Synthesis, dual ionic conductivity and DFT calculations of oxygen migration pathways in NdInO3 perovskite
Bibliographic record
Abstract
Lowering the operating temperatures of solid oxide fuel cells (SOFCs) has been a central focus of research. This shift aims to enhance performance, material compatibility and economic viability. Dual ionic conductors play a crucial role in this development, as they directly impact the efficiency and functionality of electrolytes at intermediate temperatures. In this context, we synthesized the rare-earth perovskite NdInO 3 to explore it as a promising proton-conducting oxide for application as an electrolyte in intermediate-temperature solid oxide fuel cells (IT-SOFCs). Rietveld refinement was performed to determine the crystal structure, which was further confirmed by density functional theory (DFT) calculations. The electrical properties of NdInO 3 were investigated using electrochemical impedance spectroscopy under both dry and wet atmospheric conditions. At 550 °C, the total conductivity of the synthesized perovskite in wet air (4.99 × 10 −7 S cm −1 ) is an order of magnitude higher than in dry air (4.24 × 10 −8 S cm −1 ), confirming that proton conductivity makes a significant contribution to the overall conductivity of NdInO 3 at intermediate temperatures. To further interpret these experimental findings, a DFT modeling approach was employed to provide a comprehensive understanding of the oxygen migration processes and to evaluate the migration energy barriers along each pathway in the studied compound. The computed migration energy barriers are in fair agreement with experiments. We find that the HSE06 hybrid functional lowers the migration energy barriers by 5–13% compared to the PBE exchange-correlation functional.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".