Dislocation nucleation at nanoscale fluid inclusions: Direct observation from atom probe tomography data of naturally deformed pyrite
Bibliographic record
Abstract
<p>In recent years, increasing developments in microscopy and microanalysis have allowed for the direct observation of nanoscale crystalline defects (i.e. dislocations). These defects are particularly important in naturally deformed materials yet this avenue of research remains understudied within the Earth Sciences. Dislocations can now be documented through the use of new and innovative structural and chemical analytical techniques such as electron channeling contrast imaging (ECCI), transmission electron microscopy, and atom probe tomography (APT). The presence and migration of dislocations in crystalline materials, including their role in trace element mobility, play a vital function in the way these materials respond to an applied stress. However, the mechanisms by which dislocations nucleate in minerals remain poorly understood. Prevailing models for dislocation nucleation include generation by Frank-Read sources, stress localization at crack-tips, atomic segregation, and free surface nucleation by critical stress-gradient criterion. Based on recent APT data from naturally-deformed pyrite, combined with electron backscatter diffraction (EBSD) mapping and ECC imaging, we propose a new nucleation mechanism where dislocations are generated by the local stress field in the vicinity of fluid inclusions. The investigated sample consists of a polycrystalline pyrite aggregate within a black shale host rock that has witnessed a peak temperature of 300°C. The combined EBSD and ECCI results reveal crystal plasticity in the form of lattice misorientation up to 8.5° and low-angle grain boundary development. APT data reveals nanoscale fluid inclusions enriched in As, O (H<sub>2</sub>O), Na and K as well as As- and Co-rich dislocations linked by fluid inclusions. This new model is the first documentation with APT methods of fluid inclusions (voids) in minerals, nanoscale features that are commonly misinterpreted as element clusters or chemically-enriched crystal-defects. The combined data has significant trans-disciplinary implications to the geosciences (structural geology, geochemistry, economic geology, geochronology), the material sciences (metals, ceramics, polymers), and analytical microscopy. Within geochronology voids and dislocations such as these in dated minerals may host elements or isotopes that negatively affect their age. Within ore deposit geology, voids in precious metal-hosting minerals may act as the necessary traps to structurally prevent the metals (gold, silver, copper) from migrating or diffusing out of the host mineral. In material sciences, the presence of such crystalline features can either limit or enhance the performance of engineering materials. Thus, performing APT analysis on crystalline material can help us better understand and predict their physical properties.</p>
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".