Dan Long

1.7k total citations
52 papers, 1.4k citations indexed

About

Dan Long is a scholar working on Materials Chemistry, Renewable Energy, Sustainability and the Environment and Electrical and Electronic Engineering. According to data from OpenAlex, Dan Long has authored 52 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Materials Chemistry, 19 papers in Renewable Energy, Sustainability and the Environment and 15 papers in Electrical and Electronic Engineering. Recurrent topics in Dan Long's work include Advanced Photocatalysis Techniques (19 papers), Perovskite Materials and Applications (9 papers) and Functional Brain Connectivity Studies (6 papers). Dan Long is often cited by papers focused on Advanced Photocatalysis Techniques (19 papers), Perovskite Materials and Applications (9 papers) and Functional Brain Connectivity Studies (6 papers). Dan Long collaborates with scholars based in China, United States and Australia. Dan Long's co-authors include Yongping Zhang, Xi Rao, Hailong Dou, Yaqin Chai, Ruo Yuan, Xianwei Meng, Longfei Tan, Shaohui Zheng, Minming Zhang and Xiaojun Xu and has published in prestigious journals such as Nano Letters, ACS Nano and PLoS ONE.

In The Last Decade

Dan Long

48 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dan Long China 23 633 528 398 325 275 52 1.4k
Ling Liu China 23 781 1.2× 719 1.4× 350 0.9× 471 1.4× 144 0.5× 67 2.2k
Xihua Chen China 23 876 1.4× 108 0.2× 322 0.8× 245 0.8× 469 1.7× 76 2.1k
Jingwen Yu China 22 609 1.0× 401 0.8× 339 0.9× 772 2.4× 305 1.1× 74 2.0k
Chenyang Wei China 22 892 1.4× 192 0.4× 814 2.0× 389 1.2× 241 0.9× 52 1.8k
Huihui Shi China 27 737 1.2× 242 0.5× 655 1.6× 217 0.7× 560 2.0× 65 2.2k
Sihui Chen China 21 599 0.9× 229 0.4× 209 0.5× 674 2.1× 396 1.4× 84 1.4k
Xinyue Xia China 10 461 0.7× 456 0.9× 554 1.4× 188 0.6× 185 0.7× 22 1.4k
Ronghui Zhou China 29 1.1k 1.7× 165 0.3× 1.1k 2.9× 517 1.6× 912 3.3× 82 3.0k
Junli Zhou China 24 555 0.9× 210 0.4× 488 1.2× 444 1.4× 198 0.7× 64 1.5k
Won Hyuck Suh South Korea 20 572 0.9× 172 0.3× 415 1.0× 220 0.7× 358 1.3× 45 1.7k

Countries citing papers authored by Dan Long

Since Specialization
Citations

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

Fields of papers citing papers by Dan Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Long

This figure shows the co-authorship network connecting the top 25 collaborators of Dan Long. A scholar is included among the top collaborators of Dan Long 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 Dan Long. Dan Long 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.
Long, Dan, et al.. (2025). cGAS-STING pathway as a promising target for digestive diseases: insights from natural plant products. Frontiers in Nutrition. 12. 1594120–1594120. 2 indexed citations
2.
3.
Long, Dan, et al.. (2023). Intelligent diagnosis of major depression disease based on multi-layer brain network. Frontiers in Neuroscience. 17. 1126865–1126865. 7 indexed citations
4.
Ye, Xiaoyan, et al.. (2023). Stroke Prediction Using the Trust Evaluation with Data Leakage Avoiding. Journal of Physics Conference Series. 2560(1). 12051–12051. 1 indexed citations
5.
Long, Dan, et al.. (2022). The Effect Analysis of Atlas and Global Signal Regression in Classification based on Brain Network for Major Depression Disorders. Journal of Imaging Science and Technology. 66(4). 40413–1. 5 indexed citations
6.
Long, Dan, et al.. (2021). Photoelectrochemical Assay Based on SnO2/BiOBr p–n Heterojunction for Ultrasensitive DNA Detection. Analytical Chemistry. 93(38). 12995–13000. 45 indexed citations
7.
Lü, Cheng, et al.. (2020). N and Sn Co-Doped hematite photoanodes for efficient solar water oxidation. Journal of Colloid and Interface Science. 585. 660–667. 14 indexed citations
8.
Zhu, Linna, Jing Xu, Yahan Shan, et al.. (2019). Diaryl ketone-based hole-transporting materials for efficient perovskite solar cells. Journal of Materials Chemistry C. 7(11). 3226–3230. 19 indexed citations
9.
Chen, Ying, Zhimin Ye, Fangzheng Wang, et al.. (2019). ADC correlation with Sirtuin1 to assess early chemoradiotherapy response of locally advanced esophageal carcinoma patients. Radiation Oncology. 14(1). 192–192. 8 indexed citations
10.
Long, Dan, Hailong Dou, Xi Rao, Zhiqian Chen, & Yongping Zhang. (2019). Z-Scheme Ag3PO4/g-C3N4 Nanocomposites for Robust Cocatalyst-Free Photocatalytic H2 Evolution Under Visible Light Irradiation. Catalysis Letters. 149(5). 1154–1166. 22 indexed citations
11.
Rao, Xi, Hailong Dou, Dan Long, & Yongping Zhang. (2019). Ag3PO4/g-C3N4 nanocomposites for photocatalytic degradating gas phase formaldehyde at continuous flow under 420 nm LED irradiation. Chemosphere. 244. 125462–125462. 33 indexed citations
12.
Yang, Xiaohui, et al.. (2019). An integrated inverse space sparse representation framework for tumor classification. Pattern Recognition. 93. 293–311. 14 indexed citations
13.
Yin, Zhifan, Baojun Liu, Shiying Fan, et al.. (2019). In situ FTIR spectra investigation of the photocatalytic degradation of gaseous toluene over a novel hedgehog-like CaFe2O4 hollow-structured materials. Catalysis Communications. 130. 105754–105754. 26 indexed citations
14.
Dou, Hailong, Dan Long, Xi Rao, et al.. (2019). Photocatalytic Degradation Kinetics of Gaseous Formaldehyde Flow Using TiO2 Nanowires. ACS Sustainable Chemistry & Engineering. 7(4). 4456–4465. 84 indexed citations
15.
Yang, Xiaohui, Yunmei Chen, Xianqi Li, et al.. (2018). Breast Tumor Classification Based on Decision Information Genes and Inverse Projection Sparse Representation..
16.
17.
Fu, Changhui, Longfei Tan, Tianlong Liu, et al.. (2017). Imaging-guided synergetic therapy of orthotopic transplantation tumor by superselectively arterial administration of microwave-induced microcapsules. Biomaterials. 133. 144–153. 34 indexed citations
18.
Long, Dan, Jingsong Mao, Tianlong Liu, et al.. (2016). Highly stable microwave susceptible agents via encapsulation of Ti-mineral superfine powders in urea-formaldehyde resin microcapsules for tumor hyperthermia therapy. Nanoscale. 8(21). 11044–11051. 23 indexed citations
19.
Hu, Shaohua, Dongrong Xu, Bradley S. Peterson, et al.. (2013). Association of Cerebral Networks in Resting State with Sexual Preference of Homosexual Men: A Study of Regional Homogeneity and Functional Connectivity. PLoS ONE. 8(3). e59426–e59426. 100 indexed citations
20.
Long, Dan, Jinwei Wang, Min Xuan, et al.. (2012). Automatic Classification of Early Parkinson's Disease with Multi-Modal MR Imaging. PLoS ONE. 7(11). e47714–e47714. 119 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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