Thermodynamic modelling of decomposition processes in the Mn-O and Mn-O-H systems
Bibliographic record
Abstract
An understanding of the decomposition processes in the Mn-O and the Mn-O-H systems is important for several applications such as the reduction of manganese ores or the production of pure manganese oxides. Precise thermodynamic data is required for modelling these thermal processes and in particular data is lacking for Mn5O8. It would be advantageous to more accurately model these processes, but this depends on the availability of reliable thermodynamic data. In the present research, the currently available data for the Mn-O system was evaluated in order to obtain consistent decomposition temperatures of the various manganese oxides, as calculated using the Equilibrium Composition program of HSC Chemistry® 7.1. Subsequently, these temperatures were compared to the experimentally determined values in the literature. Also, the effects of variables such as atmospheric composition and pressure on the temperatures were evaluated. Additionally, the thermal conditions required for the formation of Mn5O8, were delineated. The thermodynamic data utilized for Mn5O8 were as follows: ΔH° = −2444.5 kJ/mol, ΔS° = 280.2 J/(mol·K) and Cp(T)=227.8+182.2×10−3T−16.7×105T−2−60.4×10−6T2J/(mol·K). The selected data utilized in this research, can be used for accurate thermodynamic calculations and as examples, phase stability diagrams for the Mn-O, Mn-O-H and Mn-O-N systems were determined.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".