Microstructural Optimization by Tailoring Particle Sizes for LSM-YSZ Solid Oxide Fuel Cell Composite Cathodes
Bibliographic record
Abstract
Serial sectioning by FIB/SEM and 3D reconstruction was used to study the microstructure-performance correlations for (LSM) and 8 mol% yttria-stabilized (YSZ) solid oxide fuel cell (SOFC) composite cathodes. In this study, the starting LSM and YSZ particle sizes were varied, keeping overall weight fractions of each solid phase constant. This analysis revealed a near one–to-one correlation between the specific parameters of the microstructure (such as the volumetric density of electrochemically-active triple phase boundaries) and cathode resistance ( ). This study has also quantitatively demonstrated that for coarser particle size and larger particle-size disparity, lower TPB lengths and surface area densities lead to lower performance. The implications for design of optimal SOFC composite electrode microstructure are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".