研究目的
To demonstrate how multivariate analysis can improve the signal recovery from fragile materials and unravel elemental composition at the nanoscale in perovskite films.
研究成果
Multivariate statistical methods can assist in minimising the electron dose for efficient elemental mapping and in identifying different chemical phases. Efficient data processing can be performed employing PCA for data denoising, and NMF for determining the different compounds and phases present in a specific material.
研究不足
The electron dose during STEM analysis needs to be minimised to prevent local damage. The signal-to-noise ratio is limited by the onset of degradation.
1：Experimental Design and Method Selection:
The study combines the acquisition of high resolution chemical maps by scanning transmission electron microscopy (STEM) with dedicated MultiVariate Analysis (MVA) methods.
2：Sample Selection and Data Sources:
A cross-sectional sample is extracted from a solar cell using conventional Focused Ion Beam (FIB) preparation and transferred to a (scanning) transmission electron microscope (STEM).
3：List of Experimental Equipment and Materials:
STEM-EDX (Energy Dispersive X-ray spectroscopy) analysis is carried out using optimised illumination conditions.
4：Experimental Procedures and Operational Workflow:
STEM-EDX analysis is carried out with dwell time of 100 ms and spatial sampling every 10 nm, using a 600 pA current in a
5：5 nm probe. Data Analysis Methods:
The experimental data is processed using MVA algorithms available in open source scientific analysis packages such as python-based Hyperspy.
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