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Record W2043778061 · doi:10.1021/am403745k

The Influence of Deposition Solution pH and Ionic Strength on the Quality of Poly(<i>N</i>-isopropylacrylamide) Microgel-Based Thin Films and Etalons

2013· article· en· W2043778061 on OpenAlexaff
Liang Hu, Michael J. Serpe

Bibliographic record

VenueACS Applied Materials & Interfaces · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceIonic strengthPoly(N-isopropylacrylamide)PolymerChemical engineeringMonolayerOverlayerHomogeneity (statistics)Acrylic acidThin filmSubstrate (aquarium)Polymer chemistryCopolymerComposite materialNanotechnologyAqueous solutionChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Poly(N-isopropylacrylamide)-co-acrylic acid (pNIPAm-co-AAc) microgel-based thin films and etalons were fabricated via "painting" a pNIPAm-co-AAc microgel monolayer on a Au-coated substrate, followed by the deposition of another Au overlayer. Herein, in situ observation of how the pH and ionic strength (I.S.) of the painting solution influenced microgel deposition and, ultimately, the optical homogeneity and pH sensitivity of the etalon was carried out. It was shown that microgels closely pack on the Au substrate when they are deposited at pH 3.0, leading to a good optical homogeneity. Additionally, increasing the painting solution I.S. leads to a slight decrease in microgel packing density on the substrate, but enhances the ability of the microgel layer to swell, exhibiting thicker polymer layers when immersed in pH 3.0 solutions. When painting at pH 7.5, the optical homogeneity of the etalon is improved at the expense of swellability, exaggerated high I.S. We also determined the device's sensitivity to pH changes and found a maximum sensitivity when the microgels were deposited at pH 7.5 with an I.S. of 10 mM.

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 score0.002
Threshold uncertainty score0.387

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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations23
Published2013
Admission routes1
Has abstractyes

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