<i>In situ</i> observations of the transition between beryl and phenakite in aqueous solutions using a hydrothermal diamond-anvil cell
Bibliographic record
Abstract
ABSTRACT Beryl and phenakite are important industrial beryllium minerals. In the hydrous melt of the BeO–Al2O3–SiO2–H2O (BASH) system, experiments using quench-type high-temperature and high-pressure equipment have revealed that the different activities of Al2O3 and SiO2 (αAl2O3 and αSiO2) are the main factors that lead to different beryllium mineral assemblages. In this study, we attempted in situ observation of the crystallization process of phenakite and beryl in an aqueous solution of the BASH system using a hydrothermal diamond-anvil cell. Experimental results indicate that phenakite and beryl can crystallize faster in this regime (i.e., 2.93–0.58 × 10−5 cm/s in length and 22.39–3.23 μm3/s in volume) than from a hydrous melt. In addition, in the phenakite and beryl crystallization, pressure–temperature conditions were greater than 467 °C and 220 MPa and 495 °C and 221 MPa, respectively, and their upper temperatures and pressures attained 845–870 °C and 500–1300 MPa. These features indicate that temperature is not the main factor that controls the stability of phenakite and beryl in the BASH system. This stability can be attributed to the diffusion of components in aqueous solution that change αSiO2 and αAl2O3 during the heating and cooling processes. During heating, αSiO2 increases while beryl is dissolving, which leads to phenakite crystallization; during cooling, αSiO2 and αAl2O3 are sufficient for the remaining beryl to recrystallize. Therefore, the transition between phenakite and beryl in the aqueous solution in the BASH system may be different during heating and cooling processes. This reasoning can explain the abundance of phenakite in miarolitic cavities and the occurrence of phenakite, rather than beryl, in hydrothermally altered pegmatites, volcanic rocks, and other beryllium-rich rocks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".