Imaging Atomic Structure, Strain, and Disorder By Atomic Electron Tomography
Bibliographic record
Abstract
Knowledge of the atomic structure of materials is critical to understanding their functionality in many fields such as biology, microelectronics, condensed matter, and nanotechnology. Traditionally, x-ray crystallography has been the main method used to solve structures of all types. New techniques such as micro electron diffraction and single particle cryo electron microscopy are now utilizing electrons to study crystals that cannot be crystallized at the scales necessary for x-ray experiments. However, these techniques require ensembles of identical unit cells (like molecules or proteins) to solve the structure from a series of images or diffraction patterns. The need to average over many unit cells means that important material properties such as defects, dopants, and disorder are not captured. Investigations of unique arrangements of atoms requires a direct imaging method that does not rely on structural averaging or the assumption of crystallinity. Transmission electron microscopy (TEM) provides the ability to directly image atomic structure, and the development of powerful, stable aberration correctors in the last decade now routinely provide sub-Angstrom imaging resolution. Scanning TEM (STEM) has also become an effective method for measuring the atomic structure of inorganic materials based on so-called Z-contrast where the image intensities are proportional to the atomic number of the material being imaged. However, by their nature TEMs are limited to producing only two-dimensional projections of a structure. Electron tomography is a technique that can reconstruct the three-dimensional structure of unique nanoscale objects from a series of two-dimensional images acquired at different viewing angles. Tomography was traditionally limited to nanoscale resolution due to experimental difficulties and a lack of high-quality reconstruction algorithms. We have developed atomic electron tomography (AET) by utilizing the sub-Angstrom real-space resolution of STEM with advanced tomographic reconstruction algorithms to solve the structure of materials at the single atom level without averaging or the assumption of crystallinity. This talk will present recent progress and capabilities in the field of AET for reconstructing order and disorder in materials with 20 picometer precision. Our success in resolving chemical order/disorder in metallic FePt nanoparticles was extended to include the time domain by capturing nucleation and growth or ordered phases in the same nanoparticle. Also, the ability to reconstruct atomic structure without the need for averaging or crystallinity led to the achievement of directly imaging three-dimensional atomic arrangements in amorphous solids. We determined the chemical and structural disorder of an eight-element metallic glass to quantitatively characterize short- and medium-range order, and we determined the disordered atomic packing of monatomic amorphous materials. The field of electron microscopy is rapidly changing with the advent of improved detectors, aberration correctors, and monochromators. We are exploring the use of these new hardware to implement new imaging algorithms such as ptychography to allow AET to explore all new classes of materials especially lightly scattering materials like oxygen. We have also established the Materials Databank to share experimentally determined 3D atomic structures with other scientists. As more structures and classes of materials are solved, we expect AET to become an important technique in the field of atomic structural characterization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".