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Record W4392095596 · doi:10.1002/adma.202312707

From Nature‐Sourced Polysaccharide Particles to Advanced Functional Materials

2024· article· en· W4392095596 on OpenAlexafffund
Yingshan Ma, Sofia M. Morozova, Eugenia Kumacheva

Bibliographic record

VenueAdvanced Materials · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaRussian Science Foundation
KeywordsBiocompatibilityNanotechnologyMaterials scienceRaw materialDrug deliverySurface modificationPolymerFabricationBiochemical engineeringChemical engineeringOrganic chemistryChemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

Polysaccharides constitute over 90% of the carbohydrate mass in nature, which makes them a promising feedstock for manufacturing sustainable materials. Polysaccharide particles (PSPs) are used as effective scavengers, carriers of chemical and biological cargos, and building blocks for the fabrication of macroscopic materials. The biocompatibility and degradability of PSPs are advantageous for their uses as biomaterials with more environmental friendliness. This review highlights the progresses in PSP applications as advanced functional materials, by describing PSP extraction, preparation, and surface functionalization with a variety of functional groups, polymers, nanoparticles, and biologically active species. This review also outlines the fabrication of PSP-derived macroscopic materials, as well as their applications in soft robotics, sensing, scavenging, water harvesting, drug delivery, and bioengineering. The paper is concluded with an outlook providing perspectives in the development and applications of PSP-derived materials.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
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)0.0010.001

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 head0.256
Teacher spread0.245 · 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 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

Citations57
Published2024
Admission routes2
Has abstractyes

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