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Record W2021529454 · doi:10.1021/bm0492529

Covalent Immobilization of Cellulose Layers onto Maleic Anhydride Copolymer Thin Films

2005· article· en· W2021529454 on OpenAlexaff
Uwe Freudenberg, Stefan Zschoche, Frank Simon, Andreas Janke, Kati Schmidt, Sven H. Behrens, Helmut Auweter, Carsten Werner

Bibliographic record

VenueBiomacromolecules · 2005
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCelluloseCopolymerMaleic anhydrideMaterials sciencePolymer chemistryCovalent bondAqueous solutionChemical engineeringChemistryOrganic chemistryPolymerComposite material

Abstract

fetched live from OpenAlex

Thin films of cellulose are advantageous for analytical studies in aqueous environments to investigate various factors determining the performance of cellulose-based products. However, the weak fixation of cellulose layers on common carrier materials often limits this approach. To address this problem, we suggest a novel maleic anhydride copolymer precoating technique which allows for the covalent attachment of cellulose thin films through esterification. Maleic anhydride copolymers were deposited and covalently bound onto planar, aminosilane-modified glass or silicon oxide surfaces. Cellulose was subsequently immobilized on top of the copolymer precoatings by spin coating from N-methylmorpholine-N-oxide/dimethyl sulfoxide solutions. The resulting cellulose films were thoroughly characterized with respect to layer thickness, morphology, chemical constitution, and electrical charging. The stability of the layers against shear stress was demonstrated in aqueous solutions and the covalent attachment of the cellulose to the copolymer films was proven by means of dissolution experiments followed by ellipsometry and high-resolution X-ray photoelectron spectroscopy.

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 score0.004
Threshold uncertainty score0.998

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)0.0010.000

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 head0.016
GPT teacher head0.281
Teacher spread0.265 · 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

Citations67
Published2005
Admission routes1
Has abstractyes

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