Enhanced cathodic activity by tantalum inclusion at B-site of La0.6Sr0.4CO0.4Fe0.6O3 based on structural property tailored via camphor-assisted solid-state reaction
Bibliographic record
Abstract
Abstract Lanthanum strontium cobalt ferrite (LSCF) is an appreciable cathode material for solid oxide fuel cells (SOFCs), and it has been widely investigated, owing to its excellent thermal and chemical stability. However, its poor oxygen reduction reaction (ORR) activity, particularly at a temperature of ⩽ 800 °C, causes setbacks in achieving a peak power density of > 1.0 W·cm−2, limiting its application in the commercialization of SOFCs. To improve the ORR of LSCF, doping strategies have been found useful. Herein, the porous tantalum-doped LSCF materials (La0.6Sr0.4Co0.4Fe0.57Ta0.03O3 (LSCFT-0), La0.6Sr0.4Co0.4Fe0.54Ta0.06O3, and La0.6Sr0.4Co0.4Fe0.5Ta0.1O3) are prepared via camphor-assisted solid-state reaction (CSSR). The LSCFT-0 material exhibits promising ORR with area-specific resistance (ASR) of 1.260, 0.580, 0.260, 0.100, and 0.06 Ω·cm2 at 600, 650, 700, 750, and 800 C, respectively. The performance is about 2 times higher than that of undoped La0.6Sr0.4Co0.4Fe0.6O3 with the ASR of 2.515, 1.191, 0.596, 0.320, and 0.181 Ω·cm2 from the lowest to the highest temperature. Through material characterization, it was found that the incorporated Ta occupied the B-site of the material, leading to the enhancement of the ORR activity. With the use of LSCFT-0 as the cathode material for anode-supported single-cell, the power density of > 1.0 W·cm−2 was obtained at a temperature < 800 °C. The results indicate that the CSSR-derived LSCFT is a promising cathode material for SOFCs.
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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".