Abstract
Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision [1]. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics.
| Original language | American English |
|---|---|
| Number of pages | 3 |
| DOIs | |
| State | Published - 2025 |
| Event | Microscopy and Microanalysis 2025 - Salt Lake City, UT Duration: 27 Jul 2025 → 31 Jul 2025 |
Conference
| Conference | Microscopy and Microanalysis 2025 |
|---|---|
| City | Salt Lake City, UT |
| Period | 27/07/25 → 31/07/25 |
NLR Publication Number
- NLR/CP-5K00-93231
Keywords
- automation
- cross-sectioning
- focused ion beam
- machine learning
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