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Record W2013962500 · doi:10.1021/la8022985

Formation and Shear-Induced Processing of Quantum Dot Colloidal Assemblies in a Multiphase Microfluidic Chip

2008· article· en· W2013962500 on OpenAlexaff
Greg Schabas, Chih‐Wei Wang, Ali Oskooei, Huda Yusuf, Matthew G. Moffitt, David Sinton

Bibliographic record

VenueLangmuir · 2008
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPolystyreneMaterials scienceMicrofluidicsBreakupShear rateSurface tensionShear flowQuantum dotColloidShear (geology)PolymerNanotechnologyChemical physicsComposite materialChemistryMechanicsThermodynamicsPhysicsRheologyOrganic chemistry

Abstract

fetched live from OpenAlex

The controlled self-assembly of polymer-stabilized quantum dots (QDs) into mesoscale aqueous spherical assemblies termed quantum dot compound micelles (QDCMs) using a two-phase gas-segmented microfluidic reactor is described. Self-assembly is initiated by the fast mixing of water (approximately 1 s) with a blend solution of polystyrene-coated QDs and amphiphilic polystyrene-block-poly(acrylic acid) stabilizing chains via chaotic advection within liquid plugs moving through a sinusoidal channel. Subsequent recirculating flow within a post-formation channel subjects the dynamic QDCMs to shear-induced processing, controlled via the flow rate and channel length, before a final quench into pure water. During processing, larger QDCMs within the initial population undergo breakup into smaller particles, resulting in smaller mean particle sizes, smaller relative standard deviations, and more skewed distribution shapes, as the overall shear exposure is increased. For these cases, shear-induced size reduction is sufficient to dominate surface tension-driven growth.

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 score0.030
Threshold uncertainty score0.432

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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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

Citations51
Published2008
Admission routes1
Has abstractyes

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