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Record W2038616211 · doi:10.1080/02773813.2012.751427

Investigation of Structural Changes of Alkaline-extracted Wood Using X-ray Microtomography and X-ray Diffraction: A Comparison of Microwave versus Conventional Method of Extraction

2013· article· en· W2038616211 on OpenAlexaff
Suhara Panthapulakkal, Mohini Sain

Bibliographic record

VenueJournal of Wood Chemistry and Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCrystallinityExtraction (chemistry)PorosityCelluloseChemistryComposite materialMicrowaveX-ray crystallographyX-rayMaterials scienceLigninDiffractionChromatographyOptics

Abstract

fetched live from OpenAlex

Abstract The effect of extraction of wood components on wood anatomy and cellulose crystallinity was studied using X-ray computed microtomography (micro CT) and X-ray crystallography. Micro CT of the xylem vessels of birch wood samples was used for the quantitative determination of the wood porosity after conventional and microwave extraction. The method was also used as an indirect means for the determination of temperature generated inside the fibers. Original porosity of birch wood was 18.5 ± 1.5%, and porosity increased with both extractions. The increase in wood porosity after 10 minutes of microwave extraction was double that of wood after conventional method of extraction at 90°C for two hours (42% vs. 26%), indicating a sudden rupture of the wood structure due to the volumetric heating effect produced by the microwave heating. Comparison of porosities of the conventionally extracted samples at different temperatures for the same duration indicated that the temperature generated inside the fibers during 10 minutes of microwave extraction is around 120°C. Crystallinity of cellulose did not change after both extractions, suggesting that the extraction did not affect the strength of the fibers.

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 score0.016
Threshold uncertainty score0.427

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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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