New Perovskite Materials for Sensors and Low Temperature Solid Oxide Fuel Cell (LT-SOFC) Applications
Bibliographic record
Abstract
This work involved the development of new perovskite oxides based on SmFeO3 and testing their performances as sensors for reducing gases (H2, CO & CH4) and as anode materials for dry methane oxidation in solid oxide fuel cells. The new perovskite oxide materials with formula Sm0.95Ce0.05Fe1-xMxO3-δ (M= Co, Ni & Cr) were synthesized by a sol gel method using citric acid as a complexing agent. The resulting materials were characterized by using a battery of techniques including XRD, XRF, XPS, SEM and electrochemical methods. Sensing experiments revealed that both cobalt doped and Cr doped materials can detect H2, CO and CH4 in air at different temperatures including room temperature. The Ni doped materials did not prove good candidates as sensors. However, their reduction treatment studies showed the formation of metallic nanoparticles on the surface which deeply influence their electrical conductivity as well as sensing ability. Consequently, this modification in surface structure and chemical composition enabled them to sense hydrogen gas at 300oC very effectively. The response of sensors based on these reduced materials was measurable and reversible. Some materials were also selected on the basis of their reduction stability and electrical properties, and their electrochemical performances were evaluated as SOFC anodes under dry methane and dry hydrogen fuels separately. The performance tests as SOFC anode revealed that the best anode material for the oxidation of dry hydrogen fuel is Sm0.95Ce0.05FeO3-δ. Furthermore, Sm0.95Ce0.05FeO3-δ proved to be coke resistant anode under dry methane fuel and exhibited reasonably low charge transfer resistance values at temperatures between 600-700oC. The doping of Co and Ni at the B-site of Sm0.95Ce0.05FeO3-δ found to be very effective in further improving its performance as SOFC anode towards oxidation of dry methane fuel at the lower temperatures.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".