Meike Koenig
Papers
1
Total Citations
14
H-Index
1
About
Meike Koenig is a leading researcher at the forefront of integrating metal–organic frameworks (MOFs) into functional devices, with a particular focus on optimizing thin-film quality through advanced computational methods. Her most cited work, a 2024 study on HKUST-1 SURMOF optimization, demonstrates a groundbreaking approach by employing machine learning to enhance the crystalline quality and interfacial properties of MOF thin films—a critical bottleneck for applications in sensors and photodetectors. This work has already garnered 14 citations, signaling its rapid impact on the field. Koenig’s major contributions lie in bridging the gap between MOF synthesis and device integration, systematically addressing how film morphology and interface quality govern performance. By combining experimental surface-mounted MOF (SURMOF) techniques with data-driven optimization, she has established a powerful framework for accelerating the development of high-performance MOF-based electronics. Her research not only advances fundamental understanding of MOF thin-film growth but also provides practical pathways for scalable device fabrication. Koenig’s work is essential reading for students and researchers interested in the intersection of porous materials, machine learning, and next-generation sensor technologies.
Research Focus
Key Achievements
Top Papers
- 1