A-site-deficiency facilitated in situ growth of bimetallic Ni–Fe nano-alloys: a novel coking-tolerant fuel cell anode catalyst
Bibliographic record
Abstract
To date, most investigations of Ni-Fe bimetallic catalysts for solid oxide fuel cells (SOFCs) have focused on materials with micro-scale particle sizes, which severely restrict their catalytic activity. In this study, we fabricated a Ni- and/or Fe-doped A-site-deficient LaSrCrO3 perovskite (A-LSC) bimetallic anode material on which the in situ exsolution of uniformly dispersed nano Ni, Fe and Ni-Fe alloy with an average particle size of 25 to 30 nm was facilitated by the introduction of A-site deficiency under a reducing atmosphere. The dopants were shown to significantly enhance the electrical conductivity of the material by many orders of magnitude. Further characterization of the bimetallic material showed that the addition of Fe changed the reduction behavior and increased the amount of oxygen vacancies in the material. Fuel cell performance tests demonstrated that the prepared bimetallic anode catalyst with a highly catalytically active nano Ni-Fe alloy promoted the electrochemical performance in 5000 ppm H2S-syngas and improved the carbon deposition resistance compared to a monometallic anode catalyst.
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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".