Changjin Wan

9.3k total citations · 8 hit papers
96 papers, 7.0k citations indexed

About

Changjin Wan is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Biomedical Engineering. According to data from OpenAlex, Changjin Wan has authored 96 papers receiving a total of 7.0k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Electrical and Electronic Engineering, 35 papers in Cellular and Molecular Neuroscience and 26 papers in Biomedical Engineering. Recurrent topics in Changjin Wan's work include Advanced Memory and Neural Computing (68 papers), Advanced Sensor and Energy Harvesting Materials (25 papers) and Neuroscience and Neural Engineering (23 papers). Changjin Wan is often cited by papers focused on Advanced Memory and Neural Computing (68 papers), Advanced Sensor and Energy Harvesting Materials (25 papers) and Neuroscience and Neural Engineering (23 papers). Changjin Wan collaborates with scholars based in China, Singapore and Germany. Changjin Wan's co-authors include Xiaodong Chen, Ming Wang, Hui Yang, Pingqiang Cai, Geng Chen, Ke He, Zhiyuan Liu, Jiancan Yu, Dianpeng Qi and Liang Pan and has published in prestigious journals such as Advanced Materials, Nature Communications and Nano Letters.

In The Last Decade

Changjin Wan

91 papers receiving 6.9k citations

Hit Papers

Highly Stretchable, Elast... 2017 2026 2020 2023 2018 2020 2018 2017 2020 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Changjin Wan 3.9k 3.3k 2.2k 1.5k 1.4k 96 7.0k
Yeongjun Lee 3.6k 0.9× 4.2k 1.3× 3.1k 1.4× 964 0.6× 1.8k 1.2× 62 6.9k
Jiheong Kang 5.4k 1.4× 3.2k 1.0× 4.1k 1.9× 1.2k 0.8× 1.2k 0.8× 49 8.3k
Yeongin Kim 5.8k 1.5× 4.7k 1.4× 4.5k 2.0× 1.5k 1.0× 1.6k 1.1× 30 9.2k
Yuxin Liu 4.7k 1.2× 2.9k 0.9× 3.0k 1.4× 962 0.6× 1.4k 1.0× 67 8.2k
Raphael Pfattner 4.1k 1.1× 3.5k 1.1× 3.0k 1.4× 898 0.6× 1.0k 0.7× 68 6.9k
Donghee Son 7.9k 2.0× 4.4k 1.3× 4.5k 2.1× 2.5k 1.7× 1.9k 1.3× 151 11.1k
Qijun Sun 6.3k 1.6× 4.1k 1.2× 3.6k 1.7× 1.8k 1.2× 664 0.5× 184 9.3k
Jin Young Oh 5.6k 1.4× 5.1k 1.6× 4.8k 2.2× 1.1k 0.8× 1.1k 0.8× 145 9.5k
Yang‐Kyu Choi 5.4k 1.4× 7.2k 2.2× 2.7k 1.2× 973 0.7× 725 0.5× 359 11.3k
Amir M. Foudeh 3.7k 0.9× 2.4k 0.7× 1.9k 0.9× 1.0k 0.7× 1.0k 0.7× 21 5.1k

Countries citing papers authored by Changjin Wan

Since Specialization
Citations

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

Fields of papers citing papers by Changjin Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Changjin Wan

This figure shows the co-authorship network connecting the top 25 collaborators of Changjin Wan. A scholar is included among the top collaborators of Changjin Wan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Changjin Wan. Changjin Wan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pei, Mengjiao, et al.. (2025). A flexible spiking hair sensillum for ultralow power density noncontact perception. Science Advances. 11(38). eady0336–eady0336.
2.
Li, Taotao, Li M. Li, Changjin Wan, et al.. (2025). Designable excitonic effects in van der Waals artificial crystals with exponentially growing thickness. Nature Communications. 16(1). 2712–2712. 1 indexed citations
3.
Pei, Mengjiao, Chao Ai, Yating Li, et al.. (2025). A Cascaded Duplex Organic Vertical Memory with Learning Rate Scheduling for Efficient Artificial Neural Network Training. Advanced Functional Materials. 35(20). 1 indexed citations
4.
Yu, Xiao, Mengjiao Pei, Shuo Ke, et al.. (2025). A bioinspired in-materia analog photoelectronic reservoir computing for human action processing. Nature Communications. 16(1). 2263–2263. 16 indexed citations
5.
Zhu, Yixin, Xiangjing Wang, Yuqing Hu, et al.. (2025). Neuromorphic devices for intelligent visual perception. International Journal of Extreme Manufacturing. 8(1). 12006–12006.
6.
Hu, Zehua, et al.. (2024). A Fourier neuromorphic visual system based on InGaZnO synaptic transistor. Applied Physics Letters. 124(3). 3 indexed citations
7.
Fu, Chuanyu, et al.. (2024). A flexible thermal-coupled InGaZnO adaptive synapse. Applied Physics Letters. 124(16). 2 indexed citations
8.
Pei, Mengjiao, et al.. (2024). Noise Robust Reservoir Computing Based on Flexible Doped Hafnium Oxide Memcapacitors. IEEE Transactions on Electron Devices. 71(5). 2970–2975. 3 indexed citations
9.
Zhu, Li, Shuo Ke, Xiang Wan, et al.. (2024). Visible-light responsive CdS-QDs modified InGaZnO synapse for biologically plausible color-to-gray conversion. Applied Physics Letters. 125(3). 9 indexed citations
10.
Li, Zhan, Xudong Pei, Yuan Li, et al.. (2024). Highly Oriented WS2 Monolayers for High‐Performance Electronics. Advanced Materials. 37(6). e2414100–e2414100. 5 indexed citations
11.
Meng, Jialin, Tianyu Wang, Changjin Wan, et al.. (2024). InGaZnO-based photoelectric synaptic devices for neuromorphic computing. Journal of Semiconductors. 45(9). 92402–92402. 8 indexed citations
12.
Fu, Chuanyu, Mengjiao Pei, Shuo Ke, et al.. (2024). IGZO/PVP Composite Nanofiber Neuromorphic Transistors with Optoelectronic Synapse Emulation and Reservoir Computing. The Journal of Physical Chemistry Letters. 15(38). 9585–9592. 2 indexed citations
13.
Ke, Shuo, Yixin Zhu, Huiwu Mao, et al.. (2024). One-Transistor One-Memristor Based Universal Oscillating Units for Spike-Encoding Artificial Sensory Neuron. IEEE Electron Device Letters. 45(9). 1661–1664.
14.
Pei, Mengjiao, Ying Zhu, Siyao Liu, et al.. (2023). Power‐Efficient Multisensory Reservoir Computing Based on Zr‐Doped HfO2 Memcapacitive Synapse Arrays. Advanced Materials. 35(41). e2305609–e2305609. 63 indexed citations
15.
Zhu, Yixin, Huiwu Mao, Ying Zhu, et al.. (2023). CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review. International Journal of Extreme Manufacturing. 5(4). 42010–42010. 70 indexed citations
16.
Li, Hua, Honglei Li, Xingzhi Wang, et al.. (2021). Spontaneous Polarity Flipping in a 2D Heterobilayer Induced by Fluctuating Interfacial Carrier Flows. Nano Letters. 21(16). 6773–6780. 11 indexed citations
17.
Wan, Changjin, Pingqiang Cai, Xintong Guo, et al.. (2020). An artificial sensory neuron with visual-haptic fusion. Nature Communications. 11(1). 4602–4602. 256 indexed citations breakdown →
18.
Cai, Pingqiang, Changjin Wan, Liang Pan, et al.. (2020). Locally coupled electromechanical interfaces based on cytoadhesion-inspired hybrids to identify muscular excitation-contraction signatures. Nature Communications. 11(1). 2183–2183. 65 indexed citations
19.
Jiang, Ying, Zhiyuan Liu, Zhiyuan Liu, et al.. (2018). Auxetic Mechanical Metamaterials to Enhance Sensitivity of Stretchable Strain Sensors. Advanced Materials. 30(12). e1706589–e1706589. 444 indexed citations breakdown →
20.
Wu, Guodong, Changjin Wan, Jumei Zhou, Liqiang Zhu, & Qing Wan. (2014). Low-voltage protonic/electronic hybrid indium zinc oxide synaptic transistors on paper substrates. Nanotechnology. 25(9). 94001–94001. 18 indexed citations

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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