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Preparation and characterization of hydroxyethyl cellulose/nanolignin composite films

2024· article· en· W4401875010 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

VenueBioResources · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSynthesis and properties of polymers
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceCharacterization (materials science)CelluloseComposite numberComposite materialHydroxyethyl celluloseChemical engineeringPolymer scienceNanotechnologyEngineering

Abstract

fetched live from OpenAlex

Hydroxyethyl cellulose/nanolignin composite films were prepared and characterized. The composite films were produced via casting of synthesized nanolignin added to hydroxyethyl cellulose at different concentrations (2.5%, 5%, 10%, and 20% by mass). A control film without nanolignin was also prepared for comparison. The thermal properties of the composite films were examined by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), while the mechanical properties were determined by tensile testing and the surface properties were determined by water contact angle measurements. In addition, the morphologies of the samples were examined by scanning electron microscopy (SEM). It was observed that with the addition of nano lignin, the glass transition temperature of the composite films increased from 109 °C to 262 °C; the elongation at break increased from 19% to 51%; and the contact angles increased from 53 °C to 73 °C. The results showed that the presence of nanolignin produced materials being more flexible and more hydrophobic with higher glass transition temperatures.

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)0.000
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 score0.007
Threshold uncertainty score0.296

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 head0.011
GPT teacher head0.230
Teacher spread0.220 · 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