Modeling of dissolution phenomena in Cu-Cl Cycle for hydrogen production
Bibliographic record
Abstract
The dissolution process of cuprous chloride (CuCl) in aqueous hydrochloric acid (HCl (aq) ) is one of the crucial intermediary steps in the thermochemical water splitting cycle to produce hydrogen. A mass transfer model for dissolution process presented in this paper has been developed based on Noyes Whitney equation which is dependent on concentration gradient across the boundary layer and solute's remaining surface area. The concentration variation and remaining surface area of CuCl in 6 M and 9 M HCl (aq) have been observed with time and mass transfer coefficient has been calculated with and without mixing during the dissolution. The mass transfer coefficient of CuCl dissolution in 6 M HCl without mixing effect has been calculated as 0,29.10 −5 m/s while mass transfer coefficient of dissolution with mixing effect as 1,09.10 −5 m/s. This indicates that mixing the solution can increase the mass transfer rate and reduce the dissolution time. The proposed mass transfer model has been verified with previous experimental data obtained from the literature and exhibited exceptionally good agreement. Further results obtained from the simulation study have been discussed in detail.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".