Electrochemical Transport in CuCl/HCl(aq) Electrolyzer Cells and Stack of the Cu–Cl Cycle
Bibliographic record
Abstract
This paper develops a comprehensive predictive model for the CuCl/HCl(aq) electrolyzer stack in the electrochemical unit of the Cu–Cl cycle of hydrogen production. A strong aqueous anolyte is fed into the stack and forms complex speciation. The unit single cell is modeled to predict the decomposition voltage by applying the Gibbs energy minimization method (GEM). The kinetic correlations are incorporated to take into account the overpotential losses during the hydrogen generation process under a non-equilibrium condition with the stack under potential. To evaluate the single-cell contribution to the average performance of stack, a hydrodynamic analysis reveals the anolyte and catholyte flow distribution using a finite element method for solutions of the equation of mass and momentum conservation equations of the flow field. Using the simulated stack, the voltage spread across the individual cells in the stack, cell and stack voltage efficiency, and the sensitivity of stack performance under the operating conditions, are investigated. It is shown that the speciation model has good agreement with data in past literature. With an increase in the stack operating temperature from 25 °C to 65 °C, the average stack efficiency improves from 68% to 72%. Cells close to the anolyte or catholyte input ports possess a higher voltage efficiency than other cells. This is mainly due to less electrolyte received by the cells placed in the middle of the stack for the X-shape bipolar modules, resulting in less decomposition potential.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".