Nina Scheuermann
Papers
1
Total Citations
44
H-Index
1
About
Dr. Nina Scheuermann is a pioneering researcher at the intersection of materials chemistry and automation, specializing in the synthesis and optimization of metal–organic frameworks (MOFs). Her work centers on developing robotic platforms and machine learning approaches to accelerate the discovery and fabrication of MOF thin films, a critical step for integrating these porous materials into functional devices. Her most cited work, “Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches” (2022, 44 citations), demonstrates a transformative methodology that combines high-throughput experimentation with intelligent algorithms to rapidly identify optimal growth conditions, dramatically reducing the time and resources traditionally required for materials optimization. This contribution has positioned her at the forefront of autonomous materials discovery, enabling the systematic exploration of structure–property relationships in MOFs. By bridging robotics, data science, and coordination chemistry, Scheuermann’s research not only advances fundamental understanding of thin film growth but also paves the way for scalable, reproducible fabrication of MOF-based sensors, membranes, and electronic devices. Her work exemplifies a new paradigm in materials research, where automation and machine learning drive efficiency and innovation.
Research Focus
Key Achievements
Top Papers
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