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Record W2032975657 · doi:10.1115/fedsm-icnmm2010-31243

Effects of Hydrophobic Recovery of Plasma Treated PDMS Microchannels on Surface Tension Driven Flow

2010· article· en· W2032975657 on OpenAlexaff
Zeyad Almutairi, Carolyn L. Ren, David A. Johnson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
FundersKing Saud University
KeywordsPolydimethylsiloxaneMicrofluidicsSurface tensionElectrokinetic phenomenaMaterials scienceSurface energyNanotechnologyFabricationSurface modificationContact angleComposite materialChemical engineering

Abstract

fetched live from OpenAlex

Surface tension driven flow is used in numerous microfluidic applications. It is considered a passive pumping technique which doesn’t require any external energy, aside from the interfacial surface energy between the fluid and walls. Thus, it is preferred in applications where the goal is fluid and sample transport. In many applications PDMS (Polydimethylsiloxane) is the most adapted material for chip manufacturing in microfluidics. PDMS has several aspects that make it favorable for microfluidic applications. Ease of chip fabrication, cost effectiveness, chemical stability, and good optical properties are features offered by PDMS and desirable for microfluidics. On the other hand, PDMS has some shortcomings. One of importance is that PDMS is naturally hydrophobic. For this reason it is hard to achieve surface tension flow in native PDMS for various fluids used in microfluidics. Thus, native PDMS must be treated to get hydrophilic surface properties. The most used method for altering PDMS properties to a hydrophilic state is by plasma treatment. This treatment has several aspects where it enhances the attachment of PDMS to substrates, it alters the surface from a hydrophobic to a hydrophilic state, and it increases the electrokinetic properties of PDMS. As a result, after plasma treatment surface tension pumping can be achieved in PDMS, unlike native PDMS. However, plasma treatment is not permanent due to the diffusion of non-cured PDMS species to the surface of microchannels, as is well documented in the literature. The change of plasma treated PDMS with time will affect both the electrokinetic and surface tension driven flow. To our knowledge, researchers have quantitatively documented the time effect on plasma treated PDMS microchannels (aging of PDMS) for electrokinetic flow, but not for surface tension driven flow. Therefore, a quantitative examination of the time effect on surface tension driven flow for plasma treated PDMS gives valuable information on both regaining the hydrophobic properties in PDMS and changes in the passive flow conditions. In this work a quantitative study on the hydrophobic recovery for oxygen-plasma treated PDMS and its effects on surface tension flow was examined. The study was performed with a quantitative flow visualization technique (micro particle image velocimetry). It was found that the aging of PDMS will strongly affect surface tension flow of water based solutions in PDMS microchannels. This study gives important information on the effectiveness of surface tension driven flow for oxygen plasma treated PDMS microchannels.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.172
Teacher spread0.169 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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