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Record W4298395321 · doi:10.1002/sia.7154

Two different approaches to XPS quantitative analysis of polyelectrolyte adsorption layers

2022· article· en· W4298395321 on OpenAlexaff
Marie Ernstsson, Andra Dédinaité, Orlando J. Rojas, Per M. Claesson

Bibliographic record

VenueSurface and Interface Analysis · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPolyelectrolyteSilanolAdsorptionPolyelectrolyte adsorptionX-ray photoelectron spectroscopyCharge densityIonic strengthSurface chargeChemical engineeringChemistryMicaAqueous solutionMaterials scienceOrganic chemistryPhysical chemistryCatalysisPolymerComposite material

Abstract

fetched live from OpenAlex

X‐ray photoelectron spectroscopy (XPS) was employed to quantify adsorption of polyelectrolytes from aqueous solutions of low ionic strength onto mica, glass, and silica. Silica surfaces were conditioned in base or in acid media as last pre‐treatment step (silica‐base last or silica‐acid last, respectively). Consistency in the determined adsorbed amount, Γ , was obtained independent of the choice of XPS mode and with the two quantification approaches used in the data evaluation. Under the same adsorption conditions, the adsorbed amount, Γ , varied as Γ mica > Γ silica‐base last ≈ Γ glass > Γ silica‐acid last . In addition, the adsorbed amount increased with decreasing polyelectrolyte charge density (100% to 1% of segments being charged) for all substrates. Large adsorbed amount was measured for low‐charge density polyelectrolytes, but the number of charged segments per square nanometer was low due to steric repulsion between polyelectrolyte chains that limited the adsorption. The adsorbed amount of highly charged polyelectrolytes was controlled by electrostatic interactions and thus limited to that needed to neutralize the substrate surface charge density. For silica, the adsorbed amount depended on the cleaning method, suggesting that this process influenced surface concentration and fraction of different silanol groups. Our results demonstrate that for silica, a higher density and/or more acidic silanol groups are formed using base, rather than acid, treatment in the last step.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.316
Teacher spread0.237 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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