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Record W2990351148 · doi:10.1039/c9sm01801h

Diffusiophoresis of active colloids in viscoelastic media

2019· article· en· W2990351148 on OpenAlexafffund
Shabab Saad, Giovanniantonio Natale

Bibliographic record

VenueSoft Matter · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsJanusColloidViscoelasticityHydrogen peroxideMicrosphereChemistryJanus particlesNanotechnologyChemical engineeringMaterials scienceComposite materialOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Self-diffusiophoresis of synthetic Janus (Si/Pt) microspheres in the presence of hydrogen peroxide in complex environments is here investigated. We aim to address the single particle dynamics of these active colloids in different viscoelastic fluids. Experimentally, the Janus colloids were dispersed in a dilute polyvinylpyrrolidone (PVP) solution and in a polyacrylamide (PAM) solution in semi-dilute and semi-dilute entangled regime to analyze their Brownian and active motion. These two systems were chosen to probe different relaxation times from relatively short (∼5 ms) for PVP to large (∼14.5 s) for PAM but always smaller than the rotary Brownian motion time scale. Within this regime, we investigate the coupling between the self-propulsion velocity and the medium rheology. Janus particles are found to get physically confined by polymeric entanglements but surprisingly they are able to escape the physical cage in a time scale much shorter than the relaxation time of the polymer solution. This is particularly relevant for application of self-propelling particles in biomedicine, water and soil remediation where complex environments are naturally present.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.003
GPT teacher head0.194
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations40
Published2019
Admission routes2
Has abstractyes

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