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Record W3006217703 · doi:10.1021/acs.biomac.9b01733

Cogrinding Wood Fibers and Tannins: Surfactant Effects on the Interactions and Properties of Functional Films for Sustainable Packaging Materials

2020· article· en· W3006217703 on OpenAlexaff
André Luiz Missio, Bruno D. Mattos, Caio G. Otoni, Marina Gentil, Rodrigo Coldebella, Alexey Khakalo, Darci Alberto Gatto, Orlando J. Rojas

Bibliographic record

VenueBiomacromolecules · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoAcademy of FinlandCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorH2020 European Research CouncilFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsAcacia mearnsiiChemistryPulmonary surfactantChemical engineeringTanninCellulose fiberCelluloseProanthocyanidinContext (archaeology)AdsorptionPolyphenolFiberOrganic chemistryMaterials scienceAntioxidantBotany

Abstract

fetched live from OpenAlex

binding of tannins on the freshly exposed cellulosic surfaces. Hence, a tightly bound bicomponent system is produced, which is otherwise not possible if typical adsorption protocols are followed, mainly due to the extensive hydration typical of CNFs. A nonionic surfactant is used to tailor the cellulose-tannin interactions. The proposed strategy not only enables the incorporation of tannins with CNFs but also endows a high and prolonged antioxidant effect of films formed by filtration. Compared to tannin-free films, those carrying tannins are considerably more hydrophobic. In addition, they show selective absorption of ultraviolet light while maintaining optical transparency in the visible range. The proposed simple protocol for incorporating tannins and surfactants with CNFs is suitable to produce functional materials. This is possible by understanding associated interfacial phenomena in the context of sustainable materials within the concept of the circular bioeconomy.

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)0.001
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 score0.002
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.036
GPT teacher head0.265
Teacher spread0.228 · 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

Citations42
Published2020
Admission routes1
Has abstractyes

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