Korea Advanced Institute of Science and Technology (KAIST) has developed a method to eliminate false signals in nanoscale battery analysis, revealing that surface roughness can mislead measurements of ion movement. The research, published in Small Methods, introduces a polishing technique to distinguish true ion pathways from artifacts caused by material topography.
The study was conducted by a multi-departmental research team led by Professors Seungbum Hong, Jong Min Yuk, and Nam-Soon Choi at KAIST. The work, published in Small Methods, demonstrates how surface roughness in battery materials can generate misleading signals that mimic ion movement, complicating the development of next-generation batteries. According to the research, uneven surface topography causes contact stiffness fluctuations, feedback loop delays in Dual AC Resonance Tracking ESM (DART-ESM), and micro-grooves at grain boundaries, which produce false signals. These artifacts are not indicative of actual ion transport but rather result from mechanical contact variations and surface morphology.
KAIST identifies cause of artifacts in battery nanoscale analysis
To isolate topographic effects from actual ionic transport, the team etched fine trenches into an ionically inactive single-crystal silicon substrate. Scans of this non-conductive, ion-free material confirmed that height variations alone generated artificial ESM signals identical to those observed in active battery components. Similar interference was detected in graphite anodes and the sodium solid electrolyte Na2Zn2TeO6, the researchers reported. The study was announced on the 7th by KAIST, highlighting its potential to improve the accuracy of nanoscale battery analysis.
The team proposed a cryogenic cross-section polishing (CCP) method using an argon ion beam to smooth sample surfaces. This technique eliminates surface roughness without altering the material’s chemical or structural properties. After treatment, surface roughness dropped sharply, and measurement errors caused by uneven surfaces were effectively reduced. The researchers emphasized that polished surfaces significantly reduce measurement errors, stating that the method is critical for solid-state and sodium-ion batteries, where precise ion pathway analysis is essential for performance optimization.
Professor Hong Seung-beom, a lead researcher, noted that the study clearly established how surface height variations affect measurement results when battery materials are analyzed at the nanoscale. We expect it to contribute to understanding and designing the operating principles of next-generation battery materials by tracking ion movement inside batteries more accurately,
he said. Professor Choi Nam-sun added, This can serve as important baseline information for designing battery materials that let ions move smoothly, in order to speed up charging and extend lifespan.
KAIST develops nano-analysis method to eliminate false battery signals
The findings have broader implications for battery research and artificial intelligence (AI) modelling. By eliminating false signals, the KAIST method provides reliable data for training machine learning algorithms used to discover new materials and predict degradation. This is particularly valuable for sodium-ion and solid-state batteries, which require precise ion transport analysis to achieve commercial viability. Jin Dong-young, a doctoral researcher involved in the study, emphasized, We expect it will help more accurately identify where ions inside a battery move well and where they are blocked.
The research team tested single-crystal silicon substrates with fine trenches to isolate topographic effects. Scans confirmed that surface height variations alone generated artificial ESM signals. The team also detected false signals in actual battery materials, including graphite anodes and the sodium solid electrolyte Na2Zn2TeO6. The experiments quantitatively confirmed that changes in material surface height alter the degree of contact between the microscope probe and the sample, generating signals similar to ion transport.
SIMS Surface Analysis
The cryogenic cross-section polisher used in the study finely trims a sample’s cross-section with an argon (Ar) ion beam. Argon, which barely reacts chemically with other substances, is used to process surfaces precisely without significantly altering the sample’s properties. After the cross-section was trimmed, surface roughness dropped sharply, and measurement errors caused by uneven surfaces were effectively reduced. The study’s results were published in Small Methods, with a schematic illustrating the trace–retrace discrepancy observed in DART-ESM measurements (Figure 1).
The research team included Professors Hong Seung-beom from the Department of Materials Science and Engineering, Yuk Jong-min from the same department, and Choi Nam-soon from the Department of Chemical and Biomolecular Engineering. Their work was supported by KAIST, which highlighted the potential of the study to improve the reliability of research into next-generation battery materials. The findings are expected to enhance the accuracy of surface analysis techniques.
KAIST’s research image, generated using AI, was published with the study. The image, courtesy of KAIST, illustrates the impact of surface topography on ESM measurements. The study’s implications extend beyond battery development, offering insights into nanoscale material analysis and the importance of surface preparation in electrochemical studies. By addressing the root causes of false signals, the KAIST team has provided a critical tool for advancing the design and performance of next-generation energy storage systems.
