Advanced Applications of Responsive Nanomaterials in Intelligent Food Packaging
Bibliographic record
Abstract
Abstract To meet the growing for convenience markets, intelligent packaging materials have become integral to innovative packaging systems distinguished by their embedded visualization and digitization capabilities. However, the widespread adoption of such systems is hindered by the inherent limitations of conventional indicator materials, particularly their low resistance to moisture, heat, and light. Recent advances in nanoscience have positioned multifunctional responsive nanomaterials as highly promising candidates for intelligent packaging applications owing to their superior sensitivity, selectivity, recyclability, and resistance to migration. The incorporation of nanomaterials is shown to significantly improve the accuracy and sensitivity of monitoring platforms, while enabling the replacement of traditional water‐ or alcohol‐soluble pigments to prevent indicator leakage. This review begins by outlining the mechanisms of food spoilage, followed by an in‐depth discussion of the fundamental principles behind intelligent packaging indicators, including colorimetric, time‐temperature, humidity, and biosensor‐based systems. It further presents a comprehensive overview of the fabrication strategies, sensing mechanisms, and practical applications of responsive nanomaterials in intelligent food packaging. Lastly, future directions are highlighted, including the development of novel composite materials for multifaceted analysis, the shift toward sustainable and low‐toxicity nanomaterials, and the integration of advanced analytics with machine learning for enhanced monitoring interfaces.
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) | 0.000 | 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) | 0.001 | 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".