Steam Electrolysis By Proton-Conducting Solid Oxide Electrolysis Cells (SOECs)
Bibliographic record
Abstract
Solid oxide electrolysis cells (SOECs) are considered as an effective way of converting renewable energies to chemical energy in the form of hydrogen. Using this conversion as energy storage, we can solve the site-specific and intermittent problems for renewable energies, such as solar and wind energy. During the SOEC working condition, H2O is split into H2 and O2 by applying voltage. Compared with low temperature electrolysis cells, SOECs that work at high temperatures can save electricity with the compensation from heat sources [1]. However, conventional SOECs using oxygen-ion conducting electrolytes have several problems. First, the working temperature is quite high due to the use of yttria-stabilized zirconia (YSZ) as electrolyte, which possesses adequate conductivity only at high temperatures. Second, the produced H2 is mixed with H2O, needing further separation. Third, the Ni-based fuel electrode materials trend to be oxidized by H2O during operation. To solve these problems, proton-conducting oxides are proposed as alternative electrolytes that show several advantages and can avoid the mentioned problems occurring for conventional oxygen-ion SOECs [2]. However, current proton-conducting SOECs focus on the use of BaCeO3-based electrolytes, which have been demonstrated to be unstable in the presence of water. In this talk, chemically stable BaZrO3-based electrolyte material used for proton-conducting SOECs is presented. Proton-conducting SOECs with BaZrO3-based electrolyte show a good chemical stability, together with reasonable cell performance and a superior long-term stability. The possibility of applying proton-conducting SOECs for synthesizing CH4 by co-electrolyzing CO2 and H2O will be also discussed. Reference 1. A. Hauch, S. D. Ebbesen, S. H. Jensen and M. Mogensen, J. Mater. Chem., 2008, 18, 2331-2340. 2. Lei Bi, Samir Boulfrad and Enrico Traversa, Chem. Soc. Rev., 2014, 43, 8255-8270.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".