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Record W2587066259 · doi:10.1039/c6sm02674e

Effect of internal architecture on microgel deformation in microfluidic constrictions

2017· article· en· W2587066259 on OpenAlexaff
Lynna Chen, Kai Xi Wang, Patrick S. Doyle

Bibliographic record

VenueSoft Matter · 2017
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Waterloo
FundersDivision of Materials ResearchMaterials Research Science and Engineering Center, Harvard UniversityNational Science Foundation
KeywordsMicrofluidicsDeformation (meteorology)Materials scienceSoft lithographyNanotechnologyParticle (ecology)Internal flowFinite element methodWork (physics)Soft matterBucklingMechanicsFlow (mathematics)Composite materialMechanical engineeringStructural engineeringPhysicsFabricationEngineeringChemical engineering

Abstract

fetched live from OpenAlex

The study of how soft particles deform to pass through narrow openings is important for understanding the transit of biological cells, as well as for designing deformable drug delivery carriers. In this work, we systematically explore how soft microparticles with various internal architectures deform during passage through microfluidic constrictions. We synthesize hydrogel particles with well-defined internal structure using lithography-based UV polymerization in microfluidic channels (stop-flow lithography). Using this in situ technique, we explore a range of 2D particle architectures and their effect on particle deformation. We observe that particles undergo buckling of internal supports and reorient at the constriction entrance in order to adopt preferred shapes that correspond to minimum energy configurations. Using finite element simulations of elastic deformation under compression, we accurately predict the optimal deformation configuration of these structured particles.

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 score0.367
Threshold uncertainty score0.480

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 head0.006
GPT teacher head0.266
Teacher spread0.260 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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