An Experimental Study of Pyrochlore Solubility in Peralkaline Granitic Melts
Bibliographic record
Abstract
Abstract Peralkaline rocks (defined by molar (Na + K)/Al > 1) are typically enriched in Nb and halogens (such as F and Cl). They can further be subdivided into silica-saturated (e.g., alkali granites) and silica-undersaturated (e.g., nepheline syenites). The current study investigates the solubility product (Ksp) of pyrochlore, the most important ore mineral for Nb in peralkaline granites. The Ksp of pyrochlore increases strongly with increasing temperature and with decreasing A/CNK (molar Al2O3/CaO+Na2O+K2O). By contrast, the Ksp of pyrochlore is only weakly dependent on the F content of the melt, if F concentrations are greater than 1 wt %. The Ksp values of pyrochlore from this study are compared to those of columbite from both this study and the literature to evaluate the controls on the crystallization of these two Nb minerals for granites in variable composition. In peralkaline granitic melts with A/CNK < 1, the Ksp values of pyrochlore are lower than those of columbite, but in peraluminous melts with A/CNK > 1, the Ksp values of pyrochlore are higher than those of columbite, and in subaluminous melts, the Ksp values of pyrochlore and columbite are almost the same. Thus, for melts with similar concentrations of essential structural constituents (Ca-Na in the case of pyrochlore and Mn in the case of columbite), the solubility experiments explain why pyrochlore is more common in peralkaline granitic systems, whereas columbite is the main Nb-bearing mineral in peraluminous systems. An expression that describes the dependence of logKsp on temperature and A/CNK was obtained using the experimental results from the F-enriched granitic melts:logKsp=(−5.22±0.50)×(1000⁄T)−(1.91±0.16)×A/CNK+(3.60±0.61)R2=0.97 where temperature (T) is in Kelvin (K). Using this expression, the saturation solubility or the crystallization temperature of pyrochlore can be calculated for the differentiation of peralkaline granitic magmas. This equation was used in conjunction with data from natural melt inclusions to evaluate whether these melts could have been pyrochlore-saturated. In some cases, the melts could not have been pyrochlore-saturated at reasonable temperatures, but in other cases, notably the pegmatite melts at Strange Lake, the concentrations of the essential structural constituents of pyrochlore (i.e., Nb, Ca, Na, F) in the melt inclusions are consistent with magmatic pyrochlore saturation.
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 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 | 0.000 |
| 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) | 0.002 | 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".