| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
|
| Presentation Title |
Optimizing Nanoindentation Methods for the High Throughput Study of Combinatorial Thin Film Libraries |
| Author(s) |
Andre Bohn, Adie Alwen, Andrea Maria Hodge |
| On-Site Speaker (Planned) |
Andre Bohn |
| Abstract Scope |
The demand for faster materials development and large datasets to train machine learning models has led to an increased interest in the field of combinatorial and high-throughput (CHT) materials science. Within this field, vapor deposition techniques are often used to synthesize films spanning large composition ranges. Subsequent high-throughput characterization enables the determination of composition-structure-property relationships, which can guide advancements of fundamental science or screen for promising materials. Nanoindentation is a common method for mechanical characterization of these material libraries, as it requires minimal sample preparation and can be readily automated. However, there is currently no standardized protocol for high-throughput nanoindentation and the associated statistical analysis. This work uses a CuNi library as a model-system to establish protocols to optimize the reliability of the data, thus minimizing experiment times and costs. The standardization of methods will increase the efficiency and reproducibility of combinatorial studies, expanding their value to the scientific community. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Mechanical Properties, Other |