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Spray Gelation of Ionic Polysaccharide Microgels

2025· article· en· W4416706217 on OpenAlexafffund
Chunchang Li, John M. Frostad, Vassilis Kontogiorgos

Bibliographic record

VenueACS Food Science & Technology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundCanada Foundation for Innovation
KeywordsPectinFabricationIonic bondingPolysaccharideIonic strengthParticle sizeReproducibility

Abstract

fetched live from OpenAlex

Biopolymeric microgels have attracted considerable interest in the food and pharmaceutical sectors due to their tunable physicochemical properties. This study presents a simple and efficient method for producing microgels with consistent characteristics in terms of the density, particle size, and morphological uniformity. Microgels were generated by spraying ionic polysaccharide solutions using a hand-held atomizer directly into a calcium ion-containing buffer, eliminating the need for additional thermal or mechanical processing. To validate the reproducibility and tunability of this method, pectin-based microgels were prepared under varying conditions, including the degree of methylation and amidation of pectin, cross-linker concentration, and pH. The microgels exhibited high batch-to-batch consistency across all preparation conditions. Overall, the proposed spray-gelation approach offers a robust platform for the reproducible fabrication of pectin microgels and serves as a reference for a broader application to other ionic polysaccharides.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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 routes2
Has abstractyes

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