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Record W4413429801 · doi:10.3390/molecules30173453

Magnetic Nanoparticle-Based Nano-Packaging and Nano-Freezing in Food Storage Applications

2025· review· en· W4413429801 on OpenAlexaff
Sayan Ganguly, Shlomo Margel

Bibliographic record

VenueMolecules · 2025
Typereview
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNano-NanoparticleNanotechnologyFood packagingMaterials scienceChemistryFood scienceComposite material

Abstract

fetched live from OpenAlex

Magnetic nanoparticles (MNPs) have emerged as essential agents in food preservation, tackling significant issues related to shelf life extension, quality maintenance, and safety assurance. This thorough analysis consolidates current developments in MNP-based nano-packaging and nano-freezing technologies, emphasizing their processes, effectiveness, and commercial feasibility. Metallic nanoparticles augment packaging efficacy via antibacterial properties, oxygen absorption, and real-time freshness assessment, while transforming freezing techniques by inhibiting ice crystal development and maintaining cellular integrity. Notwithstanding their potential applications, regulatory uncertainties, toxicity issues, and scalability challenges necessitate collaborative multidisciplinary approaches. We rigorously survey the technological, environmental, and safety aspects of MNP deployment in the food sector and suggest research priorities for sustainable implementation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.274
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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