Determination and Modeling of the Solubility of Na<sub>2</sub>SiO<sub>3</sub>·9H<sub>2</sub>O in the NaCl–KCl–H<sub>2</sub>O System
Bibliographic record
Abstract
The solubilities of sodium metasilicate nonahydrate (Na 2 SiO 3 ·9H 2 O) in the NaCl–H 2 O, KCl–H 2 O, and NaCl–KCl–H 2 O systems were determined at the temperature range from (288.15 to 308.15) K using a dynamic method. The results show that the solubility of Na 2 SiO 3 ·9H 2 O decreases first and then levels off with increasing concentration of NaCl from (0.17 to 4.50) mol·kg –1, whereas the solubility in the Na 2 SiO 3 ·9H 2 O(s)-saturated KCl solution increases slightly with the addition of KCl in the concentration range from (0.20 to 3.10) mol·kg –1 . The Na 2 SiO 3 ·9H 2 O solubilities in all cases investigated were found to increase with the temperature increment. A new chemical model for the solubility has been established by the regression of the experimental solubilities data in the NaCl–H 2 O system to obtain the parameters of the Bromley–Zemaitis model. These newly obtained model parameters were applied to well predict the Na 2 SiO 3 ·9H 2 O solubility in two other systems, namely, the KCl–H 2 O and the NaCl–KCl–H 2 O systems, without parametrization with an average relative deviation of 1.4 % and 4.1 %, respectively.
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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 | 0.000 |
| 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 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".