Electrolytic cement clinker production sustained through orthogonalization of ion vectors
Bibliographic record
Abstract
Electrochemical reactors can reduce the carbon intensity of cement production by using electricity to convert limestone (CaCO3) into Ca(OH)2, which can then be combined with silica (SiO2) at high temperatures to produce cement clinker. A key challenge with this method is the deposition of solid Ca(OH)2 at the reactor membrane leads to unacceptably low energy efficiencies. To address this challenge, we connected the electrochemical reactor (“cement electrolyser”) to a distinctive chemical reactor (“calcium reactor”) so that Ca(OH)2 could form there instead, and not within the electrochemical reactor. In this tandem system, the cement electrolyser generated H+ and OH– in the respective chemical and cathode compartments. The H+ then reacted with CaCO3 to form Ca2+ ions, which were diverted into the calcium reactor to react with the OH– to form Ca(OH)2. We fabricated a composite membrane to selectively block the transport of Ca2+ into the cathode compartment. Charge balance in the cement reactor was enabled with monovalent ions (e.g., K+) as the positive charge carrier. This orthgonalized ion management was validated by operando imaging. The tandem reactor enabled the electrolysis process to operate for 50 hours at 100 mA cm-2 without any voltage increase, which represents a meaningful step forward for cement clinker precursor production.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".