Thermodynamic optimisation of the FeO–Fe<sub>2</sub>O<sub>3</sub>–SiO<sub>2</sub> (Fe–O–Si) system with FactSage
Bibliographic record
Abstract
Abstract Phase equilibrium and thermodynamic experimental data available in the literature on the FeO – Fe2O3 – SiO2 (Fe – O – Si) system were critically reviewed and used to obtain a self-consistent set of parameters for thermodynamic models for all oxide phases using the FactSage computer package. The present optimisation covers the range of oxygen partial pressures from equilibrium with pure oxygen to metal saturation and temperatures from 25 °C to above the liquidus. The present thermodynamic optimisation was performed as part of the development of a thermodynamic database for the multi-component system Al – Ca – Fe – Mg – O – Pb – Si – Zn; the thermodynamic parameters for the Fe – O – Si system therefore were chosen to be consistent not only with the experimental data in this ternary system, but also with the data in higher-order systems. The modified quasichemical model was used for the liquid slag phase. Sublattice (based upon the compound-energy formalism) and polynomial models were used for the spinel (magnetite) and monoxide (wustite) solid solutions, respectively. The use of physically reasonable models means that the models can be used to predict thermodynamic properties and phase equilibria in composition and temperature regions where experimental data are not available. From these model parameters, the optimised ternary phase diagram of the FeO – Fe2O3 – SiO2 (Fe – O – Si) system was back calculated. The database of the model parameters can be used in conjunction with computer software for Gibbs-free-energy minimisation in order to calculate all thermodynamic properties and any type of phase-diagram section in the FeO – Fe2O3 – SiO2 (Fe – O – Si) system.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".