Effect of Sintering Temperature on the Mechanical Properties of Film Gd 0.2 Ce 0.8 O 1.9 Electrolyte for SOFCs Using Nanoindentation
Bibliographic record
Abstract
The mechanical properties of thin film gadolinia doped ceria (Gd0.2Ce0.8O1.9, GDC) electrolyte, for solid oxide fuel cells (SOFCs), with different levels of sintering density were investigated by the nanoindentation technique. Electrolyte thin film supported on Ni-GDC cermet was made by co-sintering at several temperatures between 1350 and 1450 oC. The microstructures of the electrolyte films and the cells performances were studied by scanning electron microscope (SEM) and current-voltage tests, respectively. In order to determine the mechanical properties, a Berkovich indenter was used at different applied loads (30, 50 and 100 mN). Plastic deformation took place, so Oliver and Pharr equations must be applied to evaluate the hardness and Youngrs modulus of the electrolyte film. The residual nanoindentations were observed by optical microscope (M.O.) and field emission scanning electron microscope (FE-SEM). The present study reveals that the nanoindentation is a non-destructive and ideal technique to determinate the quality and the mechanical properties of the thin film of a SOFC. The results also show that the hardness decreases with the increasing of the applied load, which is attributed to the indentation size effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".