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Record W3045810098 · doi:10.1186/s10086-020-01899-8

Effect of high-temperature saturated steam treatment on the physical, chemical, and mechanical properties of moso bamboo

2020· article· en· W3045810098 on OpenAlexaff
Xinzhou Wang, Dali Cheng, Xianai Huang, Lulu Song, Wenli Gu, Xingyu Liang, Yanjun Li, Bin Xu

Bibliographic record

VenueJournal of Wood Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsNatural Resources Canada
FundersNational Natural Science Foundation of China
KeywordsBambooPhyllostachysMaterials scienceYoung's modulusComposite materialSuperheated steamEquilibrium moisture contentChemistryAdsorptionBoiler (water heating)Waste managementOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Moso bamboo (Phyllostachys heterocycla (Carr.) Mitford cv. Pubescens) was subjected to heat treatment with saturated steam at 140, 160, and 180 °C for 10–30 min to evaluate the effects of the treatment on the properties of bamboo. The changes in the physical, mechanical, and chemical properties as well as microscopic structures of moso bamboo were analyzed in this study. Results indicated that its equilibrium moisture content (EMC) decreased gradually after being treated by high-temperature saturated steam mostly due to the hemicelluloses’ degradation. Both the temperature and duration of treatment affect the mechanical properties of bamboo. The modulus of elasticity (MOE) of bamboo was prone to be affected by heat treatment with saturated steam as compared to the modulus of rupture (MOR). The MOR and MOE increased as compared to the control when the sample was treated at 140 °C. However, a severe treatment negatively affects the mechanical properties of bamboo. The MOR and MOE decreased by 47% and 20% as compared to the control when the sample was heated at 180 °C for 30 min.

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 head0.022
GPT teacher head0.218
Teacher spread0.196 · 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".

Quick stats

Citations89
Published2020
Admission routes1
Has abstractyes

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