Enhanced oxygen reduction kinetics by a porous heterostructured cathode for intermediate temperature solid oxide fuel cells
Why this work is in the frame
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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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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 | 0.000 | 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 it