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Record W3033030720 · doi:10.1002/app.49524

Fabrication of microfluidic chips using controlled dissolution of<scp>3D</scp>printed scaffolds

2020· article· en· W3033030720 on OpenAlexafffund
Tartela Alkayyali, Ali Ahmadi

Bibliographic record

VenueJournal of Applied Polymer Science · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsPolydimethylsiloxaneMaterials scienceFabricationNanotechnologySoft lithographyPDMS stampBiocompatibilityLithographyMicropatterningDissolutionOptoelectronicsChemical engineering

Abstract

fetched live from OpenAlex

Abstract Microfluidic chips are commonly fabricated using soft lithography, which often requires a clean room and micropatterning equipment. Recently, microfluidic chips are increasingly fabricated using 3D printing, but this technology is still limited in smallest channel printability, transparency, supports residue, and biocompatibility. In this work, a simple, fast, and inexpensive step is introduced to fabricate polydimethylsiloxane (PDMS) microfluidic chips using enhanced internal scaffold removal (eISR). It is found that final channel dimension decreases by 0.22 ± 0.02 μm/revolution with a 7% error using eISR. Surface topology is inspected after dissolution using scanning electron microscopy. A T‐junction device, bifurcation channels, and curved channels are fabricated to demonstrate the usability of eISR in multiple applications. Compared to previous methods, eISR provides acrylonitrile–butadiene–styrene dissolution before PDMS casting to achieve thinner and smoother channels produced using a commercial 3D printer.

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)0.001
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 score0.301
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.016
GPT teacher head0.242
Teacher spread0.225 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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