Synthesis and Characterization of Nano-Size La0.8Sr0.2MO3-d Cathode Materials by Induction Plasma
Bibliographic record
Abstract
Abstract The synthesis of nanoscale particles has received considerable attention because of the potential for new materials and unique properties. The novel properties and the numerous applications of nanophase materials, especially ceramic nanopowders, have attracted many scientists and engineers to invent and explore the preparation methods of ceramic nanoparticles. Induction plasma is used to synthesize cathode materials for fuel cells. Solid oxide fuel cells (SOFCs) are very promising energy conversion systems. SOFCs are based on an oxide-ion conducting electrolyte and they offer a clean, low-pollution technology to electrochemically generate electricity at high efficiencies. These fuel cells provide many advantages over traditional energy conversion systems including high efficiency, reliability, modularity, fuel adaptability, and very low levels of SOx and NOx emissions. It has been found that La1-xSrxMO3-d, (M= Fe, Co etc) are perovskite materials widely considered as the Intermediate Temperature SOFC cathode materials of choice. In particular, La0.6Sr0.4Co0.2Fe0.8O3-δ is extensively used for IT-SOFCs because its thermal expansion coefficient is relatively close to that of the common electrolytes. In this paper, the nanopowders of SOFC cathode materials were synthesized by thermal plasma spray technique. The results of their structure, morphology and particle size distributions will be presented. Abstract only; no full-text paper available.
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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".