Constructing Robust and Efficient Ceramic Cells Air Electrodes Through Collaborative Optimization Bulk and Surface Phases
Bibliographic record
Abstract
Abstract Slow reaction kinetics of air electrodes is a common problem faced by low‐temperature (<650 °C) oxygen‐ion conducting solid oxide fuel cells (O‐SOFCs) and proton‐conducting reversible proton ceramic cells (R‐PCCs). Here, an innovative approach is proposed to design and prepare two efficient and durable Ba0.5Sr0.5Co0.8Fe0.2O3−δ (BSCF)‐based nanocomposites through self‐reconstruction strategy, which aim to optimize both the bulk and surface properties of electrode materials simultaneously. Specifically, the two nanocomposites with a nominal composition of Ba0.4Sr0.5Cs0.1Co0.7Fe0.2M0.1O3−δ (M═Ni, Zr) consisted of the major perovskite phase and surface‐enriched NiO and BaZrO3 minor phases. When Ba0.4Sr0.5Cs0.1Co0.7Fe0.2Ni0.1O3−δ (BSCsCFNi) is used as an air electrode in O‐SOFCs, the peak power density is 1.36 W cm−2 at 650 °C; while Ba0.4Sr0.5Cs0.1Co0.7Fe0.2Zr0.1O3−δ (BSCsCFZr) is used in R‐PCCs, a peak power density of 1.24 W cm−2 and a current density of −1.98 A cm−2 (1.3 V) are achieved at 650 °C, and exhibits stable reversibility over 100 h. Theoretical calculations and experiments indicate that Cs+ doping enhances the bulk conduction of oxygen ions and protons; NiO nanoparticles enhance oxygen adsorption and surface exchange; BaZrO3 nanoparticles increase steam adsorption and hydration capacity. This study provides a new idea for designing efficient and durable air electrodes of ceramic cells.
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 | 0.001 | 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".