KAIST Identifies the Cause of Battery Ion-Transport “False Signals” as Surface Roughness
TECHWORLD ·
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KAIST announced on the 7th that it had identified the cause of measurement errors in battery material analysis. The research team said battery nanoanalysis signals may have come not from actual ion movement but from the uneven surface structure of the material. To reduce errors, it proposed surface flattening, and said the increase in ESM signals at grain boundary disappeared after surface processing.
Battery performance and lifespan are affected by how smoothly lithium or sodium ions move during repeated charging and discharging. As a result, analysis that accurately identifies where ions actually move and where they are blocked is becoming more important.
In this regard, KAIST announced on the 7th that it had identified the cause of measurement errors in battery material analysis. The study revealed why measurement errors that could be mistaken for actual ion movement arise during battery nanoanalysis.
The lead research team was headed by Professor Hong Seung-beom of the Department of Materials Science and Engineering, with Professor Yuk Jong-min’s team from the same department and Professor Choi Nam-soon’s team from the Department of Biological and Chemical Engineering participating as co-researchers. The researchers suggested that some signals observed in battery nanoanalysis had been interpreted as ion movement inside batteries, but may actually have originated from the rough surface structure of the material.
The team also proposed surface flattening as a way to reduce such errors. This study is significant in that, in efforts to determine whether signals observed in nanoanalysis reflect actual ion movement, it showed that some of them may in fact be measurement errors caused by surface roughness.
The team focused on atomic force microscopy (AFM)-based electrochemical strain microscopy (ESM), an analysis technique that indirectly measures ion motion in battery materials. ESM uses a fine probe to measure the surface of battery materials and indirectly analyze ion movement by detecting tiny changes in the material caused by ion transport.
The key finding of the study, however, was that if a material’s surface is uneven, similar signals can appear even without actual ion movement. The cause was that the degree of contact between the microscope probe and the sample changes depending on surface height, and as a result, measurement signals similar to those of ion movement can appear.
The research team conducted experiments to identify the source of this “false signal.” The test subject was a single-crystal silicon surface with no ion movement, on which they intentionally formed tiny grooves. The experiment was carried out under conditions in which ion-movement variables were excluded and only surface roughness remained, allowing the effect of surface roughness alone to be isolated and examined.
In the experiment, changes in surface height led to changes in the contact state between the microscope probe and the sample, confirming that signals similar to those from actual ion movement could be generated in the process. This phenomenon was numerically verified through quantitative analysis.
The same phenomenon was then confirmed in actual battery materials. In graphite anodes and sodium solid electrolytes (Na₂Zn₂TeO₆), which were the materials analyzed, changes in ESM signals according to surface morphology were observed.
The results show that consideration of signal distortion caused by surface topography is necessary in nano-level analysis of various battery materials. In other words, this issue was shown to be a factor that should be considered across battery materials, not one limited to a specific material.
Accordingly, the team proposed maximizing the flattening of battery material surfaces as a way to reduce measurement errors. The method uses an argon (Ar) ion beam, and the equipment used is the Cooling Cross-Section Polisher (CCP). Through this, precision processing of the sample cross-section was carried out.
Because argon reacts little, if at all, with other substances, it was used for precision surface processing without significantly changing the sample’s properties.
As a result, surface roughness was greatly reduced, and measurement errors caused by surface irregularities were also effectively reduced. The team focused on signals from grain boundary, a small crystal boundary in battery materials, and before surface flattening, strong ESM signals were observed at grain boundary, but after surface processing, the increase in ESM signals at grain boundary disappeared.
The team expects this analysis method to be used to study the operating principles of next-generation battery materials, including lithium-ion batteries, all-solid-state batteries that use solid electrolytes, and sodium-ion batteries that use sodium ions instead of lithium.
It was suggested that once highly accurate nanoanalysis data are available, they can be used as highly reliable training data for research on next-generation battery material design and performance prediction based on AI and machine learning.
In this study, Professor Hong Seung-beom said that the team identified how surface height affects measurement results in battery material nanoanalysis, and expects this to contribute to more accurate understanding of ion movement inside batteries. He also said he expects it to contribute to understanding and designing next-generation battery materials.
Source: TECHWORLD · Park Kyu-chan
Original: https://www.epnc.co.kr/news/articleView.html?idxno=406575
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Source: TECHWORLD
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