Shangda Qu

402 citations
19 papers · 321 · 1 hit paper · h-index 8

Impact in

Papers in

Shangda Qu

19 papers receiving 317 citations

Shangda Qu's Hit Papers

Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement 2023 · 118 citations
1180+1+2Years since publication255075100

Peers

Shangda Qu
Comparison fields: 5 of 39
  • Cellular and Molecular Neuroscience 137
  • Electrical and Electronic Engineering 265
  • Polymers and Plastics 48
  • Cognitive Neuroscience 52
  • Acoustics and Ultrasonics 2
Replace Shengliang Cheng with:
Shengliang Cheng China
Amoolya Nirmal Singapore
Sebastián Pazos Argentina
Jee Young Kwak United States
Jingting Yang China
Shenming Huang China
Xichen Chuai China
Yifan Zhou Finland
Chuanyu Fu China
Gang Shang China
Shangda Qu relative to Shengliang Cheng China Shengliang Cheng's profile →
Citations per field
00.5×1.5×1.9×
Shengliang Cheng · 1×
Citations per year

Countries citing papers authored by Shangda Qu

Since Specialization
Citations

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

Fields of papers citing papers by Shangda Qu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

19 of 19 papers shown
#Work
1
Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement
Hit paper breakdown →
2023118
2 202369
3 202234
4 202325
5 202417
6 202310
7 20249
8 20257
9 20216
10 20245
11 20214
12 20244
13 20203
14 20223
15 20242
16 20242
17 20251
18 20131
19 20251

About Shangda Qu

Shangda Qu is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience, Condensed Matter Physics and Electronic, Optical and Magnetic Materials, having authored 19 papers that have together received 321 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (14 papers), Neuroscience and Neural Engineering (10 papers), Photoreceptor and optogenetics research (6 papers), GaN-based semiconductor devices and materials (4 papers), Neural dynamics and brain function (4 papers), Ga2O3 and related materials (4 papers), ZnO doping and properties (2 papers) and Analytical Chemistry and Sensors (2 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (137 citations), Electrical and Electronic Engineering (265 citations), Polymers and Plastics (48 citations), Cognitive Neuroscience (52 citations) and Acoustics and Ultrasonics (2 citations). Shangda Qu has collaborated with scholars based in China, South Korea and Singapore. Frequent co-authors include Wentao Xu, Jiaqi Liu, Lin Sun, Yao Ni, Chengpeng Jiang, Lu Liu, Yue Li, Song Zhang, Junchi Liu and Yue Li. Their work appears in journals such as Nature Communications, Nano Letters, The Journal of Physical Chemistry Letters, Materials Horizons and Advanced Functional Materials.

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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