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Record W2105955725 · doi:10.1039/c1lc20623k

Parylene C coating for high-performance replica molding

2011· article· en· W2105955725 on OpenAlexafffund
Kevin A. Heyries, Carl L. Hansen

Bibliographic record

VenueLab on a Chip · 2011
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsParyleneMaterials scienceFabricationMicrofabricationCoatingPDMS stampPolymerSoft lithographyMolding (decorative)WaferChemical vapor depositionNanotechnologyMoldNanometrePhotolithographySiliconConformal coatingComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

This paper presents an improvement to the soft lithography fabrication process that uses chemical vapor deposition of poly(chloro-p-xylylene) (parylene C) to protect microfabricated masters and to improve the release of polymer devices following replica molding. Chemical vapor deposition creates nanometre thick conformal coatings of parylene C on silicon wafers having arrays of 30 μm high SU8 pillars with densities ranging from 278 to 10,040 features per mm(2) and aspect ratios (height : width) from 1 : 1 to 6 : 1. A single coating of parylene C was sufficient to permanently promote poly(dimethyl)siloxane (PDMS) mold release and to protect masters for an indefinite number of molding cycles. We also show that the improved release properties of parylene treated masters allow for fabrication with hard polymers, such as poly(urethane), that would otherwise not be compatible with SU8 on silicon masters. Parylene C provides a robust and high performance mold release coating for soft lithography microfabrication that extends the life of microfabricated masters and improves the achievable density and aspect ratio of replicated features.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.359

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 head0.024
GPT teacher head0.222
Teacher spread0.199 · 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

Citations20
Published2011
Admission routes2
Has abstractyes

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