photodynamic Therapy Research Studies (4 papers). The work is most often cited by research in Neurology (218 citations), Biological Psychiatry (53 citations), Biomaterials (129 citations), Surfaces, Coatings and Films (60 citations) and Biomedical Engineering (371 citations). Sheng Song has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Feifan Zhou, Wei R. Chen, Jau‐Shyong Hong, Da Xing, Shengnan Wu, Daniel E. Resasco, Belinda Wilson, Jong‐in Hahm, Qingshan Wang and Esteban A. Oyarzabal. Their work appears in journals such as Langmuir, Journal of Neuroinflammation, Acta Biomaterialia, Nanoscale and Molecular Neurobiology.

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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