Bridging Trans-Scale Electrode Engineering for Mass CO<sub>2</sub> Electrolysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Electrochemical CO 2 upgrade offers an artificial route for carbon recycling and neutralization, while its widespread implementation relies heavily on the simultaneous enhancement of mass transfer and reaction kinetics to achieve industrial conversion rates. Nevertheless, such a multiscale challenge calls for trans-scale electrode engineering. Herein, three scales are highlighted to disclose the key factors of CO 2 electrolysis, including triple-phase boundaries, reaction microenvironment, and catalytic surface coordination. Furthermore, the advanced types of electrolyzers with various electrode design strategies are surveyed and compared to guide the system architectures for continuous conversion. We further offer an outlook on challenges and opportunities for the grand-scale application of CO 2 electrolysis. Hence, this comprehensive Perspective bridges the gaps between electrode research and CO 2 electrolysis practices. It contributes to facilitating the mixed reaction and mass transfer process, ultimately enabling the on-site recycling of CO 2 emissions from industrial plants and achieving net negative emissions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".