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Record W2020178829 · doi:10.1021/ie050635c

Enhancing Wet Cellulose Adhesion with Proteins

2005· article· en· W2020178829 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCelluloseAdhesionLysineAmine gas treatingChemistryWet strengthGraftingChemical engineeringAmino acidPolymer chemistryMaterials scienceOrganic chemistryPolymerBiochemistry

Abstract

fetched live from OpenAlex

Twenty proteins were compared as potential paper wet strengthening additives by measuring the peel force required to delaminate wet, regenerated cellulose films laminated with a thin (3 mg/m 2 ) protein layer. Wet adhesion results ranged over nearly an order of magnitude, reflecting the importance of protein composition. The proteins with the highest contents of lysine and arginine gave the strongest adhesion with secondary contributions from hydroxyl and phenolic amino acid residues. Wet adhesion was performed with TEMPO oxidized cellulose and with laminates that were cured at high temperatures (120 °C), suggesting that protein grafting to the cellulose and protein cross-linking was important for good wet strength. Although none of the protein laminates was as strong as polyvinylamine or a commercial PAE resin used in the paper industry, this paper suggests that increasing the primary amine (amino group) content as well as optimizing heat-induced bond formation may someday lead to a protein-based paper wet strength resin.

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.

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 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 score0.049
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.051
GPT teacher head0.268
Teacher spread0.216 · 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