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Record W4294770798 · doi:10.1021/acsapm.2c00943

Microfluidic Generation of Therapeutically Relevant Polycaprolactone (PCL) Microparticles: Computational and Experimental Approaches

2022· article· en· W4294770798 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Polymer Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersBritish Columbia Knowledge Development FundCanada Research ChairsMichael Smith Health Research BCCanada Foundation for InnovationCanadian Institutes of Health ResearchInnovate BC
KeywordsMicrofluidicsMicroparticlePolydimethylsiloxaneNanotechnologyPolycaprolactoneDrug deliveryDispersityFlow focusingMaterials scienceFabricationPolymerChemical engineering

Abstract

fetched live from OpenAlex

Drug releasing microparticles play an important role in drug delivery as they can be used for site specific delivery as well as control over the release time of therapeutics. The use of microfluidic technologies for the fabrication of these particles is of increasing interest since they provide enhanced control over microparticle size and size distribution compared to bulk production methods. However, the use of microfluidic platforms in the production of drug releasing microparticles with therapeutically relevant cargo still requires optimization depending on their application, and the effect of the addition of cargo on the production process is still unexplored. Here we show the formation of therapeutically relevant (in terms of size and dose of cargo) polycaprolactone (PCL) microparticles using a microfluidic platform and analyze the effect of the addition of cargo in the microparticle size and size distribution. This microfluidic platform was designed with the aid of computational fluid dynamic simulations, allowing us to construct a polydimethylsiloxane (PDMS) microfluidic device capable of making microparticles in the range of 15 to 35 μm with low coefficient of variation (CV) both with and without cargo by varying the flow rate ratios of the phases used during droplet generation. Our data show the effect of the addition of cargo on the droplet and microparticle sizes and monodispersity. Our fabrication method allows the formation of spherical microparticles, optimal for biomedical applications. In addition, our microfluidic platform can maintain the generation of monodisperse droplets (with an average size of 52.5 μm) over extended periods of time, suggesting it has the capacity to be used for scaled-up production of PCL microparticles. This microfluidic device is a robust and reliable method for the fabrication of PCL microparticles with cargo, which can potentially be loaded with other relevant therapeutic molecules for biomedical applications.

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)0.000
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 score0.035
Threshold uncertainty score0.714

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 head0.034
GPT teacher head0.232
Teacher spread0.198 · 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