Nanomaterials in Smart Packaging Applications: A Review
Bibliographic record
Abstract
Food wastage is a critical and world-wide issue resulting from an excess of food supply, poor food storage, poor marketing, and unstable markets. Since food quality depends on consumer standards, it becomes necessary to monitor the quality to ensure it meets those standards. Embedding sensors with active nanomaterials in food packaging enables customers to monitor the quality of their food in real-time. Though there are many different sensors that can monitor food quality and safety, pH sensors and time-temperature indicators (TTIs) are the most critical metrics in indicating quality. This review showcases some of the recent progress, their importance, preconditions, and the various future needs of pH sensors and TTIs in food packaging for smart sensors in food packaging applications. In discussing these topics, this review includes the materials used to make these sensors, which vary from polymers, metals, metal-oxides, carbon-based materials; and their modes of fabrication, ranging from thin or thick film deposition methods, solution-based chemistry, and electrodeposition. By discussing the use of these materials, novel fabrication process, and problems for the two sensors, this review offers solutions to a brighter future for the use of nanomaterials for pH indicator and TTIs in food packaging applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 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".