Recent Advances on the Gas-Sensing Properties and Mechanism of Perovskite Oxide Materials - A Review
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Perovskite oxide-based materials (ABO 3 ) have gained much attention as promising candidates for advanced gas-sensing applications due to their versatile structures, tunable properties, and excellent stability. This review discusses recent developments in the synthesis, structural optimization, and functionalization of perovskites to enhance their gas-sensing performance. Strategies such as doping, creating oxygen vacancies, tuning morphology, and forming heterojunctions have significantly improved their sensitivity, selectivity, response, and recovery times. Specific advances include the incorporation of nanostructures, porous morphologies, and catalytic elements, which have optimized the adsorption and desorption processes for various target gases, including volatile organic compounds, NO 2, and CO 2 . Mechanistic insights into the role of oxygen vacancies, surface defects, and charge carrier dynamics are also addressed. These developments position perovskite materials as important components in next-generation gas sensors for environmental monitoring and industrial applications.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".