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A Comprehensive Review of Synthesis and Surface Modifications of Starch Nanomaterials for Fortification of Vitamin D<sub>3</sub>

2025· article· en· W4414626143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Food Science & Technology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicronutrientFood fortificationSurface modificationNanomaterialsApplications of nanotechnologyStarch

Abstract

fetched live from OpenAlex

Starch-based nanomaterials (SNPs) have garnered significant attention for their potential applications in food fortification and supplementation owing to their unique surface properties. Numerous studies have reported diverse preparation methods to produce SNPs with varying morphologies, particle sizes, stabilities, chemical reactivities, flow properties, opacities, and mechanical strengths. However, these properties, particularly in relation to micronutrient loading and release performance, remain underexplored in the current literature. Notably, the incorporation of certain micronutrients, such as vitamin D 3, into SNPs often compromises particle stability and impairs release kinetics in aqueous conditions. These limitations highlight the need for optimized surface modification strategies and scalable and cost-effective encapsulation protocols. This review critically illustrates the factors influencing starch nanoparticle fabrication, characterization, surface modification, micronutrient loading, and release kinetics. We provide a comprehensive overview of the key formulation considerations, from starch selection and physicochemical properties to emerging strategies for targeted delivery applications. Additionally, the review underscores the importance of precise surface functionalization and processing techniques, including ultrasonication, lyophilization, and the degree of substitution, in enhancing micronutrient solubility and bioavailability. To our knowledge, this work offers one of the most complete syntheses of VD 3 nanoencapsulation using SNPs, serving as a valuable resource for scholars in food science and nanotechnology. Furthermore, we present a detailed bibliometric analysis of the past decade, mapping research trends, identifying influential studies, and highlighting future research directions. This analysis offers insights into the impact, quality, and practical relevance of starch nanoparticle research, promoting innovation and advancement in food fortification technologies.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
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 head0.028
GPT teacher head0.292
Teacher spread0.265 · 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