MétaCan
Menu
Back to cohort

Plasma enhanced bonding of polydimethylsiloxane (PDMS) with parylene

2011· article· en· W2118957617 on OpenAlexaff
Pouya Rezai, P. Ravi Selvaganapathy, Gregory R. Wohl

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsMcMaster University
Fundersnot available
KeywordsParylenePolydimethylsiloxaneMaterials scienceMicrofluidicsPolymerComposite materialUltimate tensile strengthBond strengthNanotechnologyAdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

PDMS and parylene are among the most widely used polymers in biomedical and microfluidic applications. However, integration of both these materials on a single device has not been possible due to low bond strength achieved using conventional methods. In this paper, we demonstrate plasma-enhanced bonding of PDMS slabs with parylene thin films (from 0.4MPa for PDMS glued assembly to >;1.4MPa tensile strength enhancement) without the usage of high temperature or pressure. This was achieved by systematic investigation of bonding parameters using Taguchi's design of experiment method. The application of this bond has also been demonstrated in PDMS-parylene microfluidic channels.

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 score0.025
Threshold uncertainty score0.391

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 head0.056
GPT teacher head0.234
Teacher spread0.178 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicNeuroscience and Neural EngineeringFrench-language works237,207