Solution Precursor Plasma Spray of Porous La1−xSrxMnO3 Perovskite Coatings for SOFC Cathode Application
Bibliographic record
Abstract
The deposition of porous La1−xSrxMnO3 (LSM) perovskite cathode materials by conventional plasma spray has been a challenge because of the decomposition of perovskite materials to their suboxides at high temperature. In this paper, the solution precursor plasma spraying (SPPS) process, in which solution precursors of the desired resultant materials are fed into a direct current plasma jet by atomizing gas, was used to simultaneously synthesize LSM perovskite and deposit porous cathode coatings. The experimental results show that process parameters have a significant effect on the fabricated coatings. The perovskite coatings consist of porous agglomerates of small particles with rounded features and local denser regions referred to as thick flakes. The small particles and thick flakes were held together by the previously molten material. There are two kinds of pores in the fabricated coatings: large pores located between the agglomerates and fine pores inside the agglomerates. The porous LSM cathode coatings have 20–40 area % of desirable homogeneous pores determined by several processing parameters. X-ray diffraction of sintered coatings shows that no suboxides of La1−xSrxMnO3 perovskite appear. The results of this project indicate that the SPPS is a potential process to produce high quality cathodes for solid oxide fuel cell application.
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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".