Jonggeol Na

3.5k total citations · 1 hit paper
84 papers, 2.8k citations indexed

About

Jonggeol Na is a scholar working on Mechanical Engineering, Control and Systems Engineering and Catalysis. According to data from OpenAlex, Jonggeol Na has authored 84 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Mechanical Engineering, 24 papers in Control and Systems Engineering and 23 papers in Catalysis. Recurrent topics in Jonggeol Na's work include CO2 Reduction Techniques and Catalysts (15 papers), Process Optimization and Integration (14 papers) and Catalysts for Methane Reforming (12 papers). Jonggeol Na is often cited by papers focused on CO2 Reduction Techniques and Catalysts (15 papers), Process Optimization and Integration (14 papers) and Catalysts for Methane Reforming (12 papers). Jonggeol Na collaborates with scholars based in South Korea, United States and Australia. Jonggeol Na's co-authors include Ung Lee, Yun Jeong Hwang, Chan Woo Lee, Si Young Lee, Young Jin, Chonghun Han, Jay Liu, Hyung‐Suk Oh, Won Bo Lee and Dong Ki Lee and has published in prestigious journals such as Chemical Society Reviews, Nature Communications and Renewable and Sustainable Energy Reviews.

In The Last Decade

Jonggeol Na

80 papers receiving 2.7k citations

Hit Papers

align trajectories

What are hit papers?

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.

Catalyst–electrolyte interface chemistry for electrochemical CO₂ reduction

2020 367 citations Young Jin, Jonggeol Na et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jonggeol Na South Korea	25	1.2k	732	706	489	467	84	2.8k
Chang He China	24	508 0.4×	304 0.4×	461 0.7×	444 0.9×	313 0.7×	126	2.1k
Luis Ricardez‐Sandoval Canada	41	1.3k 1.0×	665 0.9×	974 1.4×	1.0k 2.1×	1.6k 3.5×	248	5.7k
Harvey Arellano‐García Germany	30	326 0.3×	1.1k 1.4×	199 0.3×	480 1.0×	1.1k 2.3×	143	2.8k
Muhammad Abdul Qyyum South Korea	39	632 0.5×	498 0.7×	471 0.7×	864 1.8×	488 1.0×	150	4.0k
Stefan Pischinger Germany	42	484 0.4×	644 0.9×	1.4k 2.0×	2.0k 4.1×	1.8k 3.8×	544	7.2k
Debangsu Bhattacharyya United States	29	421 0.3×	399 0.5×	705 1.0×	851 1.7×	592 1.3×	148	2.8k
M. M. Faruque Hasan United States	32	293 0.2×	374 0.5×	343 0.5×	573 1.2×	399 0.9×	83	3.0k
Jean‐Marc Commenge France	25	264 0.2×	358 0.5×	416 0.6×	1.1k 2.2×	561 1.2×	53	2.2k
Yongrong Yang China	38	435 0.4×	477 0.7×	641 0.9×	1.6k 3.2×	1.3k 2.8×	385	6.0k
Hao Yan China	30	642 0.5×	568 0.8×	274 0.4×	771 1.6×	1.3k 2.7×	150	2.9k

Countries citing papers authored by Jonggeol Na

Since Specialization

Citations

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

Fields of papers citing papers by Jonggeol Na

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonggeol Na

This figure shows the co-authorship network connecting the top 25 collaborators of Jonggeol Na. A scholar is included among the top collaborators of Jonggeol Na 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 Jonggeol Na. Jonggeol Na is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shah, Parth, et al.. (2025). Physics-informed neural network with moving boundary constraints for modeling hydraulic fracturing. Computers & Chemical Engineering. 203. 109308–109308.

Choi, Inyoung, et al.. (2025). Accelerated Discovery of Solvation Structure Engineering for Stable Aqueous Rechargeable Zinc Batteries via Physics‐Guided Bayesian Active Learning. Small. 21(23). e2411632–e2411632. 1 indexed citations

Shin, Daeun Chloe, Chang Soo Kim, Kyeongsu Kim, et al.. (2025). Discovering the Origin of Catalyst Performance and Degradation of Electrochemical CO₂ Reduction through Interpretable Machine Learning. ACS Catalysis. 15(3). 2158–2170. 7 indexed citations

Ifaei, Pouya, et al.. (2024). The AI circular hydrogen economist: Hydrogen supply chain design via hierarchical deep multi-agent reinforcement learning. Chemical Engineering Journal. 497. 154464–154464. 3 indexed citations

Na, Jonggeol, et al.. (2024). Maximizing biomass utilization: An integrated strategy for coproducing multiple chemicals. Journal of Energy Chemistry. 100. 180–191. 14 indexed citations

Ifaei, Pouya, et al.. (2024). Optimal hybrid renewable microgrids via energy demand control using media platforms in South Korea. Sustainable Cities and Society. 118. 106027–106027. 2 indexed citations

Khaligh, Vahid, et al.. (2024). Multi-period hydrogen supply chain planning for advancing hydrogen transition roadmaps. Renewable and Sustainable Energy Reviews. 200. 114536–114536. 20 indexed citations

Lee, Jinhee, Suk Min Kim, Byoung Wook Jeon, et al.. (2024). Molar-scale formate production via enzymatic hydration of industrial off-gases. 1(5). 354–364. 17 indexed citations

Lee, Won Bo, et al.. (2024). Materials discovery with extreme properties via reinforcement learning-guided combinatorial chemistry. Chemical Science. 15(21). 7908–7925. 6 indexed citations

10.

Niaz, Haider, et al.. (2023). Optimal planning of hybrid energy storage systems using curtailed renewable energy through deep reinforcement learning. Energy. 284. 128623–128623. 35 indexed citations

11.

Pilario, Karl Ezra, et al.. (2023). Explainable Artificial Intelligence for Fault Diagnosis of Industrial Processes. IEEE Transactions on Industrial Informatics. 21(1). 4–11. 27 indexed citations

12.

Na, Jonggeol, et al.. (2021). CFD modeling for the prediction of molecular weight distribution in the LDPE autoclave reactor: Effects of non-ideal mixing. Chemical Engineering Journal. 427. 131829–131829. 14 indexed citations

13.

Park, Seongho, Chulwan Lim, Woong Hee Lee, et al.. (2021). Design methodology for mass transfer-enhanced large-scale electrochemical reactor for CO 2 reduction. Chemical Engineering Journal. 424. 130265–130265. 44 indexed citations

14.

Na, Jonggeol, et al.. (2021). Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention. Journal of Chemical Information and Modeling. 61(12). 5804–5814. 42 indexed citations

15.

Hong, Seokyoung, et al.. (2021). Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes. IEEE Transactions on Industrial Informatics. 18(2). 827–834. 91 indexed citations

16.

Kim, Minsu, et al.. (2021). Data-driven robust optimization for minimum nitrogen oxide emission under process uncertainty. Chemical Engineering Journal. 428. 130971–130971. 21 indexed citations

17.

Lee, Yong-Kyu, et al.. (2019). Multicompartment Model of an Ethylene–Vinyl Acetate Autoclave Reactor: A Combined Computational Fluid Dynamics and Polymerization Kinetics Model. Industrial & Engineering Chemistry Research. 58(36). 16459–16471. 16 indexed citations

18.

Lee, Yong-Kyu, Jonggeol Na, & Won Bo Lee. (2018). Robust design of ambient-air vaporizer based on time-series clustering. Computers & Chemical Engineering. 118. 236–247. 12 indexed citations

19.

Na, Jonggeol, et al.. (2018). Toxic gas release modeling for real-time analysis using variational autoencoder with convolutional neural networks. Chemical Engineering Science. 181. 68–78. 47 indexed citations

20.

Lee, Won Je, Jonggeol Na, Kyeongsu Kim, et al.. (2018). NARX modeling for real-time optimization of air and gas compression systems in chemical processes. Computers & Chemical Engineering. 115. 262–274. 20 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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