Rapid and Low-Energy Fabrication of Symmetrical Solid Oxide Cells by Microwave Methods
Bibliographic record
Abstract
Abstract Solid oxide fuel cells are highly efficient, low-emission, and fuel-flexible energy conversion devices that can also run in reverse as solid oxide electrolysis cells, converting CO2 and/or H2O to useful fuels and pure O2. Our team has recently developed a highly promising mixed conducting oxide catalyst (La0.3Ca0.7Fe0.7Cr0.3O3−δ) that can be used at both the anode and cathode in either the fuel cell or electrolysis mode in a lower-cost symmetrical cell. However, there is still a need to improve material processing and cell manufacturing methods in this field. Here, we report, for the first time, fabrication of a symmetrical solid oxide cell, based on our very promising catalysts, using rapid, low-cost, low-energy, and green microwave (MW) processing techniques. These cells were fabricated with MW-sintered powders and were then MW-sintered without the use of any MW susceptors inside the electrode layers or any additional presintering steps. The catalyst layers show very stable nanostructures and do not delaminate, and the cells exhibit reaction rates that are similar to those obtained using normal ceramic processing methods. Importantly, the powder preparation and cell sintering steps, carried out using MW methods, require only ca. 1/3 and 1/9 of the time/energy, respectively, versus those required in traditional furnace methods, thus translating to significant cost savings.
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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 | 0.000 |
| Open science | 0.001 | 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".