Partial Molar Volumes in Highly Siliceous Melts and the Relationship to Liquid Immiscibility
Bibliographic record
Abstract
Partial molar volumes ( V ¯ ) of SiO 2 , K 2 O, Na 2 O, Li 2 O and BaO have been re-evaluated in binary silicate melts at 1673 K. Volumetrically, the SiO 2 component mixes ideally in K 2 O-SiO 2 melts but mixes non-ideally in Li, Na and Ba melts, with V ¯ SiO2 displaying maxima between ~80–95 mole% SiO 2 . K 2 O partial molar volumes ( V ¯ K2O ) display weak, non-ideal behaviour in K 2 O-SiO 2 melts due to electrostriction, where tetrahedra collapse around the modifier cation, K + , in response to K-O Coulombic attraction. V ¯ Na2O , V ¯ Li2O and V ¯ BaO also behave non-ideally in their respective binary melts due to electrostriction. The combined effects of non-ideal mixing of SiO 2 and electrostriction associated with the modifier cations result in molar volumes of the four melts being less than expected for ideal mixing. The extent of non-ideal volumetric mixing in the binary melts increases in the order K non-ideal volumetric mixing results from the same chemical interactions that give rise to melt immiscibility and that these interactions are due primarily to non-ideal behaviour of the SiO 2 component. The non-ideal volumetric mixing behaviour required use of quadratic expressions to fit molar volume-compositional trends of the four melt systems studied. Although mixing is non-ideal, the partial molar volumes of SiO 2 and modifier oxides are remarkably similar to values obtained from linear mixing models for melts containing ~45–70 wt.% SiO 2 . Pronounced effects of non-ideal mixing are mostly restricted to highly siliceous melts (X SiO2 >0.75) where V ¯ SiO2 values are appreciably greater than the molar volume of liquid SiO 2 ( V° SiO2 ), which is ~26.75 cm 3 /mole at 1673 K. The findings are consistent with volumetric (density) studies of highly siliceous (haplogranitic) melts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".