Enhanced oxygen reduction kinetics by a porous heterostructured cathode for intermediate temperature solid oxide fuel cells
Bibliographic record
Abstract
A novel porous heterostructured Nd0.8Sr1.2CoO4±δ/Nd0.5Sr0.5CoO3-δ (NSC214/113) cathode for intermediate temperature solid oxide fuel cells (IT-SOFCs) is developed to significantly enhance oxygen reduction reaction (ORR) kinetics. Compared to single-phase materials, the fabricated porous heterostructured NSC214/113 shows optimized electrochemical properties, including a better conductivity, 20 times faster surface oxygen exchange kinetics, and a comparatively lower area-specific resistance (0.065 Ω cm2 at 800 °C). The single cell with Ni-YSZ|YSZ-GDC|NSC214/113 configuration exhibits a high peak power density of 1.10 W cm−2 at 800 °C, superior to other cells reported in literature with similar heterostructured cathodes. Moreover, the underlying mechanism of the ORR performance enhancement is further investigated, revealing that the formation of heterojunction can lead to a narrowed energy bandgap and a decrease of Co oxidation state, which further induce better conductivity, more available electrons and oxygen vacancies to enhance the ORR process. Taken together, our research also provides new insights into potential application of artificial intelligence (AI) method involved in materials intelligent identification, cell state estimation, system diagnostic and optimization. The revolutionary force of AI, especially in the field of new electrode material development is now advancing in its full swing. More and greater breakthroughs are still expected.
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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) | 0.000 | 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 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".