Shanjun Mao

6.1k total citations · 3 hit papers
105 papers, 5.2k citations indexed

About

Shanjun Mao is a scholar working on Materials Chemistry, Renewable Energy, Sustainability and the Environment and Organic Chemistry. According to data from OpenAlex, Shanjun Mao has authored 105 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Materials Chemistry, 41 papers in Renewable Energy, Sustainability and the Environment and 32 papers in Organic Chemistry. Recurrent topics in Shanjun Mao's work include Electrocatalysts for Energy Conversion (33 papers), Catalytic Processes in Materials Science (29 papers) and Nanomaterials for catalytic reactions (27 papers). Shanjun Mao is often cited by papers focused on Electrocatalysts for Energy Conversion (33 papers), Catalytic Processes in Materials Science (29 papers) and Nanomaterials for catalytic reactions (27 papers). Shanjun Mao collaborates with scholars based in China, Russia and Portugal. Shanjun Mao's co-authors include Yong Wang, Zhongzhe Wei, Jing Wang, Haoran Li, Yiqing Chen, Haiyan Jin, Yuzhuo Chen, Yueling Cao, Mingming Li and Yutong Gong and has published in prestigious journals such as Journal of the American Chemical Society, Chemical Society Reviews and Angewandte Chemie International Edition.

In The Last Decade

Shanjun Mao

97 papers receiving 5.1k citations

Hit Papers

Highly uniform Ru nanoparticles over N-doped carbon: pH a... 2017 2026 2020 2023 2018 2017 2023 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shanjun Mao China 38 2.8k 2.2k 1.7k 1.5k 951 105 5.2k
Zhongzhe Wei China 37 4.1k 1.5× 2.4k 1.1× 2.8k 1.6× 1.2k 0.8× 966 1.0× 71 6.1k
Chao Yu China 38 2.1k 0.7× 2.0k 0.9× 2.0k 1.2× 765 0.5× 546 0.6× 114 4.7k
Wengang Liu China 19 2.3k 0.8× 1.7k 0.8× 1.1k 0.6× 957 0.6× 555 0.6× 55 3.7k
Paul N. Duchesne Canada 34 4.6k 1.7× 3.9k 1.8× 2.1k 1.3× 552 0.4× 1.0k 1.1× 55 6.6k
Aijuan Han China 33 3.5k 1.3× 2.0k 0.9× 2.4k 1.4× 553 0.4× 465 0.5× 60 4.8k
Zechao Zhuang China 52 5.3k 1.9× 3.9k 1.8× 3.9k 2.3× 597 0.4× 1.6k 1.7× 154 8.6k
Qingquan Kong China 48 3.9k 1.4× 2.2k 1.0× 2.5k 1.5× 683 0.4× 2.9k 3.0× 177 6.9k
Molly Meng‐Jung Li Hong Kong 31 2.3k 0.8× 2.3k 1.0× 953 0.6× 415 0.3× 1.4k 1.5× 78 4.1k
Xiaobo Zheng China 36 3.5k 1.3× 2.1k 1.0× 3.7k 2.2× 399 0.3× 795 0.8× 91 6.2k

Countries citing papers authored by Shanjun Mao

Since Specialization
Citations

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

Fields of papers citing papers by Shanjun Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shanjun Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Shanjun Mao. A scholar is included among the top collaborators of Shanjun 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 Shanjun Mao. Shanjun 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
2.
Li, Ben, Jiadong Chen, Lihua Wang, et al.. (2025). High‐Performance Bi‐Based Catalysts for CO₂ Reduction: In Situ Formation of Bi/Bi₂O₂CO₃ and Enhanced Formate Production. Advanced Science. 12(10). e2415616–e2415616. 11 indexed citations
3.
Liu, Wencong, Bing Lü, Shipan Liang, et al.. (2025). Noble Metal‐Free Low‐Temperature Aromatization of Polyethylene Waste for High‐Quality Fuel Production. Angewandte Chemie International Edition. 64(39). e202506815–e202506815.
4.
Mao, Shanjun, et al.. (2025). ConvMamba: Combining Mamba with CNN for hyperspectral image classification. Neurocomputing. 652. 131016–131016.
5.
Tan, Mingwu, Jichao Shi, Lili Zhang, et al.. (2025). Accelerating Oxygen Evolution Activity via Premagnetization-Induced Active Sites in Ferromagnetic Nickel–Iron Hydroxide Catalysts. JACS Au. 5(6). 2500–2512. 4 indexed citations
6.
Mao, Shanjun, et al.. (2025). Efficient Upcycling of Polyolefin Waste to Light Aromatics via Coupling C─C Scission and Carbonylation. Angewandte Chemie International Edition. 64(22). e202424334–e202424334. 4 indexed citations
7.
Wang, Xiaoqing, Cong Du, Hao Wang, et al.. (2024). Bimetallic Ni and Pt single atoms anchored on nitrogen–phosphorus doped porous carbon fibers to achieve significant electrocatalytic hydrogen evolution. Separation and Purification Technology. 349. 127909–127909. 8 indexed citations
8.
Wang, Xiaoqing, Cong Du, Tuo Li, et al.. (2024). Efficient photocatalytic degradation with a lattice-matched α-Bi2O3/Co3O4 Z-scheme heterojunction: An integrated experimental and DFT study. Journal of Water Process Engineering. 65. 105829–105829. 11 indexed citations
9.
Mao, Shanjun, Qian Luo, Honghui Ning, et al.. (2024). Mediating trade-off between activity and selectivity in alkynes semi-hydrogenation via a hydrophilic polar layer. Nature Communications. 15(1). 1228–1228. 29 indexed citations
10.
Li, Mei, et al.. (2024). The Crack Diffusion Model: An Innovative Diffusion-Based Method for Pavement Crack Detection. Remote Sensing. 16(6). 986–986. 18 indexed citations
11.
Mao, Shanjun, et al.. (2024). Refinement of selective hydrogenation catalysts guided by the concept of ‘group recognition’. Fundamental Research. 2 indexed citations
12.
Wang, Hao, et al.. (2024). Personalized machine learning models of terminal olefin hydroformylation for regioselectivity prediction. Chem Catalysis. 4(9). 101079–101079. 3 indexed citations
14.
Zheng, Xiaozhong, Xiaoyun Shi, Honghui Ning, et al.. (2023). Tailoring a local acid-like microenvironment for efficient neutral hydrogen evolution. Nature Communications. 14(1). 4209–4209. 162 indexed citations breakdown →
15.
Chen, Yuzhuo, Hao Wang, Yi Ni, et al.. (2023). Fine-structure sensitive deep learning framework for predicting catalytic properties with high precision. CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION). 50. 284–296. 3 indexed citations
16.
Shi, Xiaoyun, Xiaozhong Zheng, Hao Wang, et al.. (2023). Hierarchical Crystalline/Amorphous Heterostructure MoNi/NiMoOx for Electrochemical Hydrogen Evolution with Industry‐Level Activity and Stability. Advanced Functional Materials. 33(41). 67 indexed citations
17.
Li, Mei, et al.. (2023). A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model. Remote Sensing. 16(1). 97–97. 15 indexed citations
18.
Zhang, Liwei, Shanjun Mao, Yali Liu, et al.. (2023). Tandem catalytic efficient olefin epoxidation with integrated production of nicotinamide derivatives. Chem Catalysis. 3(8). 100691–100691. 8 indexed citations
19.
Ning, Honghui, Yuzhuo Chen, Zhenzhen Wang, et al.. (2021). Selective upgrading of biomass-derived benzylic ketones by (formic acid)–Pd/HPC–NH2 system with high efficiency under ambient conditions. Chem. 7(11). 3069–3084. 36 indexed citations
20.
Bao, Xiaobing, Yutong Gong, Yuzhuo Chen, et al.. (2019). Carbon vacancy defect-activated Pt cluster for hydrogen generation. Journal of Materials Chemistry A. 7(25). 15364–15370. 76 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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