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Record W2606311443 · doi:10.1109/jmems.2017.2688705

MEMS Stabilized Lipid Membranes and Their Parylene Encapsulation

2017· article· en· W2606311443 on OpenAlexfundno aff
Michael Martin, Angela Thompson, Thomas G. Schuhmann, Kevin Walsh, Robert Keynton

Bibliographic record

VenueJournal of Microelectromechanical Systems · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsnot available
FundersOntario Council on Graduate Studies, Council of Ontario UniversitiesUniversity of Louisville
KeywordsMembraneMaterials scienceParyleneMicroscopyLipid bilayerOptical microscopeScanning electron microscopeAnalytical Chemistry (journal)Chemical engineeringChemistryPolymerOpticsComposite materialChromatography

Abstract

fetched live from OpenAlex

Planar lipid membranes composed of egg phosphatidylcholine and 1, 2-dioleoyl-sn-glycero-3-phosphocholine were suspended over arrays of 100, 75, and 30 μm diameter holes in a 3.7-mm-thick polyimide or 2-μm-thick Parylene film that was supported on a silicon frame. Membranes were allowed to thin in buffer and periodically examined in air with reflected light microscopy. Once the membranes approached the thickness of a bilayer, they were vapor coated with 80 nm of conformal Parylene. The Parylene coating essentially fixed the membranes indefinitely for further study via optical microscopy, scanning electron microscopy, energy dispersive x-ray spectroscopy, atomic force microscopy, white-light interferometry, and confocal microscopy. This paper demonstrated that lipid membranes suspended on microfabricated hole arrays lasted over six days in buffer and that thinning membranes go through a phase where the lipids form lamellar stacks of bilayers within the holes. A technique for encapsulating the suspended membranes in Parylene was demonstrated that enabled detailed measurements of membrane geometry and composition.

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 score0.017
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.248
Teacher spread0.236 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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