Speciation and Phase Equilibria of Aqueous Boric Acid and Alkali Metal Borates from Ambient to Hydrothermal Conditions: A Comprehensive Thermodynamic Model
Bibliographic record
Abstract
To address the needs for thermodynamic simulation of nuclear power reactor chemistry, geothermal fluid chemistry, and critical materials (Li, B) recovery processes from brines, a comprehensive model has been developed for simultaneous phase equilibrium and speciation calculations. The new model extends a previously developed model for boric acid and selected borates (Wang, P.; Kosinski, J., J.; Lencka, M., M.; Anderko, A.; Springer, R., D. Thermodynamic modeling of boric acid and selected metal borate systems. Pure & Applied Chemistry, 2013, 85, 2117) by utilizing detailed speciation results from recent electrical conductivity measurements in dilute solutions of boric acid and alkali metal borates and quantitative Raman spectroscopic studies at moderate concentrations. For this purpose, the Mixed-Solvent Electrolyte (MSE) framework has been adopted and parametrized for systems containing boric acid, lithium borate, sodium borate, and potassium borate by incorporating the new speciation data together with vapor–liquid equilibrium and solid solubility data. The MSE model combines a treatment of standard-state properties of simple and complex aqueous species with an excess Gibbs energy model that is valid up to solid–liquid saturation or the fused electrolyte limit. This approach ensures the correct prediction of the formation of experimentally identified polyborate species, while reproducing extensive experimental solubility and vapor–liquid equilibrium data. The model has been validated for the B 2 O 3 + H 2 O, Li 2 O + B 2 O 3 + H 2 O, Na 2 O + B 2 O 3 + H 2 O, and K 2 O + B 2 O 3 + H 2 O systems at temperatures up to 623 K at widely varying alkali metal/boron ratios. In particular, the model aligns with the new experimental speciation results to provide reliable predictions under the conditions of pressurized water reactors for nuclear power generation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".