Serin Lee

All Publications

• 5D-STEM of energy materials: Recording real and reciprocal space dynamics via <i>in situ</i> 4D-STEM MRS COMMUNICATIONS Lee, S., Ophus, C. 2026

  View details for DOI 10.1557/s43579-026-01009-7

  View details for Web of Science ID 001819458100001

• Unsupervised Segmentation and Clustering Workflow for Efficient Processing of 4D-STEM and 5D-STEM Data. Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada Lee, S., Ribet, S. M., McCray, A. R., Barnum, A., Dionne, J. A., Ophus, C. 2026; 32 (3)

  Abstract

  Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure-strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.

  View details for DOI 10.1093/mam/ozag044

  View details for PubMedID 42242903

• Integrative Approaches to Reveal Catalyst Dynamics: Bridging Operando Techniques, Theory, and Artificial Intelligence. ACS nano Lee, T. H., Lee, S., Yuan, L., Dionne, J. A., Park, J. 2025

  Abstract

  Catalysts operate under complex conditions that require sophisticated approaches to understand their dynamics. This perspective outlines advances in experimental operando techniques, theoretical approaches, and machine learning (ML)-based data analysis to elucidate catalyst dynamics and improve the next-generation catalyst design. We first survey operando techniques, spanning electron microscopy, X-ray spectroscopy, and vibrational spectroscopy, that capture catalyst dynamics under operating conditions. We then discuss how operando observations integrate with and inform theoretical models, creating an iterative feedback loop between experiment and computation. Finally, we highlight how advanced data analysis, especially ML, enables the interpretation of high-dimensional operando data sets and can even inform catalyst design. Together, these synergetic approaches provide a unified framework for probing catalyst function and accelerating the rational design of efficient, durable catalytic systems for sustainable chemical manufacturing.

  View details for DOI 10.1021/acsnano.5c10976

  View details for PubMedID 41099495

• Patterned Electrochemical Deposition through Local Heating of Electrodes JOURNAL OF THE ELECTROCHEMICAL SOCIETY Lee, S., Fleuren, N. F. J., Pinkowitz, A., Ross, F. M. 2025; 172 (2)

  View details for DOI 10.1149/1945-7111/adb45a

  View details for Web of Science ID 001427551400001