Effects of Iron Species on Low Temperature CO<sub>2</sub> Electrolyzers
Bibliographic record
Abstract
Abstract Electrochemical energy conversion devices are considered key in reducing CO2 emissions and significant efforts are being applied to accelerate device development. Unlike other technologies, low temperature electrolyzers have the ability to directly convert CO2 into a range of value‐added chemicals. To make them commercially viable, however, device efficiency and durability must be increased. Although their design is similar to more mature water electrolyzers and fuel cells, new cell concepts and components are needed. Due to the complexity of the system, singular component optimization is common. As a result, the component interplay is often overlooked. The influence of Fe‐species clearly shows that the cell must be considered holistically during optimization, to avoid future issues due to component interference or cross‐contamination. Fe‐impurities are ubiquitous, and their influence on single components is well‐researched. The activity of non‐noble anodes has been increased through the deliberate addition of iron. At the same time, however, Fe‐species accelerate cathode and membrane degradation. Here, we interpret literature on single components to gain an understanding of how Fe‐species influence low temperature CO2 electrolyzers holistically. The role of Fe‐species serves to highlight the need for considerations regarding component interplay in general.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".