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Structural Characterization of Wood-Polymer Composite Prepared by <i>In Situ</i> Polymerization of Styrene

2011· article· en· W1968610583 on OpenAlexaff
Yongfeng Li, Yi Xing Liu, Xiu Rong Li, Jian Li, Xiang Ming Wang

Bibliographic record

VenueMaterials science forum · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsFPInnovations
FundersNational Natural Science Foundation of China
KeywordsMaterials sciencePolystyreneStyrenePolymerPolymerizationComposite numberComposite materialFourier transform infrared spectroscopyIn situ polymerizationPorosityRadical polymerizationChemical engineeringPolymer chemistryCopolymer

Abstract

fetched live from OpenAlex

A novel biomass composite, wood-polymer composite with potential good mechanical properties, durability and environmental characteristic capable of applying in fields of construction and traffic as structural material, was prepared by generating high-performance polymer from in situ polymerization of styrene in wood porous structure. The vinyl monomer, styrene, combing with 1wt% AIBN as an initiation, was first impregnated into wood porous structure under a vacuum/pressure condition, and then in situ polymerized through a thermal treatment. The structure of the resultant composite was characterized by SEM, FTIR and XRD. Results show that styrene polymerized into polystyrene through free radical polymerization, and the resultant polymer fully filled in wood porous structure in a column form, partly contacting wood matrix. FTIR analysis indicates that polystyrene generated into wood pores, indicating weak interaction between them. XRD analysis suggests that the polystyrene generated into wood pores mainly existed in an amorphous form.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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