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Record W4416866646 · doi:10.1021/acs.biomac.5c01635

Aging of Phosphorylated Cellulose Nanofibers under Moist-Heat Conditions

2025· article· en· W4416866646 on OpenAlexaff
Akemi Sakiyama, Tsuguyuki Saito, Audrey Moores, Masato Kato, Shuji Fujisawa

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersCore Research for Evolutional Science and TechnologyJapan Science and Technology AgencyJapan Society for the Promotion of Science
KeywordsCelluloseNanofiberDephosphorylationPhosphateFibrilHydrolysisPhosphorylationPolymerization

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Understanding the long-term stability of cellulose nanofibers is critical for their practical application in advanced functional materials. In this study, we investigated the aging behavior of phosphorylated cellulose nanofibers (PCNFs) sheets under moist-heat accelerated aging conditions (80 °C, 65% relative humidity (RH)) for up to 42 days. PCNFs with different phosphate group densities were prepared by controlling the phosphorylation time, and their chemical and morphological changes were systematically analyzed. Liquid-sta 31 P NMR revealed a progressive transformation of surface phosphate esters into inorganic phosphate salts during aging. This dephosphorylation was thought to lead to a decrease in the pH within the sheets, which in turn promoted hydrolysis of the cellulose backbone. The resulting degradation manifested as decreases in the degree of polymerization (DP) and fibril length, particularly in PCNFs with higher surface charge. Conversely, the lateral crystallite size of the cellulose increased. These findings provide insights into PCNF aging and highlight the importance of controlling the initial phosphate ester structure and environmental conditions to increase the stability of PCNF-based materials in practical applications.

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 score0.015
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.298
Teacher spread0.287 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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