Electrochemical regeneration of caustic absorbent for the capture of CO2
Bibliographic record
Abstract
Abstract Electrochemistry has a potential role in the removal of CO 2 from the atmosphere in the process of “electrochemically mediated carbon capture”. The future viability of that technology rests in part on the invention and development of specialized electrochemical reactors. This paper reports tests undertaken with a novel bench-scale electrochemical reactor intended for the potential regeneration of caustic absorbent in the direct air capture of CO 2 . The reactor design was based on capillary effects in micro-porous separators, with a single electrolyte stream and no ion-exchange membranes or gas diffusion electrodes. In batch-recycle mode the reactor converted 1 to 4 molar solutions of M 2 CO 3 (M ≡ Na or K) to MOH + M 2 CO 3 plus separate gas co-products H 2 and (O 2 + CO 2 ). Integral current efficiency for OH − and CO 2 fell from ca. 90% to 10% as the product [OH − ]/[CO 3 = ] ratio climbed from 0 to 3. The current density, full cell voltage, pressure, temperature and electrochemical specific energy ranged respectively from 1–6 kA m −2 , 3–10 V, 105–120 kPa(a), 25–70 ℃ and 4–200 kWh kg CO 2 −1 . Operating time was limited to about 20 h as reactor components deteriorated rapidly with the strong alkali and high oxidation potential around the anode. In some circumstances and with further development such an electrochemical system could potentially replace the thermochemical regeneration of alkaline absorbent for carbon capture. Graphical abstract
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".