Closed-loop, machine learning–driven optimization of reactor yields in reactive carbon electrolyzers
Bibliographic record
Abstract
Reactive carbon capture combines CO2 capture and conversion in a single system. Reactive carbon electrolyzers receive a liquid eluent from a CO2 capture unit containing a sorbent that has captured CO2. This electrolyzer releases CO2 electrochemically and converts it into a value-added product like CO. The effectiveness of this system depends on high CO2 utilization and high product formation rates. We define their product as “reactor yield.” Here, we used a closed-loop, automated workflow with Bayesian optimization to maximize reactor yield in an electrolyzer operating with alkaline CO2 capture solutions. We explored a six-dimensional parameter space and found that a bicarbonate concentration of 1.5 M and carbonate concentration of 0.75 M achieved the highest reactor yield (44 mA cm^-2). Interestingly, this optimum occurred at non-maximum values of CO partial current density (54 vs. 87 mA cm^-2) and CO2 utilization (81% vs. 100%), highlighting the need for joint optimization of both factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".