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Record W4408436237 · doi:10.1002/9781119875154.ch7

Nanoencapsulation and Nanodelivery Through Food Packaging

2025· other· en· W4408436237 on OpenAlexaff
Vimala S. K. Bharathi, Digvir S. Jayas

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsUniversity of ManitobaUniversity of Lethbridge
Fundersnot available
KeywordsFood packagingBusinessFood scienceEnvironmental scienceChemistry

Abstract

fetched live from OpenAlex

Nanoencapsulation is the process of enclosing a solid, liquid, or gaseous core material inside a wall material to form nanosized encapsulated materials. Nanoencapsulated materials are preferred in food-packaging applications not only due to their ability to enhance the properties of the packaging material but also to extend the shelf life and preserve the natural characteristics (flavor, color, aroma, and texture) of food and prevent the infestation by insects, rodents, and microorganisms of the food material. This chapter mainly focuses on the application of nanoencapsulated components in the food-packaging sector with an emphasis on active and intelligent packaging. Detailed discussion of nanoencapsulated materials in perishable food-packaging and grain-storage applications are also provided. An overview of the health and environmental impacts of the nanoencapsulated materials is included to facilitate a better understanding of the subject.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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