Bibliographic record
Abstract
Plasma spray processing is a low-cost, rapid manufacturing technique that is widely used industrially for fabrication of thermal barrier and wear- and corrosion-resistant coatings. Because the technique can be used to rapidly deposit coatings of high melting temperature materials with good substrate adhesion, it has also been applied to the production of individual component layers in tubular solid oxide fuel cells (SOFCs), and more recently, in planar SOFCs. The use of plasma spray processing for the fabrication of fuel cell components presents unique challenges, due to the high porosities required for the electrode layers and fully dense coatings required for electrolytes. Application of plasma spray processing for the manufacture of solid oxide fuel cells is discussed, with consideration of potential advantages of the technique compared to standard SOFC wet ceramic processing routes. Major challenges faced in the adaptation of the processing method to solid oxide fuel cell manufacture are discussed, along with current research approaches being used to overcome these challenges. Recent developments in the use of the technique for the rapid onestep manufacturing of direct oxidation SOFC anodes are discussed, for composite material combinations that cannot be co-sintered due to widely divergent melting points. The impacts of plasma sprayed coating properties on solid oxide fuel cell performance are considered, and implications of the use of the technique on overall stack and system manufacturing costs are discussed.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".