Hao Zhan

3.1k total citations
71 papers, 2.5k citations indexed

About

Hao Zhan is a scholar working on Biomedical Engineering, Mechanical Engineering and Materials Chemistry. According to data from OpenAlex, Hao Zhan has authored 71 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Biomedical Engineering, 28 papers in Mechanical Engineering and 12 papers in Materials Chemistry. Recurrent topics in Hao Zhan's work include Thermochemical Biomass Conversion Processes (42 papers), Lignin and Wood Chemistry (18 papers) and Catalysis and Hydrodesulfurization Studies (17 papers). Hao Zhan is often cited by papers focused on Thermochemical Biomass Conversion Processes (42 papers), Lignin and Wood Chemistry (18 papers) and Catalysis and Hydrodesulfurization Studies (17 papers). Hao Zhan collaborates with scholars based in China, United States and United Kingdom. Hao Zhan's co-authors include Xiuli Yin, Xiuzheng Zhuang, Chuangzhi Wu, Yanpei Song, Yanqin Huang, Haoyi Peng, Hailong Li, Weijin Zhang, Lijian Leng and Chao He and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, The Science of The Total Environment and Bioresource Technology.

In The Last Decade

Hao Zhan

67 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hao Zhan China 30 1.7k 785 332 317 262 71 2.5k
Xianqing Zhu China 33 1.5k 0.9× 736 0.9× 400 1.2× 503 1.6× 273 1.0× 141 3.2k
Sandra Vitolo Italy 27 2.1k 1.2× 1.2k 1.6× 371 1.1× 311 1.0× 315 1.2× 104 3.2k
Abdul Raheem China 29 1.7k 1.0× 517 0.7× 367 1.1× 479 1.5× 334 1.3× 51 2.9k
M.E. Sánchez Spain 28 1.3k 0.8× 395 0.5× 359 1.1× 279 0.9× 215 0.8× 52 2.2k
Hyungseok Nam South Korea 29 1.5k 0.9× 789 1.0× 460 1.4× 319 1.0× 395 1.5× 89 2.7k
Haoyi Peng China 25 1.3k 0.7× 632 0.8× 231 0.7× 314 1.0× 315 1.2× 35 2.2k
Tianhua Yang China 32 2.0k 1.1× 697 0.9× 422 1.3× 635 2.0× 394 1.5× 160 3.5k
Zhiquan Hu China 29 1.7k 1.0× 576 0.7× 503 1.5× 327 1.0× 150 0.6× 61 2.6k
Je‐Lueng Shie Taiwan 28 1.2k 0.7× 575 0.7× 657 2.0× 283 0.9× 335 1.3× 84 2.5k
Shaojian Jiang China 14 1.1k 0.6× 512 0.7× 293 0.9× 299 0.9× 446 1.7× 37 2.2k

Countries citing papers authored by Hao Zhan

Since Specialization
Citations

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

Fields of papers citing papers by Hao Zhan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hao Zhan

This figure shows the co-authorship network connecting the top 25 collaborators of Hao Zhan. A scholar is included among the top collaborators of Hao Zhan 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 Hao Zhan. Hao Zhan 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.
Leng, Lijian, Tanghao Li, Jiaxin Gao, et al.. (2025). Machine-learning-aided prediction of the biological compositions and building block profiles of biomass using only their elemental compositions. Chemical Engineering Journal. 507. 160512–160512. 2 indexed citations
2.
Chen, Jincheng, Jialin Yang, Mingjie Chen, et al.. (2025). Hydrothermal carbonization of alcohol industry residues: More understandings from solid biofuel characteristics. Biomass and Bioenergy. 204. 108449–108449.
3.
Xu, Tianle, Mingjie Chen, Xiang Meng, et al.. (2025). Acid/alkali-assisted hydrothermal valorization of brewer's spent grain: Insights from element distribution and product potential. Journal of Cleaner Production. 519. 145990–145990. 2 indexed citations
4.
Leng, Lijian, Jiaxin Gao, Weijin Zhang, et al.. (2025). Machine-learning-aided prediction of the building block profiles of lignocellulosic biomass using elemental composition. Biomass and Bioenergy. 205. 108531–108531.
5.
Xu, Chi, Zishun Zhou, Wanwan Li, et al.. (2025). Facile fabrication of bamboo-derived hard carbon as anode for sodium-ion battery: Roles of acid-leaching and pre‑carbonization treatments. Journal of Energy Storage. 132. 117699–117699.
6.
Wang, Zheng, Meng Xiang, Jialin Yang, et al.. (2025). Co-combustion of brewery spent grain and coal: optimization strategies and synergistic effects. Energy. 327. 136494–136494. 3 indexed citations
7.
Leng, Lijian, Zhibin Wu, Shengqiang Liu, et al.. (2025). Engineering biochar from biomass pyrolysis for effective adsorption of heavy metal: An innovative machine learning approach. Separation and Purification Technology. 361. 131592–131592. 16 indexed citations
8.
Xu, Chi, Xiaoran Fan, Jialin Yang, et al.. (2025). Co-hydrothermal carbonization of antibiotic fermentation residues and Camellia oleifera shell: Enhancing coalification, demineralization, and denitrogenation for producing solid biofuel. Separation and Purification Technology. 378. 134633–134633. 1 indexed citations
9.
10.
Han, Xing, et al.. (2024). Simulation of Dendrite Remelting via the Phase-Field Method. Coatings. 14(11). 1364–1364.
11.
Leng, Lijian, Weijin Zhang, Jiefeng Chen, et al.. (2023). Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes. Energy. 288. 129854–129854. 18 indexed citations
12.
Li, Hailong, Jiefeng Chen, Weijin Zhang, et al.. (2023). Machine-learning-aided thermochemical treatment of biomass: a review. Biofuel Research Journal. 10(1). 1786–1809. 110 indexed citations
13.
Zeng, Zhiyong, et al.. (2023). Effects of the atomisation spray on heating transfer in evaporative condensers: A numerical study. Thermal Science and Engineering Progress. 42. 101923–101923. 6 indexed citations
14.
Leng, Lijian, Weijin Zhang, Qingyue Chen, et al.. (2022). Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass. Bioresource Technology. 362. 127791–127791. 48 indexed citations
15.
Liu, Jianguo, Hao Zhan, Nan Wang, et al.. (2021). Palladium Nanoparticles on Covalent Organic Framework Supports as Catalysts for Suzuki–Miyaura Cross-Coupling Reactions. ACS Applied Nano Materials. 4(6). 6239–6249. 51 indexed citations
16.
17.
Zhuang, Xiuzheng, Hao Zhan, Yanqin Huang, et al.. (2018). Denitrification and desulphurization of industrial biowastes via hydrothermal modification. Bioresource Technology. 254. 121–129. 99 indexed citations
18.
Zhuang, Xiuzheng, Yanqin Huang, Yanpei Song, et al.. (2017). The transformation pathways of nitrogen in sewage sludge during hydrothermal treatment. Bioresource Technology. 245(Pt A). 463–470. 205 indexed citations
19.
Zhan, Hao, et al.. (2016). Formation of Nitrogenous Pollutants during Biomass Thermo-Chemical Conversion. Institutional Repository of Guangzhou Institute of Energy Research, Chinese Academy of Sciences. 9 indexed citations
20.
Zhan, Hao. (2005). Identification of Multiple Damaged Locations in Structures Based on Curvature Mode and Flexibility Curvature. Journal of Wuhan University of Technology-Mater Sci Ed. 2 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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