E. Donskoi

838 total citations
42 papers, 679 citations indexed

About

E. Donskoi is a scholar working on Mechanical Engineering, Water Science and Technology and Biomedical Engineering. According to data from OpenAlex, E. Donskoi has authored 42 papers receiving a total of 679 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Mechanical Engineering, 18 papers in Water Science and Technology and 15 papers in Biomedical Engineering. Recurrent topics in E. Donskoi's work include Mineral Processing and Grinding (27 papers), Iron and Steelmaking Processes (21 papers) and Minerals Flotation and Separation Techniques (18 papers). E. Donskoi is often cited by papers focused on Mineral Processing and Grinding (27 papers), Iron and Steelmaking Processes (21 papers) and Minerals Flotation and Separation Techniques (18 papers). E. Donskoi collaborates with scholars based in Australia, Austria and New Zealand. E. Donskoi's co-authors include D. L. S. McElwain, Louis Wibberley, James Manuel, S. P. Suthers, Sarath Hapugoda, Michael Peterson, J. J. Campbell, Jingyu Shi, J. M. F. Clout and Merrick R. Mahoney and has published in prestigious journals such as Fuel, Combustion and Flame and International Journal of Coal Geology.

In The Last Decade

E. Donskoi

41 papers receiving 648 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
E. Donskoi Australia 17 515 362 170 90 57 42 679
Roberto Parra Chile 14 446 0.9× 246 0.7× 76 0.4× 83 0.9× 13 0.2× 48 576
D.J. Spottiswood New Zealand 9 409 0.8× 212 0.6× 377 2.2× 48 0.5× 15 0.3× 15 680
E.G. Kelly New Zealand 9 422 0.8× 157 0.4× 288 1.7× 40 0.4× 16 0.3× 20 679
Jiakun Tan China 13 266 0.5× 97 0.3× 318 1.9× 42 0.5× 15 0.3× 27 485
Chenyang Zhou China 14 302 0.6× 91 0.3× 170 1.0× 28 0.3× 17 0.3× 59 541
M.T. Bona Spain 10 144 0.3× 88 0.2× 29 0.2× 72 0.8× 26 0.5× 16 381
Abdulmohsen Alsaiari Saudi Arabia 13 269 0.5× 191 0.5× 191 1.1× 41 0.5× 68 1.2× 38 696
Arsam Behkish United States 9 201 0.4× 439 1.2× 197 1.2× 52 0.6× 7 0.1× 13 529
Worrada Nookuea Sweden 8 271 0.5× 181 0.5× 28 0.2× 55 0.6× 45 0.8× 14 553
Wan Sun China 15 220 0.4× 96 0.3× 140 0.8× 83 0.9× 15 0.3× 70 741

Countries citing papers authored by E. Donskoi

Since Specialization
Citations

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

Fields of papers citing papers by E. Donskoi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of E. Donskoi

This figure shows the co-authorship network connecting the top 25 collaborators of E. Donskoi. A scholar is included among the top collaborators of E. Donskoi 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 E. Donskoi. E. Donskoi 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.
Donskoi, E., et al.. (2025). Systematic differences in the microstructure of cokes made from medium/high-reflectance Northern hemisphere and Australian coals. International Journal of Coal Geology. 305. 104790–104790.
2.
Donskoi, E., et al.. (2023). Utilisation of Enhanced Thresholding for Non-Opaque Mineral Segmentation in Optical Image Analysis. Minerals. 13(3). 350–350. 3 indexed citations
3.
Donskoi, E., S. P. Suthers, & Mark I. Pownceby. (2022). Ultrasonic treatment of high phosphorus Australian iron ore fines. Minerals Engineering. 189. 107914–107914. 5 indexed citations
4.
Donskoi, E., et al.. (2020). Advances in Optical Image Analysis Textural Segmentation in Ironmaking. Applied Sciences. 10(18). 6242–6242. 8 indexed citations
5.
Donskoi, E., et al.. (2017). Mineral 4/Recognition 4: A Universal Optical Image Analysis Package for Iron Ore, Sinter and Coke Characterization. Journal of Energy and Power Engineering. 11(1). 1 indexed citations
6.
7.
Hapugoda, Sarath, Liming Lü, E. Donskoi, & James Manuel. (2016). Mineralogical quantification of iron ore sinter. Mineral Processing and Extractive Metallurgy Transactions of the Institutions of Mining and Metallurgy Section C. 125(3). 156–164. 13 indexed citations
8.
Donskoi, E., et al.. (2015). Novel developments in optical image analysis for iron ore, sinter and coke characterisation. Applied Earth Science Transactions of the Institutions of Mining and Metallurgy Section B. 124(4). 227–244. 16 indexed citations
9.
Donskoi, E., Ralph J. Holmes, S. P. Suthers, et al.. (2015). Iron ore textural information is the key for prediction of downstream process performance. Minerals Engineering. 86. 10–23. 29 indexed citations
10.
Donskoi, E., et al.. (2013). Comparative study of iron ore characterisation using a scanning electron microscope and optical image analysis. Applied Earth Science Transactions of the Institutions of Mining and Metallurgy Section B. 122(4). 217–229. 22 indexed citations
11.
Donskoi, E., et al.. (2012). Utilisation of ultrasonic treatment for upgrading of hematitic/goethitic iron ore fines. International Journal of Mineral Processing. 114-117. 80–92. 23 indexed citations
12.
Donskoi, E., et al.. (2008). Modelling and optimization of hydrocyclone for iron ore fines beneficiation — using optical image analysis and iron ore texture classification. International Journal of Mineral Processing. 87(3-4). 106–119. 36 indexed citations
13.
Shi, Jingyu, E. Donskoi, D. L. S. McElwain, & Louis Wibberley. (2008). Modelling novel coal based direct reduction process. Ironmaking & Steelmaking Processes Products and Applications. 35(1). 3–13. 6 indexed citations
14.
Donskoi, E., et al.. (2007). Examination of ultrasonic treatment of iron ore fines using automatic iron ore texture classification. Queensland's institutional digital repository (The University of Queensland). 251–257. 4 indexed citations
15.
Donskoi, E., Rene I. Olivares, D. L. S. McElwain, & Louis Wibberley. (2006). Experimental study of coal based direct reduction in iron ore/coal composite pellets in a one layer bed under nonisothermal, asymmetric heating. Ironmaking & Steelmaking Processes Products and Applications. 33(1). 24–28. 26 indexed citations
16.
Homer, J., et al.. (2005). Three-dimensional bistatic synthetic aperture radar imaging system: spatial resolution analysis. IEE Proceedings - Radar Sonar and Navigation. 152(6). 391–394. 7 indexed citations
17.
Shi, Jingyu, E. Donskoi, D. L. S. McElwain, & Louis Wibberley. (2005). Modelling the reduction of an iron ore-coal composite pellet with conduction and convection in an axisymmetric temperature field. Mathematical and Computer Modelling. 42(1-2). 45–60. 33 indexed citations
18.
Donskoi, E., D. L. S. McElwain, & Louis Wibberley. (2003). Sensitivity analysis of a model for direct reduction in swelling coal char-hematite composite pellets. ANZIAM Journal. 44. 140–140. 5 indexed citations
19.
Li, Yonghong, et al.. (2002). 3D Multi-static SAR System for Terrain Imaging Based on Indirect GPS Signals. Journal of Global Positioning Systems. 1(1). 34–39. 3 indexed citations
20.
McGuinness, Mark, E. Donskoi, & D. L. S. McElwain. (1999). Asymptotic approximations to the Distributed Activation Energy Model. Applied Mathematics Letters. 12(8). 27–34. 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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