Imaging Atomic Structure, Strain, and Disorder By Atomic Electron Tomography
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».