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Record W4387985655 · doi:10.1039/d3an01371e

Surface charging and electrophoretic behavior of conductive polymer micro-droplets in conductive polymer liquid solutions

2023· article· en· W4387985655 on OpenAlexaff
Deyu Li, Yongxin Song, Dongqing Li

Bibliographic record

VenueThe Analyst · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsElectrophoresisDextranPolyethylene glycolSurface chargePEG ratioElectric fieldPolymerChemistryElectrophoretic depositionMaterials scienceChemical engineeringChemical physicsAnalytical Chemistry (journal)ChromatographyNanotechnologyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

This study investigates the surface charging and electrophoretic motion of polyethylene glycol-rich (PEG-rich) micro-droplets in dextran-rich solutions or dextran-rich micro-droplets in PEG-rich solutions. The electrophoretic velocities of the droplets were measured in a centimeter-sized chamber under an optical microscope. It was found that the direction of electrophoretic motion of both the PEG-rich droplets and dextran-rich droplets is opposite to the applied electric field, meaning that both the PEG-rich droplets and dextran-rich droplets are negatively charged. The electrophoretic velocity is independent of droplet size but proportional to the electric field strength. Increasing the NaCl concentration reduces the electrophoretic velocity of PEG-rich droplets and increases it for dextran-rich droplets, suggesting different surface charge changes due to ion affinity. The charge densities and velocities are affected by the PEG and dextran mass fractions. Physical models for droplet surface charging under different conditions were proposed to explain the experimental results.

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 score0.019
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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