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 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".