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Record W2052093452 · doi:10.1021/la052886w

Surface Forces Measurements of Spin-Coated Cellulose Thin Films with Different Crystallinity

2006· article· en· W2052093452 on OpenAlexaff
Shannon M. Notley, Malin Eriksson, Lars Wågberg, Stephanie Beck, Derek G. Gray

Bibliographic record

VenueLangmuir · 2006
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsCelluloseCrystallinitySpin coatingAmorphous solidvan der Waals forceChemical engineeringMaterials scienceIonic bondingContact angleAqueous solutionPolymer chemistryCoatingChemistryComposite materialCrystallographyMoleculeOrganic chemistryIon

Abstract

fetched live from OpenAlex

A systematic study of the surface forces between a cellulose sphere and cellulose thin films of varying crystallinity has been conducted as a function of ionic strength and pH. Semicrystalline cellulose II surfaces and amorphous cellulose films were prepared by spin-coating of the precursor cellulose solutions onto oxidized silicon wafers before regeneration in water. Crystalline cellulose I surfaces were prepared by spin-coating wafers with aqueous suspensions of sulfate-stabilized cellulose I nanocrystals. These preparation methods produced thin, smooth films suitable for surface forces measurements. The interaction with the cellulose I was monotonically repulsive at pH 3.5, 5.8, and 8.5 and at 0.1, 1, and 10 mM ionic strengths. This was attributed to the presence of strongly ionizable sulfur-containing groups on the cellulose nanocrystal surfaces. The amorphous film typically showed a steric interaction up to 100 nm away from the interface that was independent of the solution conditions. A range of surface forces were successfully measured on the semicrystalline cellulose II films; attractive and repulsive regimes were observed, depending on pH and ionic strength, and were interpreted in terms of van der Waals and electrostatic interactions. Clearly, the forces acting near cellulose surfaces are very dependent on the way the cellulose surface has been prepared.

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 score0.003
Threshold uncertainty score0.533

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 head0.040
GPT teacher head0.290
Teacher spread0.249 · 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

Citations74
Published2006
Admission routes1
Has abstractyes

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