Enhancing the Performance of Metal-Supported Solid Oxide Fuel Cells via Infiltration with an Aqueous Solution of Metal Nitrate Salts
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The infiltration technique is a cost-effective method to develop nanostructured electrodes that can accelerate sluggish oxygen reduction reaction (ORR) and enhance the electrochemical performance of solid oxide fuel cells (SOFCs) at intermediate temperatures (600–800 °C). For metal-supported SOFCs, identifying a highly efficient ORR catalyst is an ongoing challenge due to lower temperature operation. In this work, nanostructured praseodymium oxide (PrO x ) and multiphase heterostructures containing perovskites with the nominal composition of Nd 0.6 Sr 0.4 CoO 3−δ (NSC), SrCO 3, and CoO have been developed via infiltration into the symmetric metal-supported backbone as binary layer composite, and their electrochemical performance has been investigated. The composite demonstrates enhanced electrochemical performance at various temperatures achieving the lowest polarization resistance ( R p ) of 0.05 Ω cm 2 at 700 °C compared to multiphase NSC alone (0.1 Ω cm 2 ) under similar conditions. A distribution function of relaxation time (DFRT) analysis using impedance spectroscopy genetic program (ISGP) was carried out to study different electrochemical processes. PrO x significantly improves the processes involved in the ORR. The full cell performance of the composite electrode achieves a peak power density (PPD) of 329 mW·cm –2 at 700 °C in 3%H 2 O/H 2 as fuel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".