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Record W2332556100 · doi:10.1166/jnn.2009.1469

Combinatorial Polyelectrolyte Multilayer Film Fabrication

2009· article· en· W2332556100 on OpenAlexaff
Matthew M. Coulter, D.S. dos Santos, Richard W. Loo, M. Cynthia Goh

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolyelectrolyteMaterials scienceFabricationLayer by layerAdsorptionChemical engineeringAbsorbanceSalt (chemistry)PolystyreneDeposition (geology)Layer (electronics)NanotechnologyPolymerChromatographyOrganic chemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

A combinatorial strategy for the fabrication of a library of polyelectrolyte multilayer films is presented in this paper. This innovative approach involves the parallel formation of polyelectrolyte multilayer films in the individual wells of polystyrene microtitre plates under various deposition conditions. The progress of film formation was monitored via the intensity of the UV-vis absorbance of one of the depositing polyelectrolytes using a conventional microplate reader. We demonstrate the utility of this technique by building a library of 120 distinct polyelectrolyte multilayer films. Both the primer layer composition and salt content of the polyeletrolyte solutions were systematically varied in the preparation of films of nine bilayers. Film growth did not follow a linear adsorption regime for the first three bilayers regardless of the composition of the primer layer. We observed that increasing the sodium chloride concentration in the polyelectrolyte solutions resulted in increased polyelectrolyte absorption for all the conditions studied. The approach presented here is a convenient method of producing and characterizing multiple films rapidly and reproducibly, making it a valuable tool for optimizing film fabrication.

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 score0.009
Threshold uncertainty score0.323

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 head0.265
Teacher spread0.256 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

Explore more

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