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Record W275932941 · doi:10.1007/bfb0114444

Adsorption of block polymers on well-defined silica surfaces

2007· book-chapter· en· W275932941 on OpenAlexaff
Michael L. Hair, Carl P. Tripp

Bibliographic record

VenueSteinkopff eBooks · 2007
Typebook-chapter
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsXerox (Canada)
Fundersnot available
KeywordsPolystyreneCopolymerPolymerMaterials scienceAdsorptionChemical engineeringPolymer adsorptionPolyethyleneOxidePolymer chemistryChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Block copolymers of polyethylene oxide and polystyrene have been examined in the Surface Force Apparatus and their adsorption on mica is well understood. The results of the measurements suggest that these block copolymers can be subdivided into three compositional groups according to Marques/Joanny theoretical elationships. The main focus of the SFA investigations was to determine the extended length of the polymer brush as this in turn is known to affect the flocculation-deflocculation behavior of particles. This paper describes the use of a “colloidal cell” which can be used to prepare dispersions of silica particles in carbon tetrachloride using a totally evacuated system. The surface of this silica has been defined by heat treatment in vacuo. The cell also enables infrared spectroscopy to be applied to the dispersion. The adsorption of the polymer, the specific interactions of the polyethylene oxide and polystyrene segments with the surface and the rate of settling of the particles can be quantified. Block copolymers with three different asymmetries have been examined and the results compared with data obtained for polyethylene oxide and polystyrene homopolymers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.034
GPT teacher head0.272
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2007
Admission routes1
Has abstractyes

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