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Record W2919879111 · doi:10.1039/c8sm02315h

Controlled generation of spiky microparticles by ionic cross-linking within an aqueous two-phase system

2019· article· en· W2919879111 on OpenAlexafffund
Niki Abbasi, Maryam Navi, Janine K. Nunes, Scott Tsai

Bibliographic record

VenueSoft Matter · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsToronto Metropolitan UniversityToronto Public HealthSt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsMicroparticlePolyethylene glycolDrug deliveryAqueous solutionChemical engineeringNanotechnologyAqueous two-phase systemPhase (matter)Ionic bondingPEG ratioMaterials scienceDextranBiocompatible materialChemistryChromatographyBiomedical engineeringOrganic chemistryIon

Abstract

fetched live from OpenAlex

Microparticles are used in a variety of different fields, such as drug delivery. Recently, non-spherical microparticle generation has become desirable. The high surface-to-volume ratio of non-spherical microparticles allows for enhanced targeting, and attachment to cells and tissue. Current non-spherical microparticle generation techniques require complicated setup, and utilizing natural micrograins, such as pollen grains, as non-spherical delivery vehicles, requires extensive post-processing. Here, we describe a unique and facile chemical synthesis approach, for controlled generation of pollen-like microparticles, based on ionic cross-linking of alginate and calcium chloride (CaCl2), within an all-biocompatible aqueous two-phase system (ATPS) of dextran (DEX) and polyethylene glycol (PEG). Our technique controls the length of spikes that emerge on the surface of these microparticles. We anticipate that these pollen-like spiky microparticles may be used as drug delivery vehicles, and this new chemical synthesis approach may be used for generating other biomaterials.

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 score0.107
Threshold uncertainty score0.587

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 head0.012
GPT teacher head0.266
Teacher spread0.253 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

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