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Record W2146125796 · doi:10.1039/c0lc00093k

A fast and simple method to fabricate circular microchannels in polydimethylsiloxane (PDMS)

2010· article· en· W2146125796 on OpenAlexafffund
Mohamed Abdelgawad, Lyndia C. Wu, Wei-Yin Chien, William R. Geddie, Michael A.S. Jewett, Yu Sun

Bibliographic record

VenueLab on a Chip · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
FundersUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsHarvard University
KeywordsPolydimethylsiloxaneMicrochannelCoatingMaterials scienceMicrofluidicsSoft lithographyPDMS stampSurface tensionComposite materialNanotechnologyFabrication

Abstract

fetched live from OpenAlex

A simple method to fabricate circular microchannels in polydimethylsiloxane (PDMS) is presented. A coating of liquid PDMS is applied on the walls of rectangular microchannels, fabricated using standard soft-lithography, by introducing a pressurized air stream inside the PDMS filled microchannels. Surface tension of the liquid PDMS forces the coating to take a circular cross-section which is preserved by baking the device to cure the coated layer. Diameters ranging from a few micrometres to a few hundreds of micrometres were achieved. The method was verified to work on microchannel networks as well as in straight channels. Different coating conditions were systematically tested. Design curves are reported for one to choose appropriate coating conditions for obtaining a desired diameter. A comparison between the performance of square and circular microchannels in trapping SiHa cells (cervical cancer cell line) is shown.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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
GenreMethods

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

Citations108
Published2010
Admission routes2
Has abstractyes

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