Lingyun Mo

1.0k total citations
49 papers, 796 citations indexed

About

Lingyun Mo is a scholar working on Pollution, Health, Toxicology and Mutagenesis and Computational Theory and Mathematics. According to data from OpenAlex, Lingyun Mo has authored 49 papers receiving a total of 796 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Pollution, 24 papers in Health, Toxicology and Mutagenesis and 10 papers in Computational Theory and Mathematics. Recurrent topics in Lingyun Mo's work include Pharmaceutical and Antibiotic Environmental Impacts (19 papers), Environmental Toxicology and Ecotoxicology (15 papers) and Computational Drug Discovery Methods (10 papers). Lingyun Mo is often cited by papers focused on Pharmaceutical and Antibiotic Environmental Impacts (19 papers), Environmental Toxicology and Ecotoxicology (15 papers) and Computational Drug Discovery Methods (10 papers). Lingyun Mo collaborates with scholars based in China, Rwanda and United Kingdom. Lingyun Mo's co-authors include Li‐Tang Qin, Honghu Zeng, Yanpeng Liang, Shu‐Shen Liu, Yuhan Chen, Junfeng Dai, Dunqiu Wang, Hailing Liu, Rongni Dou and Yongan Liu and has published in prestigious journals such as Environmental Science & Technology, The Science of The Total Environment and Environmental Pollution.

In The Last Decade

Lingyun Mo

46 papers receiving 790 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lingyun Mo China 16 317 275 117 106 92 49 796
Li‐Tang Qin China 19 398 1.3× 397 1.4× 203 1.7× 127 1.2× 137 1.5× 65 1.1k
Melek Türker Saçan Türkiye 18 297 0.9× 266 1.0× 211 1.8× 74 0.7× 116 1.3× 40 864
Chung‐Yuan Chen Taiwan 19 222 0.7× 342 1.2× 88 0.8× 66 0.6× 116 1.3× 47 689
Yuanhui Zhao China 22 503 1.6× 483 1.8× 183 1.6× 176 1.7× 173 1.9× 61 1.3k
Luise Henneberger Germany 19 234 0.7× 517 1.9× 101 0.9× 139 1.3× 142 1.5× 36 879
Diana Montes‐Grajales Colombia 10 170 0.5× 179 0.7× 47 0.4× 81 0.8× 44 0.5× 15 535
Rita Schlichting Germany 21 490 1.5× 745 2.7× 68 0.6× 239 2.3× 102 1.1× 33 1.3k
Sander C. van der Linden Netherlands 12 458 1.4× 659 2.4× 30 0.3× 137 1.3× 67 0.7× 13 973
Rick Helmus Netherlands 20 318 1.0× 465 1.7× 37 0.3× 230 2.2× 317 3.4× 42 1.2k
Juan M. Ribo Canada 12 196 0.6× 257 0.9× 42 0.4× 159 1.5× 89 1.0× 19 614

Countries citing papers authored by Lingyun Mo

Since Specialization
Citations

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

Fields of papers citing papers by Lingyun Mo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lingyun Mo

This figure shows the co-authorship network connecting the top 25 collaborators of Lingyun Mo. A scholar is included among the top collaborators of Lingyun Mo 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 Lingyun Mo. Lingyun Mo 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.
Qin, Li‐Tang, et al.. (2024). Toxic interactions at the physiological and biochemical levels of green algae under stress of mixtures of three azole fungicides. The Science of The Total Environment. 926. 171771–171771. 4 indexed citations
2.
Xie, Huaijun, et al.. (2024). Joint probabilistic risk of organic micropollutants in the aquaculture seawater around Liaodong Peninsula. The Science of The Total Environment. 954. 176331–176331. 1 indexed citations
3.
Qin, Li‐Tang, et al.. (2024). A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa). Environment International. 194. 109162–109162. 4 indexed citations
4.
Liu, Yongan, et al.. (2024). Predicting the Time-Dependent Toxicities of Binary Mixtures of Five Antibiotics to Vibrio qinghaiensis sp.-Q67 Based on the QSAR Model. Environment & Health. 2(7). 465–473. 6 indexed citations
5.
Qin, Li‐Tang, et al.. (2024). Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures. Environmental Pollution. 360. 124565–124565. 13 indexed citations
6.
Mo, Lingyun, et al.. (2024). LC-QTOF/MS-based non-targeted metabolomics to explore the toxic effects of di(2-ethylhexyl) phthalate (DEHP) on Brassica chinensis L.. The Science of The Total Environment. 918. 170817–170817. 8 indexed citations
7.
Liu, Min, et al.. (2023). Toxicity interactions of azole fungicide mixtures on Chlorella pyrenoidosa . Environmental Toxicology. 38(7). 1509–1519. 8 indexed citations
8.
Mo, Lingyun, et al.. (2023). Mechanism of time-dependent toxicity of quinolone antibiotics on luminescent bacteria Vibrio qinghaiensis sp.-Q67. Ecotoxicology and Environmental Safety. 255. 114784–114784. 16 indexed citations
9.
Mo, Lingyun, Yongan Liu, Jie Zhu, et al.. (2020). Benefits from hazards, benefits from nothing, and benefits from benefits: the combined effects of five quaternary ammonium compounds to Vibrio qinghaiensis Q67. Environmental Sciences Europe. 32(1). 23 indexed citations
10.
Li, Yanhong, Xiao-Zhang Yu, Lingyun Mo, Yu-Juan Lin, & Qing Zhang. (2019). Involvement of glutamate receptors in regulating calcium influx in rice seedlings under Cr exposure. Ecotoxicology. 28(6). 650–657. 10 indexed citations
11.
Qin, Li‐Tang, et al.. (2018). QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide. Chemosphere. 198. 122–129. 60 indexed citations
12.
Mo, Lingyun, et al.. (2017). Joint toxicity of six common heavy metals to Chlorella pyrenoidosa. Environmental Science and Pollution Research. 26(30). 30554–30560. 18 indexed citations
13.
Liu, Jie, et al.. (2017). Decapitation improves the efficiency of Cd phytoextraction by Celosia argentea Linn. Chemosphere. 181. 382–389. 12 indexed citations
14.
Mo, Lingyun, et al.. (2016). Quantitative Characterization of the Toxicities of Cd-Ni and Cd-Cr Binary Mixtures Using Combination Index Method. BioMed Research International. 2016. 1–6. 9 indexed citations
15.
16.
Qin, Li‐Tang, et al.. (2015). Linear regression model for predicting interactive mixture toxicity of pesticide and ionic liquid. Environmental Science and Pollution Research. 22(16). 12759–12768. 11 indexed citations
17.
18.
Mo, Lingyun, et al.. (2011). Combined Toxicity of the Mixtures of Phenol and Aniline Derivatives to Vibrio qinghaiensis sp.-Q67. Bulletin of Environmental Contamination and Toxicology. 87(4). 473–479. 15 indexed citations
19.
Dou, Rongni, et al.. (2010). A novel direct equipartition ray design (EquRay) procedure for toxicity interaction between ionic liquid and dichlorvos. Environmental Science and Pollution Research. 18(5). 734–742. 70 indexed citations
20.
Liu, Shu‐Shen, et al.. (2009). CoMFA and CoMSIA analysis of 2,4-thiazolidinediones derivatives as aldose reductase inhibitors. Journal of Molecular Modeling. 15(7). 837–845. 17 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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