Electrochemical Reduction of H<sub>2</sub>O and CO<sub>2</sub> Using Anode Supported SDC Cells
Bibliographic record
Abstract
In this contribution, we studied electrochemical reduction of H2O and CO2 at reduced temperatures using anode supported SDC cells. We observed that the SDC cell voltage was 1.16V under electrochemical reduction of H2O and 1.17V under electrochemical reduction of CO2 at 500oC, 0.5A cm-2. The cell showed no degradation during a short term (72hr) stability test under the CO2 electrochemical reduction condition. It was noticed that the SDC cells showed much better cell performance in the electrolysis cell (EC) mode than in the fuel cell (FC) mode in term of polarization. It was also found that the impedance of the SDC cell changed dramatically with applied current. At high applied EC current the cell became more like a pure inductor (or a resistor), i.e., the semicircle representing the electrochemical polarization was decreased, and the electrolyte bulk resistance was also reduced substantially. It implies that the SDC cell under the EC mode becomes more electronically conductive, and the resistance is varied with the applied current. GC analysis of fuel side gas composition revealed that CO2 conversion rate was low, and almost irrelevant to the applied current. The corresponding faradic efficiency is extremely low, indicating a severe internal shorting problem under the EC tested conditions. Therefore, we conclude that the anode supported SDC electrolyte cell is not feasible for SOEC application even at a reduced temperature.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".