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Record W2981795176 · doi:10.3390/f10110955

Functionalized Surface Layer on Poplar Wood Fabricated by Fire Retardant and Thermal Densification. Part 1: Compression Recovery and Flammability

2019· article· en· W2981795176 on OpenAlexaff
Demiao Chu, Jun Mu, Stavros Avramidis, Sohrab Rahimi, Shengquan Liu, Zongyuan Lai

Bibliographic record

VenueForests · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central Universities
KeywordsFire retardantFlammabilityMaterials scienceComposite materialThermal stabilityCompression (physics)HardnessSurface modificationCone calorimeterFire testLayer (electronics)Fire resistanceNitrogenChemical engineeringPyrolysisChemistry

Abstract

fetched live from OpenAlex

To enhance compression stability and fire retardancy of densified wood, a new modification method i.e., combined nitrogen–phosphorus (NP) fire retardant pre-impregnation with surface thermo-mechanical densification is used to fabricate a certain thickness of functionalized surface layer on poplar. This combined treated wood is investigated via vertical density profile (VDP), and the compression stability is revealed by both soaking test and cone analysis. Results demonstrate that the combined treatment hardened the surface of wood and reformed the interface combination of the NP with the wood cell wall, thus making the surface tissue more close-grained. Fire retardancy was also enhanced; the total heat release and CO generation values decreased by 21.9% and 68.4%, respectively, when compared with that of solely NP-treated wood. Moreover, surface hardness increased by 15.8%, and the recovery of surface hardness and thickness were 56.8% and 77.2% lower than that of simply densified wood. It appears that this NP-involved thermal densification could be considered as an alternative approach to enhance both the compression stability and fire resistance of wood.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.365

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 head0.012
GPT teacher head0.186
Teacher spread0.175 · 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 designObservational
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

Citations21
Published2019
Admission routes1
Has abstractyes

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