Feng Mao

3.2k total citations
90 papers, 2.3k citations indexed

About

Feng Mao is a scholar working on Insect Science, Genetics and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Feng Mao has authored 90 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Insect Science, 35 papers in Genetics and 23 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Feng Mao's work include Insect and Pesticide Research (38 papers), Insect and Arachnid Ecology and Behavior (34 papers) and Plant and animal studies (21 papers). Feng Mao is often cited by papers focused on Insect and Pesticide Research (38 papers), Insect and Arachnid Ecology and Behavior (34 papers) and Plant and animal studies (21 papers). Feng Mao collaborates with scholars based in China, United Kingdom and United States. Feng Mao's co-authors include Yu Fang, Bin Han, David M. Hannah, Wouter Buytaert, Jianke Li, Jianke Li, Jianke Li, Han Hu, Timothy Karpouzoglou and Julian Clark and has published in prestigious journals such as Nature Communications, Environmental Science & Technology and PLoS ONE.

In The Last Decade

Feng Mao

84 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Feng Mao China 29 1.1k 782 585 419 222 90 2.3k
Johnie N. Jenkins United States 42 736 0.7× 369 0.5× 273 0.5× 248 0.6× 209 0.9× 376 6.7k
Rosa Fernández Spain 25 151 0.1× 699 0.9× 932 1.6× 407 1.0× 98 0.4× 78 2.6k
Andrew Mead United Kingdom 41 713 0.6× 462 0.6× 773 1.3× 401 1.0× 35 0.2× 123 5.9k
Christian Damgaard Denmark 29 497 0.4× 638 0.8× 1.3k 2.2× 639 1.5× 50 0.2× 211 3.9k
Pernille Thorbek United Kingdom 27 838 0.7× 510 0.7× 881 1.5× 483 1.2× 81 0.4× 74 2.8k
Sufang Zhang China 25 495 0.4× 289 0.4× 148 0.3× 97 0.2× 29 0.1× 108 2.1k
Niels Holst Denmark 24 839 0.7× 384 0.5× 460 0.8× 153 0.4× 73 0.3× 66 2.1k
Yi Zou China 29 508 0.5× 142 0.2× 561 1.0× 416 1.0× 69 0.3× 118 3.3k
Fernando E. Miguez United States 36 157 0.1× 214 0.3× 760 1.3× 533 1.3× 222 1.0× 100 5.2k
Jianghua Sun China 40 3.3k 2.9× 643 0.8× 806 1.4× 398 0.9× 31 0.1× 221 5.2k

Countries citing papers authored by Feng Mao

Since Specialization
Citations

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

Fields of papers citing papers by Feng Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Feng Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Feng Mao. A scholar is included among the top collaborators of Feng Mao 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 Feng Mao. Feng Mao 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.
2.
Li, Linwei, Kai Zhao, Lifeng Jiang, et al.. (2024). Effects of Reinforcement Content and Homogenization Treatment on the Microstructure and Mechanical Properties of in-situ TiB2/2219Al Composites. Acta Metallurgica Sinica (English Letters). 37(8). 1421–1437. 4 indexed citations
3.
Mao, Feng, Margaret Shanafield, Valérie Ouellet, David M. Hannah, & Stefan Krause. (2024). Assessing river discharge dynamics through relative surface water extent changes in river basins. iScience. 28(1). 111598–111598.
4.
Tosi, Davide, Terumoto Koike, Nestor Villamizar, et al.. (2024). Sublobar resection for lung adenocarcinoma less than 2 cm containing solid or micropapillary components radiologically presented as consolidation-to-tumor ratio (CTR) ≤0.25 [ground-glass opacity (GGO)]. Translational Lung Cancer Research. 13(7). 1685–1694. 1 indexed citations
5.
Xu, Cong, et al.. (2022). The impact of invisible-spreaders on COVID-19 transmission and work resumption. PLoS ONE. 17(1). e0252994–e0252994. 2 indexed citations
6.
Mao, Feng, Sami Ullah, Steven M. Gorelick, David M. Hannah, & Stefan Krause. (2021). Increasing nutrient inputs risk a surge of nitrous oxide emissions from global mangrove ecosystems. One Earth. 4(5). 742–748. 7 indexed citations
7.
Mao, Feng, et al.. (2021). Estimation of river flow using CubeSats remote sensing. The Science of The Total Environment. 788. 147762–147762. 21 indexed citations
8.
Hannah, David M., Iseult Lynch, Feng Mao, et al.. (2020). Author Correction: Water and sanitation for all in a pandemic. Nature Sustainability. 3(12). 1068–1068. 2 indexed citations
9.
Hannah, David M., Iseult Lynch, Feng Mao, et al.. (2020). Water and sanitation for all in a pandemic. Nature Sustainability. 3(10). 773–775. 54 indexed citations
10.
Zhang, Xufeng, Han Hu, Bin Han, et al.. (2020). The Neuroproteomic Basis of Enhanced Perception and Processing of Brood Signals That Trigger Increased Reproductive Investment in Honeybee (Apis mellifera) Workers. Molecular & Cellular Proteomics. 19(10). 1632–1648. 11 indexed citations
11.
Mao, Feng, Xianfu Zhao, Peiming Ma, et al.. (2019). Revision of biological indices for aquatic systems: A ridge-regression solution. Ecological Indicators. 106. 105478–105478. 12 indexed citations
12.
Hu, Han, Feng Mao, Xufeng Zhang, et al.. (2019). In-depth Proteome of the Hypopharyngeal Glands of Honeybee Workers Reveals Highly Activated Protein and Energy Metabolism in Priming the Secretion of Royal Jelly. Molecular & Cellular Proteomics. 18(4). 606–621. 46 indexed citations
13.
Wu, Fan, Bin Han, Han Hu, et al.. (2019). Mechanistic insight into binding interaction between chemosensory protein 4 and volatile larval pheromones in honeybees (Apis mellifera). International Journal of Biological Macromolecules. 141. 553–563. 15 indexed citations
14.
Du, Yuanyuan, Ying Ge, Yuan Ren, et al.. (2018). A global strategy to mitigate the environmental impact of China’s ruminant consumption boom. Nature Communications. 9(1). 4133–4133. 89 indexed citations
15.
Mao, Feng, Julian Clark, Timothy Karpouzoglou, et al.. (2017). HESS Opinions: A conceptual framework for assessing socio-hydrological resilience under change. Hydrology and earth system sciences. 21(7). 3655–3670. 52 indexed citations
16.
Cheng, Han, Bin Han, Feng Mao, et al.. (2017). Phosphoproteome Analysis Reveals Phosphorylation Underpinnings in the Brains of Nurse and Forager Honeybees (Apis mellifera). Scientific Reports. 7(1). 1973–1973. 14 indexed citations
17.
Fan, Pei, Bin Han, Feng Mao, et al.. (2016). Functional and Proteomic Investigations Reveal Major Royal Jelly Protein 1 Associated with Anti-hypertension Activity in Mouse Vascular Smooth Muscle Cells. Scientific Reports. 6(1). 30230–30230. 40 indexed citations
18.
Li, Rongli, Lan Zhang, Yu Fang, et al.. (2013). Proteome and phosphoproteome analysis of honeybee (Apis mellifera) venom collected from electrical stimulation and manual extraction of the venom gland. BMC Genomics. 14(1). 766–766. 61 indexed citations
19.
Mao, Feng & Jianke Li. (2009). Proteome analysis of the development of hypopharyngeal gland of high royal jelly producing bees and native Italian bees.. Zhongguo nongye Kexue. 42(2). 677–687. 2 indexed citations
20.

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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