Enhanced gas sensing performance of Ag-Doped BiFeO3 microspheres synthesized via flash auto combustion technology
Bibliographic record
Abstract
This investigation demonstrates the successful synthesis of well-crystallized pristine and Ag-doped BiFeO3 microspheres using flash auto combustion technology. The effects of Ag doping on the morphology and microstructural characteristics were thoroughly examined through SEM, EDS, and powder X-ray diffraction (XRD) studies. Gas sensing experiments were performed to evaluate the response of the synthesized materials to NO gas. The results revealed a remarkable enhancement in the gas sensing capabilities of 5% wt Ag-doped BiFeO3 compared to pure BiFeO3. Specifically, the gas response towards NO was found to be 2.4 times higher for Ag-doped BiFeO3. This significant improvement can be attributed to the presence of Ag atoms within the lattice structure, which not only increased the density of holes in the material but also created additional gas molecule adsorption sites. Furthermore, the Ag dopant exhibited a catalytic effect, contributing to the excellent gas sensor performance of the material. These findings hold great promise for the development of highly sensitive and efficient gas sensors, particularly in applications where the detection of low concentrations of NO is crucial. The utilization of flash auto combustion technology in the synthesis process offers a viable route for scalable production of advanced gas sensing materials with enhanced performance.
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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) | 0.000 | 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".