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Record W2967968582 · doi:10.1039/c9lc00364a

Plasma-induced covalent immobilization and patterning of bioactive species in microfluidic devices

2019· article· en· W2967968582 on OpenAlexafffund
Amid Shakeri, Sara M. Imani, Eric Chen, Hanie Yousefi, Raed Shabbir, Tohid F. Didar

Bibliographic record

VenueLab on a Chip · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsMicrofluidicsCovalent bondNanotechnologyChemistryPlasmaBiophysicsMaterials scienceOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Here, we present a straightforward technique to create bio-functional microfluidic channels using CO2 plasma to induce both carboxylic and hydroxyl groups onto the channel surface. Consequently, not only does the surface allow for irreversible covalent bonding to an oxygen plasma treated PDMS for microfluidic device fabrication, but it also provides functionality for biomolecular immobilization. Furthermore, we demonstrate integration of this technique with microcontact printing to covalently micropattern functional biomolecules inside microfluidic channels. The bio-functionality and efficacy of the microcontact printed antibodies is demonstrated for both bioassays as well as patterning and culturing different cell lines. Results show that the introduced method can be an excellent candidate for cell culture studies in microfluidics. With the new printing method, full cell confluency (∼400 cells per mm2) was achieved after incubation for only 1 day, which is significantly greater than other conventional cell culture techniques inside microfluidic devices. As a proof of concept, we demonstrated the endothelial cells functionality by stimulating von Willebrand Factor secretion under shear stress. This is done via perfusion of histamine through the channel and performing immunofluorescence labeling to observe the inflammatory response of the cells. The developed method eliminates the need for wet chemistry and significantly simplifies producing bio-functional chips which can be used for biosensing, organs-on-chips and tissue engineering applications.

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 score0.076
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.203
Teacher spread0.193 · 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

Citations28
Published2019
Admission routes2
Has abstractyes

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