Papers
2
Total Citations
81
H-Index
2
About
Juran Noh is a pioneering researcher at the intersection of energy storage and data-driven materials discovery, with a primary focus on redox flow batteries (RFBs). Her work addresses a critical bottleneck in battery technology: the limited solubility of redox-active molecules, which directly constrains energy density. Noh’s major contributions center on generating high-quality experimental solubility datasets and integrating them with artificial intelligence to accelerate materials design. Her landmark 2024 paper, “An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations,” has already garnered 64 citations, showcasing its immediate impact. In this work, she developed a fully automated robotic platform that rapidly screens electrolyte candidates, coupled with an active learning algorithm to iteratively refine predictions. This approach dramatically reduces the time and cost of identifying high-solubility compounds. Her earlier 2023 study, with 17 citations, laid the groundwork by establishing a high-throughput solubility determination method, emphasizing that the lack of large, reliable datasets had hindered machine learning models in RFB research. Together, these works position Noh as a leader in combining experimental automation with computational intelligence, offering a scalable pathway to next-generation energy storage solutions.
Research Focus
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