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Record W3022246988 · doi:10.1021/acs.biomac.0c00462

Graft Modification of Starch Nanoparticles Using Nitroxide-Mediated Polymerization and the “Grafting to” Approach

2020· article· en· W3022246988 on OpenAlexafffund
Jaime C. Cazotti, Alexander T. Fritz, Omar García‐Valdez, Niels M. B. Smeets, Marc A. Dubé, Michael F. Cunningham

Bibliographic record

VenueBiomacromolecules · 2020
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of OttawaEcoSynthetix (Canada)Queen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDispersityGraftingPolymer chemistryPolymerCopolymerPolymerizationNitroxide mediated radical polymerizationStyreneMaterials scienceNanoparticleChemical engineeringRadical polymerizationMethyl methacrylateChemistryOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

Starch nanoparticles (SNP) were modified with synthetic polymers using the “grafting to” approach and nitroxide-mediated polymerization. SG1-capped poly(methyl methacrylate-co-styrene) (P(MMA-co-S)) copolymers with low dispersity and high degree of livingness were first synthesized in bulk. These macroalkoxyamines were then grafted to vinyl benzyl-functionalized SNP to obtain biosynthetic hybrids. The grafted materials, SNP-g-P(MMA-co-S), were characterized by 1H NMR, FTIR, TGA, and elemental analysis. The total amount of grafted polymer and the grafting efficiency were evaluated for different molecular weights (5870–12150 g·mol–1) of the grafted polymer, the polymer addition approach (batch or semibatch) and the initial polymer loading (2.5, 5, or 10 g polymer/g SNP). The proposed approach presented in this work to graft modify SNP allows for a precise surface modification of the nanoparticles, while permitting that the final properties of the resulting biohybrid to be tunable according to the choice of polymer grafted.

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 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 score0.084
Threshold uncertainty score0.299

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)0.0000.000

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 head0.042
GPT teacher head0.263
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.

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

Citations22
Published2020
Admission routes2
Has abstractyes

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