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Record W4287377516 · doi:10.1021/acssuschemeng.2c02678

Nanocellulose Removes the Need for Chemical Crosslinking in Tannin-Based Rigid Foams and Enhances Their Strength and Fire Retardancy

2022· article· en· W4287377516 on OpenAlexafffund
André Luiz Missio, Caio G. Otoni, Bin Zhao, Marco Beaumont, Alexey Khakalo, Tero Kämäräinen, Silvia H. F. da Silva, Bruno D. Mattos, Orlando J. Rojas

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020 Framework ProgrammeCentro Nacional de Pesquisa em Energia e MateriaisFundação de Amparo à Pesquisa do Estado do Rio Grande do SulLaboratório Nacional de NanotecnologiaCanada Foundation for InnovationAcademy of FinlandEuropean CommissionCanada Excellence Research Chairs, Government of CanadaFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsNanocelluloseFurfuryl alcoholMaterials scienceCelluloseTanninComposite materialPolymerizationPyrolysisThermal insulationMicrostructureThermal stabilityChemical engineeringPolymerOrganic chemistryLayer (electronics)ChemistryCatalysis

Abstract

fetched live from OpenAlex

. 25% lighter compared to those produced with formaldehyde, currently known as one of the best performers in chemically coupling tannin and furfuryl alcohol. Compared to the those chemically crosslinked, our CNF-reinforced tannin foams display higher thermal degradation temperature (peak shifted upward, by 30-50 °C) and fire resistance (40% decrease in mass loss). Furthermore, we demonstrate partially hydrophobized CNF to tailor the foam microstructure and derived physical-mechanical properties. In sum, green and sustainable foams, stronger, lighter, and more resistant to fire are demonstrated compared to those produced by formaldehyde crosslinking.

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)0.001
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 score0.013
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.006
GPT teacher head0.224
Teacher spread0.217 · 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

Citations31
Published2022
Admission routes2
Has abstractyes

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