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Record W2911904976 · doi:10.1038/s41598-018-38317-5

Effects of zinc chloride–silicone oil treatment on wood dimensional stability, chemical components, thermal decomposition and its mechanism

2019· article· en· W2911904976 on OpenAlexaff
Zhengbin He, Lijie Qu, Zhenyu Wang, Jing Qian, Songlin Yi

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesBeijing Forestry UniversityChina Scholarship Council
KeywordsZincDecompositionThermal stabilityThermal decompositionSilicone oilChlorideSiliconeChemical engineeringMechanism (biology)PyrolysisChemistryMaterials scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The hygroexpansion and anisotropy of wood limit its application in construction and wood products industry. Zinc chloride-silicone oil was use to decrease the hygroscopicity and improve the dimensional stability of wood at 80 °C, 140 °C, 160 °C and 180 °C. The effects of the treatment on the dimensional stability, chemical structure, thermal degradation, morphology of wood were evaluated, and the mechanism was determined. Results indicated that the zinc chloride-silicone oil treatment at 80 °C improved the dimensional stability and decreased the hygroscopicity of wood. The tangential, radial, and volumetric swelling coefficients of the treated wood decreased by 9.7%, 33.5%, and 18.2%, respectively, relative to those of the untreated wood. Zinc chloride-silicone oil treatment also changed the chemical structure of wood by degrading the wood components and decreasing the moisture absorption groups. Moreover, zinc chloride-silicone oil treatment significantly influenced the thermal degradation of wood, as samples treated with zinc chloride-silicone oil at 140 °C, 160 °C and 180 °C presented sharp peaks around 511 °C, 501 °C and 473 °C. The control group exhibited a more common derivative thermogravimetric curve with a sharp peak at 375 °C. In addition, the silicone oil could impregnate wood, occlude moisture passage, and prevent the movement of moisture in wood. This method can be applied in building and wood industries to expand the applications of wood products.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.591

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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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

Citations41
Published2019
Admission routes1
Has abstractyes

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