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Record W4408378523 · doi:10.1016/j.jobab.2025.03.001

Self-densified super-strong wood

2025· article· en· W4408378523 on OpenAlexvenueno aff
Dafang Huang, Jie Li, Suiyi Li, Jianbing Hu, Zhiru Cao, Yang Guo, Yu Ding, Mingwei Zhu, Yan‐Feng Chen

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersNational Key Research and Development Program of China Stem Cell and Translational ResearchNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMaterials scienceComposite materialPolymer sciencePulp and paper industryEngineering

Abstract

fetched live from OpenAlex

Lightweight structural materials with high strength and toughness are highly desirable for many advanced applications. Wood, as a sustainable structural material, has been widely used in engineering due to its abundant availability and excellent mechanical properties. In this paper, we report a self-densification strategy to develop super-strong wood by reassembling orderly arranged wood fibers as functional units and self-densified without the need for hot pressing. The resulting self-densified wood exhibits ultra-high tensile strength (496.1 MPa), flexural strength (392.7 MPa) and impact toughness (75.2 kJ/m 2 ), surpassing those of compressed densified wood and traditional metal materials like aluminum alloys. Notably, the self-densified wood exhibits uniform shrinkage in the cross-section while maintaining its longitudinal dimension. This characteristic leads to an order-of-magnitude enhancement in the overall mechanical performance of the wood, presenting a significant advantage over compressed densified wood. Such super-strong yet lightweight wood has great potential for application as a sustainable engineering material, replacing traditional structural materials such as metals and alloys.

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 score0.132
Threshold uncertainty score0.350

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 communication0.0000.000
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 head0.013
GPT teacher head0.267
Teacher spread0.253 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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