Practical Electron Microscopy and Database

An Online Book, Second Edition by Dr. Yougui Liao (2006)

Practical Electron Microscopy and Database - An Online Book

Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

Crystalline and Semicrystalline Orientation Mapping with Four-dimensional (4D) STEM

Crystalline and Semicrystalline Orientation Mapping is a vital application of 4D-STEM, enabling precise measurement of crystal orientations within various materials. Traditional methods like Electron Backscatter Diffraction (EBSD) are widely used in SEM for mapping orientation distributions in crystalline materials, though they lack the spatial resolution achievable in TEM. Orientation mapping in TEM is typically accomplished by analyzing Kikuchi patterns or by indexing Bragg disks, with Kikuchi patterns offering higher orientation precision, particularly in thicker samples. Early implementations required real-time indexing due to memory limitations, but advancements now allow for comprehensive post-acquisition analysis of 4D-STEM datasets. Various applications include mapping grain orientations in nanocrystalline materials and in situ mechanical testing. More advanced approaches, such as automated crystal orientation mapping (ACOM) combined with precession electron diffraction (PED), improve orientation precision by reducing dynamical effects. In addition, orientation mapping extends to semicrystalline materials, polymers, and biological samples, demonstrating the versatility of 4D-STEM in characterizing complex structures at nanoscale resolutions.

Figure 0008 presents a nanoscale mapping of lattice reorientation within peptide nanocrystals using 4DSTEM imaging. The high-angle annular dark field (HAADF) image shows a peptide nanocrystal, while Figure 0008 (b) depicts a map of diffraction clusters derived from unsupervised classification of diffraction patterns. These clusters reveal nanoscale variations in lattice orientation that are not readily apparent in the HAADF image alone. The colors in Figure 0008 (b) indicate different lattice orientations relative to the mean orientation, as shown in the inset color wheel, where the maximum observed tilt reaches 4°. Figure 0008 (c) displays average diffraction patterns for each cluster, with colors corresponding to their respective regions in the map. This figure effectively highlights how subtle lattice reorientations within the nanocrystal can be visualized and categorized, offering insight into nanoscale structural heterogeneity in peptide assemblies. In this analysis, electron diffraction patterns are sorted by k-means clustering (see page8).

Mapping nanoscale lattice reorientation within peptide nanocrystals using 4DSTEM. (a) HAADF image of a QYNNQNNFV nanocrystal showing the overall crystal structure. (b) Map of diffraction pattern clusters, with each color representing regions of the crystal exhibiting distinct lattice orientations relative to the mean. The color wheel inset indicates the angular deviation of each cluster from the average orientation in the x and y directions, with a maximum deviation of 4°. (c) Representative diffraction patterns from clusters outlined in (b), with bounding box colors corresponding to each cluster. Labels 'C,' 'X,' and 'V' denote the carbon support, peptide crystal, and vacuum, respectively

Figure 0008. Mapping nanoscale lattice reorientation within peptide nanocrystals using 4DSTEM. (a) HAADF image of a QYNNQNNFV nanocrystal showing the overall crystal structure. (b) Map of diffraction pattern clusters, with each color representing regions of the crystal exhibiting distinct lattice orientations relative to the mean. The color wheel inset indicates the angular deviation of each cluster from the average orientation in the x and y directions, with a maximum deviation of 4°. (c) Representative diffraction patterns from clusters outlined in (b), with bounding box colors corresponding to each cluster. Labels 'C,' 'X,' and 'V' denote the carbon support, peptide crystal, and vacuum, respectively. [1]

The advantages and disadvantages of crystalline and semicrystalline orientation mapping in 4D-STEM are:

  • Advantages:
    • High-Resolution Orientation Maps: Orientation mapping using 4D-STEM allows for high-resolution orientation maps of crystalline materials, which is valuable for studying complex microstructures.
    • Detailed Grain Mapping: It enables detailed mapping of grain orientations in both crystalline and semicrystalline materials, aiding in the analysis of structural and mechanical properties at fine scales.
    • Automated Crystal Orientation Mapping (ACOM): Using computer processing, the ACOM method can automate the classification of crystal orientations, making the process efficient and suitable for large datasets.
    • Flexibility for Diverse Materials: This method can handle a wide range of materials, including metals, ceramics, and soft materials like polymers, enhancing its application scope in materials science.
    • Quantitative Orientation Data: The approach can generate quantitative data on crystal orientations and their distributions, which is essential for understanding material properties such as texture and anisotropy.
  • Disadvantages:
    • Computational Intensity: Orientation mapping requires substantial computational resources, especially with large 4D-STEM datasets, making it time- and resource-intensive.
    • Requirement for Thin Samples: For accurate orientation mapping, samples often need to be thin enough to avoid complications from multiple scattering, which can limit the method’s applicability to thicker samples.
    • Potential for Error in High Deformation Regions: In regions of high local deformation, Kikuchi pattern-based orientation mapping may struggle with accuracy, as the patterns can become delocalized and harder to interpret.
    • Dependency on Instrument Stability: High-resolution orientation mapping relies on stable TEM instrumentation, particularly for long data acquisition sessions, making it sensitive to drift and other instabilities during measurement.
    • Limited Resolution for Semicrystalline Materials: While the method works for semicrystalline materials, the resolution and precision are often lower than those for fully crystalline materials due to the less defined structural order.

 

 

 

 

 

 

 

 

 

 

 

[1] Gallagher-Jones M, Ophus C, Bustillo KC, Boyer DR, Panova O, Glynn C, Zee C-T, Ciston J, Mancia KC, Minor AM & Rodriguez JA (2019). Nanoscale mosaicity revealed in peptide microcrystals by scanning electron nanodiffraction. Commun Biol 2, 26.

 

 

Contact the Book Author | About the Book Author | Join EM Group Chats

Citation of the Online Book Listed on Google Scholar