Efficient CO electrosynthesis in hydroxide-mediated reactive capture systems through catalyst and microenvironment design
Bibliographic record
Abstract
The electrochemical conversion of captured CO 2 – also known as reactive capture – offers a promising approach to produce renewable carbon monoxide (CO) while bypass the energy and cost-intensive CO 2 capture, purification and pressurization processes at large scale. However, current reactive capture systems suffer from low CO selectivity (< 50%) and productivity (< 100 mA cm⁻ 2 ) due to the lack of efficient electrocatalysts and limited CO 2 availability at the reactive interfaces. Here, we develop a coupled catalyst and microenvironment strategy to overcome these barriers. Employing Ni single-atom catalysts with a high density of reactive sites (Ni loading up to 3.0 wt%), together with enhanced CO 2 regeneration and transport to the catalyst via local hydrophobicity control, we achieved efficient CO production with a Faradaic efficiency of 68% at 100 mA cm⁻ 2 with stable performance maintained over 100 hours in a hydroxide-mediated reactive capture system. The system achieved a CO energy efficiency of 27% and an energy intensity of 37.7 GJ ton⁻¹ CO , outperforming the best reported amine- and hydroxide-based reactive capture processes operating at ambient temperature and pressure.
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 | 0.000 | 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".