Young-Jin Oh

1.1k total citations
66 papers, 675 citations indexed

About

Young-Jin Oh is a scholar working on Ecology, Evolution, Behavior and Systematics, Biomedical Engineering and Plant Science. According to data from OpenAlex, Young-Jin Oh has authored 66 papers receiving a total of 675 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Ecology, Evolution, Behavior and Systematics, 23 papers in Biomedical Engineering and 15 papers in Plant Science. Recurrent topics in Young-Jin Oh's work include Agriculture, Soil, Plant Science (29 papers), Plasmonic and Surface Plasmon Research (18 papers) and Advanced biosensing and bioanalysis techniques (8 papers). Young-Jin Oh is often cited by papers focused on Agriculture, Soil, Plant Science (29 papers), Plasmonic and Surface Plasmon Research (18 papers) and Advanced biosensing and bioanalysis techniques (8 papers). Young-Jin Oh collaborates with scholars based in South Korea, Japan and Switzerland. Young-Jin Oh's co-authors include Donghyun Kim, Wonju Lee, Jong‐ryul Choi, Yonghwi Kim, Kyujung Kim, Jingwen Yu, Ho Jae Han, Kyung‐Sun Kang, Chang‐Hun Lee and Kyung Hwan Kim and has published in prestigious journals such as Proceedings of the National Academy of Sciences, ACS Nano and PLoS ONE.

In The Last Decade

Young-Jin Oh

57 papers receiving 629 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Young-Jin Oh South Korea 15 350 219 150 130 93 66 675
Vladimir Popov Russia 11 201 0.6× 154 0.7× 80 0.5× 13 0.1× 56 0.6× 40 640
Sergej Masich Sweden 17 188 0.5× 906 4.1× 32 0.2× 138 1.1× 10 0.1× 22 1.3k
Saurabh Vyawahare United States 9 310 0.9× 205 0.9× 79 0.5× 162 1.2× 9 0.1× 16 683
Ming‐Tzo Wei United States 15 384 1.1× 551 2.5× 14 0.1× 74 0.6× 10 0.1× 35 1.2k
Chandreyee Manas Das Singapore 16 250 0.7× 322 1.5× 138 0.9× 129 1.0× 3 0.0× 31 674
Hangrui Liu Australia 17 427 1.2× 136 0.6× 18 0.1× 205 1.6× 10 0.1× 32 680
Mina Son South Korea 11 161 0.5× 118 0.5× 19 0.1× 14 0.1× 49 0.5× 18 560
Yifan Xia United States 15 122 0.3× 228 1.0× 46 0.3× 48 0.4× 14 0.2× 31 721
Meng Sun China 17 57 0.2× 307 1.4× 42 0.3× 42 0.3× 7 0.1× 61 696
Gabriel Amselem France 11 467 1.3× 121 0.6× 12 0.1× 96 0.7× 8 0.1× 20 675

Countries citing papers authored by Young-Jin Oh

Since Specialization
Citations

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

Fields of papers citing papers by Young-Jin Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young-Jin Oh. 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 Young-Jin Oh. The network helps show where Young-Jin Oh may publish in the future.

Co-authorship network of co-authors of Young-Jin Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Young-Jin Oh. A scholar is included among the top collaborators of Young-Jin Oh 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 Young-Jin Oh. Young-Jin Oh 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.
Mun, Seong K., Young-Jin Oh, & Sanghoon Lee. (2025). Data processing method for evaluating pipe wall thinning in nuclear secondary systems using SVM regression algorithm. Nuclear Engineering and Technology. 57(7). 103517–103517. 1 indexed citations
2.
Oh, Young-Jin, et al.. (2024). Multi-step multivariate forecasting of transmission power in NPPs using operational and meteorological data. Nuclear Engineering and Technology. 57(2). 103169–103169. 1 indexed citations
3.
Choi, Jong‐ryul, et al.. (2020). Machine learning-based design of meta-plasmonic biosensors with negative index metamaterials. Biosensors and Bioelectronics. 164. 112335–112335. 68 indexed citations
4.
Park, Hyung‐Ho, et al.. (2019). ‘Choyoung’, Triticale Cultivar for Forage of Early-Heading, Resistance to Lodging and High Seed Production. Han-guk choji josaryo hakoeji. 39(2). 68–74. 3 indexed citations
5.
Oh, Young-Jin, et al.. (2017). Feed Value and Fermentation Quality of Covered Barley Grain Silage with respect to Days after Heading in Honam Region of Korea. Korean Journal of Crop Science. 62(1). 16–23. 1 indexed citations
6.
Lee, Wonju, Kyungwha Chung, Hongki Lee, et al.. (2017). Molecular overlap with optical near-fields based on plasmonic nanolithography for ultrasensitive label-free detection by light-matter colocalization. Biosensors and Bioelectronics. 96. 89–98. 11 indexed citations
7.
Oh, Young-Jin, et al.. (2017). Evaluation of Feed Value and Fermentation Quality of New Wheat Cultivar, ‘Taejoong’. Han-guk choji josaryo hakoeji. 37(1). 61–67. 2 indexed citations
8.
Park, Jong-Ho, et al.. (2017). High Forage Yielding and Good Silage Quality of a New Barley (Hordeum vulgare L.) Cultivar ‘Dachung’. Han-guk choji josaryo hakoeji. 37(4). 301–307.
9.
Oh, Young-Jin, Jong‐Chul Park, Jong-Ho Park, et al.. (2016). Growth Characteristics and Forage Productivity of New Forage Barley Variety, ‘Miho’. Han-guk choji josaryo hakoeji. 36(4). 370–375. 1 indexed citations
10.
Oh, Young-Jin, et al.. (2015). ‘Nokyang’, Whole Crop Forage Barley Cultivar with the Stay-Green Character, Resistance to Viral Disease and High-Yielding. Han-guk choji josaryo hakoeji. 35(1). 57–62. 1 indexed citations
11.
Choi, Jong‐ryul, Kyujung Kim, Young-Jin Oh, et al.. (2014). Live Cell Imaging: Extraordinary Transmission‐based Plasmonic Nanoarrays for Axially Super‐Resolved Cell Imaging (Advanced Optical Materials 1/2014). Advanced Optical Materials. 2(1). 1–1. 4 indexed citations
12.
Cho, Sang-Kyun, et al.. (2014). Effect of Mixed Seeding between Triticale and Legume crops for Increasing Protein Contents in Forage. Korean Journal of Crop Science. 59(4). 521–525.
13.
Oh, Young-Jin, Wonju Lee, Yonghwi Kim, & Donghyun Kim. (2013). Self-aligned colocalization of 3D plasmonic nanogap arrays for ultra-sensitive surface plasmon resonance detection. Biosensors and Bioelectronics. 51. 401–407. 48 indexed citations
14.
Yu, Hojeong, Young-Jin Oh, Soo-Won Kim, Seok Ho Song, & Donghyun Kim. (2012). Polarization-extinction-based detection of DNA hybridization in situusing a nanoparticle wire-grid polarizer. Optics Letters. 37(18). 3867–3867. 7 indexed citations
15.
16.
Oh, Young-Jin & Bokyung Kim. (2006). A Study of ChongMyungTang(CMT) and HyangbujaChongMyungTang(HCMT) on Dementia - Extract & Nano Powder Drug types. Journal of Oriental Neuropsychiatry. 17(1). 79–105. 3 indexed citations
17.
Kim, Young-Jin, et al.. (2006). Variations of Isoflavone Contents in Seeds and Sprouts of Sprout Soybean Cultivars. The Korean Journal of Crop Science. 51. 160–165. 4 indexed citations
18.
19.
Lee, Jung-Joon, et al.. (2004). Effects of Planting Densities and Maturing Types on Growth and Yield of Soybean in Paddy Field. The Korean Journal of Crop Science. 49(2). 105–109. 3 indexed citations
20.
Oh, Young-Jin, et al.. (2002). Damage Aspect of Bean Bug (Riptortus clavatus) and Selection of Resistant Variety in Soybean. 117–117. 1 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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