Monitoring of oxygen using colorimetric indicator based on graphene/TiO<sub>2</sub> composite with first‐order kinetics of methylene blue for modified atmosphere packaging
Bibliographic record
Abstract
Oxygen is crucial to food preservation and food spoilage. For the purpose of monitoring the integrity of modified atmosphere packaging (MAP) by nondestructive testing and controlling the reaction rate during recovery stage, we reported on a convenient and visual colorimetric oxygen indicator based on a graphene/titanium oxide composite, incorporating glycerol, methylene blue (MB), hydroxyethyl cellulose, and polyvinyl alcohol. The graphene/titanium oxide composite was synthesized from a modified Hummers synthesis of graphene oxide, followed by the hydro‐thermal treatment with butyl titanate without using any reducing agent, then the morphology and structure characteristics were analysed by X‐ray diffraction, Fourier transform infrared, Raman spectra, ultraviolet ‐ visible spectroscopy, SEM, and TEM. Supported by the performance tests using ultraviolet ‐ visible spectroscopy and CIElab, the indicator demonstrated the pseudo first‐order kinetics of MB for MAP in detecting stage. The results confirmed that the prepared colorimetric indicator could actually detect the integrity of MAP without destruction. Additionally, the reaction time of indicator in recovery stage can be controlled by changing the concentration of MB because of pseudo first‐order kinetics.
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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 | 0.001 | 0.001 |
| 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".