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Modification of Wheat Straws through Steam Explosion and its Effect on Panel Properties

2010· article· en· W2082158978 on OpenAlexaff
Shun Xin Fu, Guang Ping Han, Wan Li Cheng, James Deng

Bibliographic record

VenueAdvanced materials research · 2010
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsFPInnovations
Fundersnot available
KeywordsSteam explosionStrawMaterials scienceComposite materialAbsorption of waterUrea-formaldehydeSwellingFlexural strengthBonding strengthBond strengthPulp and paper industryAdhesiveChemistry

Abstract

fetched live from OpenAlex

Particleboards bonded with urea formaldehyde (UF) and a combination of UF and methylene diphenyl diisocyanate (MDI) resins were manufactured using wheat straws modified through different steam explosion treatments. The effect of steam explosion treatment, resin system, and mixing ratios of wood and straw materials on the panel properties was investigated. Generally, with steam explosion treatment of straw fibers, panel properties in bending and internal bond (IB) strength increased, thickness swelling (TS) and water absorption (WA) decreased. The bending properties for the panels made from the straws pretreated with 12 h water-soaking were significantly improved at the UF/MDI content level of 5%/1%. The IB strength dramatically increased, while TS and WA decreased with the use of dual resins. With increased weight ratio of wheat straw to wood particles, the bending properties, IB strength, TS and WA deteriorated. Pure wheat straw boards had the lowest properties. Wood particles in substitution for part of straw materials helped improve the panel properties. The results demonstrated that steam explosion modification and/or its combination with UF/MDI dual resin system can be a feasible approach to improve the bonding strength for wheat straw based particleboards.

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)0.001
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 score0.002
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.104
GPT teacher head0.369
Teacher spread0.265 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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