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Record W4414289357 · doi:10.1021/acs.biomac.5c00437

Highly-Flame-Retardant Performance and Sustainable Polyurethane Foams from Industrial Kraft Lignin via Exploiting Lignin Demethylation

2025· article· en· W4414289357 on OpenAlexaff
Tianyuan Xiao, Lu Wu, Qianwei Xu, Xuanjia Yu, Qiu Fu, Fengshan Zhang, Yong Li, Guangming Yin, Lingzhi Huang, Pedram Fatehi, Haiqiang Shi

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
FundersLiaoning Revitalization Talents ProgramNational Natural Science Foundation of China
KeywordsPolyurethaneLigninThermal stabilityPolymerPyrolysisLimiting oxygen indexDemethylation

Abstract

fetched live from OpenAlex

Achieving the enhancement of thermal stability in polyurethane (PU) foams and promoting their green and harmless development have long been both hotspots and challenges in this field. Here, the molecular integration of demethylated lignin and 2NH 2 DOPO was achieved through the Mannich reaction, which were successfully incorporated into the macromolecular network of polyurethane foam. The unique molecular structure and functional groups synergistically enhance both its condensed-phase and gas-phase flame-retardant performances, which endows the PU foam with an astonishing limiting oxygen index (LOI) value as high as 34.5% and enables it to meet the combustion standard of UL-94 V-0. In addition, the incorporation of lignin enhances the rigidity of the polymer network structure and establishes a chemically reversible dynamic network, thereby endowing the foam composites with improved compressive strength. This work provides an innovative approach for coordinating the development of sustainable and flame-retardant PU foams and paves the way for the high-value utilization of lignin.

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 score0.006
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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