Advances in the Development and Application of δ-Bi<sub>2</sub>O<sub>3</sub>-Based Ionic Conductors for Ceramic Electrochemical Cells
Bibliographic record
Abstract
The increasing demand for sustainable energy solutions has driven interest in ceramic electrochemical cells, which are also known as solid oxide electrochemical cells, for high-efficiency power generation and hydrogen production. Ceramic electrochemical cells offer fuel flexibility and reduced CO 2 emissions. However, high operating temperatures (>700 °C) result in higher costs and performance degradation. Efforts to lower the operating temperatures have led to advancements in materials, particularly Bi 2 O 3 -based ionic conductors, which are known for their superior oxygen ion conductivity. Despite their potential, Bi 2 O 3 -based materials suffer from instability at the expense of facile ionic transport. This review examines recent research addressing these challenges, focusing on intrinsic properties, chemical compositions, cell designs, and fabrication methods to improve the stability and performance. Additionally, the potential of incorporating Bi 3+ into other oxides is explored. The discussion and summary in this review aim to guide the rational design of ceramic electrochemical cells operating at low temperatures with Bi 2 O 3 -based ionic conductors.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".