Geoscience at the nanometre scale: review of analytical transmission electron microscopy applications
Bibliographic record
Abstract
This paper describes applications of analytical Transmission Electron Microscopy (TEM) in the geosciences. The topics include: 1) sulphide-mineral oxidation; 2) trace-metal attenuation by secondary Mn oxides; 3) silicate weathering; 4) transition-metal valence in minerals; and 5) secondary Hg minerals in stream sediments. The main advantage of the analytical TEM is the ability to obtain images, chemical information, and electron diffraction patterns at the nanometre scale. With such high spatial resolution, it is possible to observe physical and chemical features in samples that cannot be resolved with most other techniques. This information can lead to significant improvement in our understanding of the system under investigation. Sample preparation techniques that are used in each study are also described in this paper. The preparation of samples for TEM analysis can be challenging because of the heterogeneity commonly encountered in geological materials, the fragility of some geological samples (e.g., low-temperature minerals), and the need to maintain spatial relationships present in the samples. The sample preparation techniques presented are specific to the needs of the study and the appropriateness of these methods is demonstrated by the high quality analytical TEM data that are obtained. RÉSUMÉ Cet exposé décrit des applications de la microscopie électronique à transmission analytique dans les sciences de la terre. Les aspects étudiés comprennent : 1) l'oxydation des minéraux sulfurés; 2) l'atténuation des métaux-traces par des oxydes de Mn secondaires; 3) la silicatisation météorique; 4) la valence des métaux de transition dans les minéraux; et 5) les minéraux de Hg secondaires dans les sédiments fluviatiles. Le principal avantage qu'offre la MET analytique est la possibilité d'obtenir des images, des données chimiques et des figures de diffraction des électrons à l'échelle nanométrique. Une résolution spatiale aussi élevée permet l'observation dans les échantillons de propriétés physiques et chimiques impossibles à éclaircir au moyen de la majorité des autres techniques. De tels renseignements peuvent mener à une amélioration marquée de notre compréhension du système à l'étude. Cet exposé décrit en plus les techniques de préparation des échantillons utilisées lors de chaque étude. La préparation des échantillons à une analyse MET peut s'avérer compliquée en raison de l'hétérogénéité que présentent communément les matières géologiques, de la fragilité de certains échantillons géologiques (p. ex. minéraux à basse température) et de la nécessité de maintenir les liens spatiaux présents dans les échantillons. Les techniques de préparation des échantillons présentées sont propres aux besoins de l'étude; les données de haute qualité obtenues des analyses MET témoignent de la pertinence de ces méthodes. [Traduit par la rédaction]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".