Junhong Chen is currently the Crown Family Professor of Molecular Engineering at the Pritzker School of Molecular Engineering, University of Chicago, and serves as Lead Water Strategist at Argonne National Laboratory. He is also the Co-Principal Investigator and Use-Inspired R&D Lead for Great Lakes RENEW, a major NSF Regional Innovation Engine that seeks to catalyze a circular blue economy by recovering critical resources and eliminating harmful contaminants from water—securing domestic supply chains, sustaining water-intensive industries, and safeguarding national security assets.
Prior to joining the University of Chicago, Prof. Chen was a Program Director for the Engineering Research Centers (ERC) program at the U.S. National Science Foundation (NSF). In this role, he co-chaired the NSF-wide ERC Working Group that designed the ERC Planning Grants program and the Gen-4 ERC program, and represented the Engineering Directorate on NSF-wide working groups for the Graduate Research Fellowship Program (GRFP) and Research Traineeship (NRT).
Earlier in his career, Prof. Chen was a Regent Scholar of the University of Wisconsin System and served as Director of the NSF Industry–University Cooperative Research Center (IUCRC) on Water Equipment & Policy (WEP) for six years, fostering strong partnerships with leading water and energy industry stakeholders. He is the founder of NanoAffix Science LLC, a startup focused on commercializing real-time water sensors based on 2D nanomaterials.
Prof. Chen has made seminal contributions to the fields of hybrid nanomaterials and the molecular engineering of sensors and energy devices, with a strong focus on solving real-world challenges in water quality, public health, and sustainable energy. He has authored more than 300 peer-reviewed publications (37,000+ citations, h-index 105) and is recognized as a Highly Cited Researcher (top 1%) in materials science and cross-disciplinary research by Clarivate Analytics. His work has resulted in nine issued U.S. patents, six pending patents, and 15 licensing agreements. Widely regarded as a pioneer in technology translation and commercialization, Prof. Chen has forged exemplary industry partnerships and launched university spin-offs—turning breakthrough research into practical solutions that safeguard water resources, enable cleaner manufacturing, and support a more resilient circular economy.
Prof. Chen received his Ph.D. in Mechanical Engineering from the University of Minnesota in 2002 and was a postdoctoral scholar in Chemical Engineering at the California Institute of Technology from 2002 to 2003. He is an elected Fellow of the National Academy of Inventors (NAI), the Royal Society of Chemistry (RSC), and the American Society of Mechanical Engineers (ASME). His startup NanoAffix was a recipient of the 2016 Wisconsin Innovation Award.
The Junhong Chen Research Group aims to create a meaningful impact on society through scientific discovery and sustainable technological innovation. Our research focuses on the molecular engineering of nanomaterials and nanodevices, with a particular emphasis on hybrid nanomaterials featuring rich interfacial properties and their applications in sensing, water purification, and sustainable energy systems.
We take a multidisciplinary approach, integrating advanced experiments with first-principles calculations to design and discover novel nanomaterials. This approach enables the development of high-performance sensing platforms and energy devices that address critical challenges in public health, environmental stewardship, and energy sustainability.
Real-Time Phosphate Monitoring via Plant-Derived Graphene Ink FET Sensors Integrated with Deep Learning
R. Ghosh, F. X. Zhang, H. J. Jang, J. N. Hui, K. Vittore, H. Y. You, R. Vepa, W. Zhuang, J. W. Elam, S. J. Rowan, D. K. Lee, E. A. Ainsworth, M. C. Hersam, Y. X. Chen, and J. H. Chen*, “Real-Time Phosphate Monitoring via Plant-derived Graphene Ink FET Sensors Integrated with Deep-learning," Accepted to Energy & Environmental Materials, 2025.
Radical-mediated electrical enzyme assay for estradiol: Toward point-of-care diagnostics
H. J. Jang, H. A. Joung, X. A. Shi, R. Ding, J. Wagner, E. T. Tang, W. Zhuang, B. Ryu, G. M. Chen, K. T. J. Yeo, J. Huang, and J. H. Chen*, “Radical-mediated Electrical Enzyme Assay for Estradiol: Toward Point-of-care Diagnostics,” Device. https://doi.org/10.1016/j.device.2025.100807, 2025.
Real-Time Phosphate Monitoring via Plant-Derived Graphene Ink FET Sensors Integrated with Deep Learning
Y. Q. Wang, H. J. Jang, M. Topel, S. Dasetty, Y. N. Liu, M. Ibrahim, A. Tam, V. Rozyyev, E. Ouyang, W. Zhuang, H. H. Pu, S. S. Lee, X. Y. Sui, J. W. Elam, A. L. Ferguson*, S. B. Darling*, and J. H. Chen*, “Reversible ppt-Level Detection of Perfluorooctane Sulfonic Acid in Tap Water using Field-Effect Transistor Sensors," Accepted to Nature Water, 2025.
Green Synthesis of Gold Nanoparticles using American Ginseng and Their Characterization
E. Klatt, B. Lilienkamp, S. Grade, P. Bowman, Y. L. Wang, A. Debruin, K. Schmitt, J. H. Chen, and W. J. Zhang*, “Green Synthesis of Gold Nanoparticles using American Ginseng and Their Characterization," Accepted to Nanofabrication, 2025.
Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts
R. Ding, J. G. Lu, X. B. Wang, X. B. Zhang, M. H. Shao, Y. X. Chen*, and J. H. Chen*, “Synergistic Integration of Domain Knowledge, Data Mining, and Adaptive Learning for Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts: A Comprehensive Workflow Bridging Experiments and Theoretical Insights," Science Advances 11(14). DOI: 10.1126/sciadv.adr9038, 2025.
Advancing transistor-based point-of-care (POC) biosensors: additive manufacturing technologies and device integration strategies for real-life sensing
X. A. Shi, H. H. Pu, L. L. Shi, T. C. He, and J. H. Chen*, “Advancing transistor-based point-of-care (POC) biosensors: additive manufacturing technologies and device integration strategies for real-life sensing," Nanoscale 17(16): 9804-9833. 17(16):9804-9833. https://doi.org/10.1039/D4NR04441J, 2025.
Multiscale simulation and machine learning facilitated design of two-dimensional nanomaterials-based tunnel field-effect transistors: A review
C. I. Tsang, H. H. Pu, and J. H. Chen*, “Multiscale simulation and machine learning facilitated design of two-dimensional nanomaterials-based tunnel field-effect transistors: a review," APL Machine Learning. 3, 016115, https://doi.org/10.1063/5.0240004, 2025. (Front Cover)
Fine Tuning of Electrical Characteristics of Inkjet Printed Graphene for Physical and Chemical Sensing
H. J. Jang, R. Ghosh, W. Zhuang, Y. Q. Wang, X. A. Shi, H. H. Pu, B. Ryu, J. Hui, M. C. Hersam, and J. H. Chen*, “Fine Tuning of Electrical Characteristics of Inkjet Printed Graphene for Physical and Chemical Sensing," ACS AMI 17(8), https://pubs.acs.org/doi/10.1021/acsami.4c21469, 2025.
Exploring the Water-Energy-Food (WEF) Nexus through an Industry Perspective on New Technology
C. Googin, H. H. Pu, and J. H. Chen*, "Exploring the Water-Energy-Food (WEF) Nexus through an Industry Perspective on New Technology," Accepted to NAI Technology & Innovation, 2024.
Selective single-atom adsorption for precision separation of lead ions in tap water via capacitive deionization
Z. W. Gao, L. Q. Wang, X. K. Huang, C. Benmore, H. H. Pu, J. G. Wen, M. K. Y. Chan, and J. H. Chen*, "Computational Modeling-Assisted Selective Single-Atom Adsorption for Precise Lead Removal in Tap Water via Capacitive Deionization," Water Research 268 Part B, 122665, https://doi.org/10.1016/j.watres.2024.122665, 2025.