Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Advance review on the exploitation of the prominent energy-storage element: Lithium. Part I: From mineral and brine resources
2016415 citationsMin‐seuk Kim, Rajiv Ranjan Srivastava et al.Minerals Engineeringprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jae-chun Lee'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 Jae-chun Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jae-chun Lee more than expected).
This network shows the impact of papers produced by Jae-chun Lee. 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 Jae-chun Lee. The network helps show where Jae-chun Lee may publish in the future.
Co-authorship network of co-authors of Jae-chun Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Jae-chun Lee.
A scholar is included among the top collaborators of Jae-chun Lee 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 Jae-chun Lee. Jae-chun Lee is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kim, Min‐seuk, Jae-chun Lee, Eun Young Kim, & Young-Soo Yoo. (2010). Leaching of CMSX-4 Superalloy in Hydrochloric Acid Solutions. Journal of the Korean Institute of Resources Recycling. 19(5). 25–30.6 indexed citations
14.
Kim, Eun Young, et al.. (2008). Leaching of copper from waste PCBs with electro-generated chlorine -Analysis of experimental factors on the leaching by the factorial design-. Journal of the Korean Institute of Resources Recycling. 17(6). 24–33.1 indexed citations
15.
Kim, Byung‐Su, Jae-chun Lee, & Kwang-Ho Lee. (2007). A Novel Process for Extracting Valuable Metals from Waste Electric and Electronic Scrap Using Waste Copper Slag by a High temperature Melting Method. Journal of the Korean Institute of Resources Recycling. 16(3). 27–33.2 indexed citations
16.
Yoo, Kyoungkeun, et al.. (2006). Application of Sulfate-Reducing Bacteria for Treatment of Mine Drainages. Journal of the Korean Society of Mineral and Energy Resources Engineers. 43(2). 160–167.1 indexed citations
17.
Lee, Jae-chun, et al.. (2006). Solvent Extraction of Platinum Group Metals from the leach Liquor of Spent Automotive Catalyst. Journal of the Korean Institute of Resources Recycling. 15(5). 3–10.3 indexed citations
18.
Ahn, Jae-Woo, et al.. (2005). Biological Leaching of Cu, Al, Zn, Ni, Co, Sn and Pb from Waste Electronic Scrap using Thiobacillus Ferrooxidans. Journal of the Korean Institute of Resources Recycling. 14(1). 17–25.3 indexed citations
19.
Lee, Jae-chun, et al.. (2004). Reconvery of Platinum Group Metals from Spent Automotive Catalysts by Hydrochloric Acid Leaching. Journal of the Korean Institute of Resources Recycling. 13(5). 28–36.3 indexed citations
20.
Kim, Chul-Joo, et al.. (2003). Separation of Neodymium from NdEeB Permanent Magnetic Scrap. Journal of the Korean Institute of Resources Recycling. 12(6). 57–63.11 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.