Revealing the invisible dimensions of electrochemical carbon capture technologies through in situ/operando techniques
Bibliographic record
Abstract
Electrochemical carbon capture technologies are emerging as sustainable solutions for mitigating CO 2 emissions, offering compatibility with renewable energy sources and operation under ambient conditions. However, their development depends on a detailed understanding of the intricate mechanisms driving CO 2 capture. Conventional characterization methods, which often rely on aggregate data or ex situ techniques, fail to capture the real-time, dynamic behavior of these systems. This perspective highlights the importance of in situ and operando techniques in uncovering the invisible dimensions of electrochemical carbon capture systems. Through case studies spanning molecular, interfacial, and system-wide scales, we demonstrate how in situ/operando methodologies provide critical insights into reaction mechanisms, interfacial dynamics, and device performance. The insights presented here aim to encourage further adoption of these methodologies to deepen our understanding of the underlying mechanisms, ultimately driving the advancement and deployment of electrochemical carbon capture technologies. • Electrochemical carbon capture technologies offer a sustainable approach to CO 2 mitigation. • A detailed understanding of reaction mechanisms is crucial for advancing CO 2 capture technologies. • Conventional characterization methods, such as ex situ techniques, fail to capture real-time system dynamics. • In situ techniques reveal the “invisible dimensions” of electrochemical CO 2 capture. • The review highlights in situ methods bridging fundamental understanding and applied progress in electrochemical CO 2 capture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".