Method of developing Thermo–Kinetic diagrams: The Cu–H2O–acetate and the Cu-H2O systems
Bibliographic record
Abstract
A method to develop thermo-kinetic (TK) diagrams for the Cu−H2O−acetate and Cu−H2O systems is described. Conventional Eh-pH diagrams, also known as Pourbaix diagrams, are developed based on the thermodynamic stability of component species, typically in aqueous media. TK diagrams are an improvement on Eh-pH diagrams as they also describe the kinetics of electrochemical processes. These diagrams are developed by using data from linear scan voltammetry of Cu rotating disk electrodes exposed to aqueous media of different pH. By applying the same procedure, the TK diagrams can be developed for other metals or mineral systems exposed to aqueous media containing ligands. To ensure reproducibility and reconstruction of the TK diagrams for other metal/mineral/electrolyte systems, some important experimental considerations are highlighted in this study. These TK diagrams are useful to evaluate the corrosion of metals, the leaching performance of minerals and to predict the suitable conditions for metal recycling processes. Briefly, this article explains: Important experimental considerations that could affect the kinetics of electrochemical processes. A method to construct TK diagrams with examples of the Cu−H2O−acetate and Cu−H2O systems. With overlaid Eh-pH diagrams, TK diagrams explain both the thermodynamic stability of component species and the kinetics of the electrochemical processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".