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Record W2971774303 · doi:10.1002/elps.201900246

Zeta potentials of PDMS surfaces modified with poly(ethylene glycol) by physisorption

2019· article· en· W2971774303 on OpenAlexafffund
Yongxin Song, Angran Feng, Zhijian Liu, Dongqing Li

Bibliographic record

VenueElectrophoresis · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsZeta potentialPEG ratioEthylene glycolPhysisorptionElectrolyteChemical engineeringMaterials scienceElectrophoresisSurface modificationChromatographyLayer (electronics)ChemistryNanotechnologyOrganic chemistryPhysical chemistryAdsorptionElectrode

Abstract

fetched live from OpenAlex

Controlling zeta potential of PDMS surface coated with a layer of PEG is important for electroosmosis and electrophoresis in PDMS made microfluidic chips. Here, zeta potentials of PDMS surfaces modified by simple physisorption of PEG of different concentrations in phosphate buffer solutions, pure water, and PEG solution were reported. Coating PEG on PDMS surfaces was achieved by immersing a PDMS layer into the PEG solution for 10 min and then taking it out and placing it in an oven at 80℃ for 10 h. To avoid damaging the PEG layer on the PDMS surface, an induction current method was employed for zeta potential measurement. Zeta potentials of PEG modified PDMS in electrolyte solutions were measured. The results show that 2.5% PEG can effectively modify PDMS surface with positive zeta potential value in phosphate buffer solutions, pure water and 10% PEG solution. Further increase in PEG solution beyond 5% for surface modification has no obvious effect on zeta potential change.

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 score0.028
Threshold uncertainty score0.982

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 head0.003
GPT teacher head0.167
Teacher spread0.164 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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