Organic–Inorganic Hybrid Materials for Trace Detection of Formaldehyde at Room Temperature
Bibliographic record
Abstract
Detection and monitoring of trace gas analytes have become essential with the rapid advancements in manufacturing technology. Despite progress in developing gas sensing materials, many still struggle to meet fundamental requirements such as adequate sensitivity and stability, particularly due to high operational temperatures. To address this limitation, this study evaluates polymer–metal oxide hybrid materials designed for trace gas detection at room temperature. Polyaniline (PANI) hybrids doped with 5 wt % of TiO 2, SnO 2, ZnO, and Co 3 O 4 were synthesized in situ and characterized using FTIR, SEM, TEM, and EDX to analyze their chemical structure, morphology, and dopant incorporation. Pristine PANI and its metal oxide-doped variants were exposed to formaldehyde, and their sensing performance was evaluated at room temperature. Among the tested materials, PANI with 5 wt % Co 3 O 4 exhibited relatively higher sensitivity toward formaldehyde, attributed to the modified morphology and enhanced interactions as a result of Co 3 O 4 incorporation in PANI. To further investigate the effect of dopant concentration, PANI composites with lower Co 3 O 4 loadings were also evaluated. These findings provide insight into optimizing polymer-based gas sensors for enhanced performance at ambient conditions.
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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".