A redox-active polymeric network facilitates electrified reactive-capture electrosynthesis to multi-carbon products from dilute CO2-containing streams
Bibliographic record
Abstract
Reactive capture – the integration of CO2 capture with electrochemical upgrade – offers the prospect of improving overall energy efficiency in captured-CO2-to-fuels by eliminating the gas-phase CO2 desorption step, and by further offering a CO2-free gas product stream. Two related challenges limit the potential impact of electrified reactive capture today: its propensity to produce lower-value C1 products (carbon products containing one carbon atom per molecule); and its failure to retain performance when fed dilute streams (e.g. ~1-10% CO2). We posit that these could be addressed using catalysts that locally concentrate and activate in-situ generated CO2: we integrate a redox-active polymeric network whose polymer fragments undergo reversible reduction during the electrochemical conversion process, enabling electron transfer to CO2 molecules generated in-situ from carbonate capture liquid. We report as a result a 55 ± 5% C2+ (carbon products containing two or more carbon atoms per molecule) Faradaic efficiency (FE) at 300 mA/cm2 in an electrochemical reactive capture system in which the electrolysis stage is fed with 1 M K2CO3. We obtain 56 ± 4 wt% C2H4 in the product gas stream. When we use a dilute stream consisting of 1% CO2 in N2 at the KOH capture stage, we retain the C2+ FE to within 85% (relative) of its value achieved in the case of pure CO2. Reactive capture—integrating CO2 capture and electrochemical valorization—improves energy efficiency by eliminating gas-phase CO2 desorption. Here, authors design a redox-active polymeric network to boost the direct conversion of captured CO2 to multicarbon products with CO2-free gas product stream.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".