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Record W4414564462 · doi:10.1021/acs.biomac.5c01396

Phosphorus–Nitrogen Synergistic Flame-Retardant Cellulose Nanofibers from a Reactive Ternary Deep Eutectic Solvent Containing Guanidine Phosphate

2025· article· en· W4414564462 on OpenAlexaff
Yutong Zhang, Lebin Zhao, Yun Liu, Xujuan Huang, Kaitao Zhang, Henrikki Liimatainen

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsInstitute of Particle Physics
FundersNatural Science Foundation of QingdaoNational Key Research and Development Program of ChinaQingdao Postdoctoral Science Foundation
KeywordsCelluloseNanocelluloseNanofiberLimiting oxygen indexUltimate tensile strengthThermal stabilityPhosphateSolventTernary operation

Abstract

fetched live from OpenAlex

Phosphorylation is considered one of the most effective strategies for enhancing the flame retardancy of cellulose materials. Herein, a pretreatment based on a reactive deep eutectic solvent (RDES) containing guanidine phosphate was developed and used for facilitating cellulose nanofibrillation and synthesizing phosphorylated cellulose nanofibers (P-CNFs) with superior phosphorus-nitrogen synergistic flame-retardancy properties and extremely small average diameter (≈3 nm), eliminating external flame retardants. Compared with unmodified softwood pulp, the peak heat release rate (PHRR) and total heat release (THR) of P-CNFs were reduced by 87.2% and 75.3%, respectively. Moreover, the limiting oxygen index (LOI) of P-CNFs increased to 62.7%. The fabricated P-CNF films exhibited remarkable self-extinguishing behavior and demonstrated outstanding mechanical properties, (maximum tensile strength >188 MPa) and exceptional optical transparency (visible light transmittance >90%). This study presents an innovative and efficient strategy for the development of ecofriendly and flame-retardant nanocellulose materials with enhanced mechanical properties, demonstrating significant potential for fire-safe flexible electronics and transparent coatings.

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 categoriesMeta-epidemiology (narrow)
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 score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.012
GPT teacher head0.268
Teacher spread0.256 · 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.

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

Citations7
Published2025
Admission routes1
Has abstractyes

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