Modulating Lattice Oxygen Activity of Iron‐Based Triple‐Conducting Nanoheterostructure Air Electrode via Sc‐Substitution Strategy for Protonic Ceramic Cells
Bibliographic record
Abstract
Abstract Iron‐based perovskite air electrodes for protonic ceramic cells (PCCs) offer broad application prospects owing to their reasonable thermomechanical compatibility and steam tolerance. However, their insufficient electrocatalytic activity has considerably limited further development. Herein, oxygen‐vacancy‐rich BaFe0.6Ce0.2Sc0.2O3−δ (BFCS) perovskite is rationally designed by a facile Sc‐substitution strategy for BaFe0.6Ce0.4O3‐δ (BFC) as efficient and stable air electrode for PCCs. The BFCS electrode with an optimized Fe 3d‐eg orbital occupancy and more oxygen vacancies exhibits a polarization resistance of ≈ 0.175 Ω cm2 at 600 °C, ≈ 1/3 of the BFC electrode (≈0.64 Ω cm2). Simultaneously, BFCS shows favorable proton uptake with a low proton defect formation enthalpy (− 81 kJ mol−1). By combining soft X‐ray absorption spectroscopy and electrical conductivity relaxation studies, it is revealed that the enhancement of Fe4+–O2− interactions in BFCS promotes the activation and mobility of lattice oxygen, triggering the activity of BFCS in both oxygen reduction and evolution reactions (ORR/OER). The single cell achieves encouraging output performance in both fuel cell (1.55 W cm−2) and electrolysis cell (−2.96 A cm−2 at 1.3 V) modes at 700 °C. These results highlight the importance of activating lattice oxygen in air electrodes of PCCs.
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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".