Synthesis and Characterization of Nanocrystalline La0.65Sr0.3 MnO3 and La0.8Sr0.2MnO3 Cathode Powders by Auto-ignition Technique for Solid Oxide Fuel Cells (SOFC)
Bibliographic record
Abstract
Solid Oxide fuel Cells (SOFCs) are considered to be one of the most promising energy conversion devices that have several advantages such as high efficiency, system compactness and low environmental pollution. In the present investigation La0.65Sr0.3MnO3 (LSM-1) and La0.8Sr0.2MnO3 (LSM-2) nanoceramic powders were prepared by citrate-nitrate route of auto-combustion with citrate to nitrate (c/n) ratio 0.50 to see the effect of these cathode powders on the performance of SOFC cells. The as prepared powder were calcined at 900oC for 4hrs using the Thermolyne 47900 furnace to remove carboneous residues and characterized them using SEM / EDS, XRD, TGA techniques and their results are presented . From calculations using Debye Scherrer’s equation, the average crystallite size of the powders were found to be around 16nm. The SEM indicates the particle sizes are within the range of around 200nm.The surface area of the calcined LSM-2 powder was found to be ~21m2/g. The TGA studies indicate the completion of combustion since there was no further weight loss after reaching temperature of ~ 650oC. Also, Electrochemical characterization of LSM cathode powders were carried out by coating these powders (as cathode functional layer CFL-Bottom and current collector layer CL- Top) using Screen printing on the SOFC half cells (NiO-YSZ+YSZ) procured from CGCRI, Kolkata, India with a cell size of 16mm dia x1.6mm and tested these cells with H2-O2 at 750-800oC with the flow rates of 100-200 sccm. The results of the performances of single cells are presented in this paper. The Current density and powder density values obtained are 0.80A/cm2 (at 0.7V) and 0.55 W/cm2 at 800oC with 200 sccm of hydrogen and oxygen respectively. The area surface resistance (ASR) values obtained were ~0.50 Ωcm2 at 0.7V at 800oC.
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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) | 0.000 | 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".