Shao-Ling Wu

3.1k total citations · 2 hit papers
41 papers, 2.7k citations indexed

About

Shao-Ling Wu is a scholar working on Molecular Biology, Materials Chemistry and Electrical and Electronic Engineering. According to data from OpenAlex, Shao-Ling Wu has authored 41 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 10 papers in Materials Chemistry and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Shao-Ling Wu's work include Graphene and Nanomaterials Applications (7 papers), Graphene research and applications (7 papers) and Electrochemical Analysis and Applications (6 papers). Shao-Ling Wu is often cited by papers focused on Graphene and Nanomaterials Applications (7 papers), Graphene research and applications (7 papers) and Electrochemical Analysis and Applications (6 papers). Shao-Ling Wu collaborates with scholars based in China, United States and France. Shao-Ling Wu's co-authors include Yanhui Li, Qiuju Du, Zonghua Wang, Yanzhi Xia, Jiankun Sun, Linhua Xia, Yonghao Wang, Tonghao Liu, Xianjia Peng and Junjie Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and PLoS ONE.

In The Last Decade

Shao-Ling Wu

39 papers receiving 2.6k citations

Hit Papers

Comparative study of methylene blue dye adsorption onto a... 2012 2026 2016 2021 2012 2013 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shao-Ling Wu China 21 1.0k 995 864 588 536 41 2.7k
Mohsen Taghizadeh Iran 32 934 0.9× 920 0.9× 844 1.0× 399 0.7× 387 0.7× 43 3.5k
Jie Shi China 35 1.2k 1.2× 1.3k 1.3× 1.3k 1.5× 413 0.7× 952 1.8× 126 3.7k
Chen Wang China 30 1.4k 1.3× 829 0.8× 545 0.6× 540 0.9× 419 0.8× 101 3.2k
Ali Taghizadeh Iran 23 804 0.8× 781 0.8× 755 0.9× 380 0.6× 229 0.4× 39 2.7k
Guanghui Zhao China 30 356 0.3× 574 0.6× 667 0.8× 313 0.5× 445 0.8× 60 2.3k
Mahadevappa Y. Kariduraganavar India 32 1.1k 1.1× 438 0.4× 1.2k 1.4× 357 0.6× 742 1.4× 128 3.2k
Haibo Zhu China 36 1.1k 1.1× 3.2k 3.3× 1.3k 1.5× 1.6k 2.8× 468 0.9× 110 6.6k
Hong‐Qing Liang China 28 944 0.9× 504 0.5× 876 1.0× 115 0.2× 662 1.2× 57 2.7k
Bradley P. Ladewig Australia 43 1.4k 1.3× 2.0k 2.0× 1.9k 2.2× 292 0.5× 2.0k 3.8× 96 5.2k
Qianqian Zhao China 28 583 0.6× 1.0k 1.0× 532 0.6× 192 0.3× 887 1.7× 107 2.7k

Countries citing papers authored by Shao-Ling Wu

Since Specialization
Citations

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

Fields of papers citing papers by Shao-Ling Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shao-Ling Wu. 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 Shao-Ling Wu. The network helps show where Shao-Ling Wu may publish in the future.

Co-authorship network of co-authors of Shao-Ling Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Shao-Ling Wu. A scholar is included among the top collaborators of Shao-Ling Wu 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 Shao-Ling Wu. Shao-Ling Wu 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.
Qiao, Song, et al.. (2024). Targeting HDACs for diffuse large B-cell lymphoma therapy. Scientific Reports. 14(1). 289–289. 11 indexed citations
3.
Jiao, Xue, et al.. (2024). Chidamide and orelabrutinib synergistically induce cell cycle arrest and apoptosis in diffuse large B-cell lymphoma by regulating the PI3K/AKT/mTOR pathway. Journal of Cancer Research and Clinical Oncology. 150(2). 98–98. 7 indexed citations
4.
Zhou, Chunxiao, et al.. (2024). Role of histone deacetylase inhibitors in non-neoplastic diseases. Heliyon. 10(13). e33997–e33997. 8 indexed citations
5.
Wu, Shao-Ling, Huilan Luo, & Xiaoming Lin. (2024). TNPNet: An approach to Few-shot open-set recognition via contextual transductive learning. Neurocomputing. 621. 129276–129276. 2 indexed citations
6.
Gao, Chenxi, Yue Sun, Jing Li, et al.. (2023). High Light Intensity Triggered Abscisic Acid Biosynthesis Mediates Anthocyanin Accumulation in Young Leaves of Tea Plant (Camellia sinensis). Antioxidants. 12(2). 392–392. 23 indexed citations
8.
Wang, Zhihui, et al.. (2022). Identification of characteristic aroma and bacteria related to aroma evolution during long-term storage of compressed white tea. Frontiers in Nutrition. 9. 1092048–1092048. 30 indexed citations
9.
Cui, Zhongguang, Xiaodan Liu, Shao-Ling Wu, et al.. (2019). LncRNA FIRRE is activated by MYC and promotes the development of diffuse large B-cell lymphoma via Wnt/β-catenin signaling pathway. Biochemical and Biophysical Research Communications. 510(4). 594–600. 68 indexed citations
10.
Su, Zhan, Xiaodan Liu, Shao-Ling Wu, et al.. (2017). Philadelphia chromosome-positive acute myeloid leukemia with masses and osteolytic lesions: finding of 18F-FDG PET/CT. Frontiers of Medicine. 11(3). 440–444. 4 indexed citations
11.
Garrick, Taylor R., Kenneth Higa, Shao-Ling Wu, et al.. (2017). Modeling Battery Performance Due to Intercalation Driven Volume Change in Porous Electrodes. Journal of The Electrochemical Society. 164(11). E3592–E3597. 37 indexed citations
12.
Wu, Shao-Ling, Yanhui Li, Xindong Zhao, et al.. (2015). Biosorption Behavior of Ciprofloxacin ontoEnteromorpha prolifera:Isotherm and Kinetic Studies. International Journal of Phytoremediation. 17(10). 957–961. 23 indexed citations
13.
Wang, Qiuyan, et al.. (2015). Mechanisms of Dihydroartemisinin and Dihydroartemisinin/Holotransferrin Cytotoxicity in T-Cell Lymphoma Cells. PLoS ONE. 10(10). e0137331–e0137331. 19 indexed citations
14.
Wang, Yuzhen, Shao-Ling Wu, Xindong Zhao, et al.. (2014). In vitro toxicity evaluation of graphene oxide on human RPMI 8226 cells. Bio-Medical Materials and Engineering. 24(6). 2007–2013. 24 indexed citations
15.
Zhao, Xindong, Chunting Zhao, Yuzhen Wang, et al.. (2014). Cytotoxicity of graphene oxide and graphene oxide loaded with doxorubicin on human multiple myeloma cells. International Journal of Nanomedicine. 9. 1413–1413. 69 indexed citations
16.
Du, Li, Shao-Ling Wu, Yanhui Li, et al.. (2014). Cytotoxicity of PEGylated graphene oxide on lymphoma cells. Bio-Medical Materials and Engineering. 24(6). 2135–2141. 13 indexed citations
17.
Wu, Shao-Ling, Xindong Zhao, Yanhui Li, et al.. (2013). Adsorption of ciprofloxacin onto biocomposite fibers of graphene oxide/calcium alginate. Chemical Engineering Journal. 230. 389–395. 202 indexed citations
18.
Li, Yanhui, Jiankun Sun, Qiuju Du, et al.. (2013). Mechanical and dye adsorption properties of graphene oxide/chitosan composite fibers prepared by wet spinning. Carbohydrate Polymers. 102. 755–761. 155 indexed citations
19.
Wu, Shao-Ling, Mark E. Orazem, Bernard Tribollet, & Vincent Vivier. (2013). The Influence of Coupled Faradaic and Charging Currents On Impedance Spectroscopy. ECS Meeting Abstracts. MA2013-02(48). 2683–2683.
20.
Ji, Liwen, Huolin L. Xin, Tevye Kuykendall, et al.. (2012). SnS2 nanoparticle loaded graphene nanocomposites for superior energy storage. Physical Chemistry Chemical Physics. 14(19). 6981–6981. 75 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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