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Record W4417042381 · doi:10.1002/adma.202518621

Wood Hydroplasticization Toward Ultra‐Strong and Self‐Densified Complex Structures

2025· article· en· W4417042381 on OpenAlexafffund
Suiyi Li, Yang Wang, Zhangmin Wan, Yingkuan Du, Linghui Qi, Rui Yang, Jianzhang Li, Hongqi Dai, Orlando J. Rojas

Bibliographic record

VenueAdvanced Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of ChinaCanada Foundation for Innovation
KeywordsFormabilityFlexural strengthPolymerElasticity (physics)Material efficiencyDeformation (meteorology)AluminiumMaterial properties

Abstract

fetched live from OpenAlex

Wood has been one of the most widely used sustainable materials for millennia, but its limited mechanical properties and formability have restricted its application in diverse structural contexts. In this study, we elucidate the mechanisms governing the micromechanical behaviors of wood and the strengthening effects achieved through room-temperature hydroplasticization. This process transforms wood into ultra-strong, self-densified structures with customizable shapes, driven by system deformation and energy dissipation. These effects are governed by the interplay between polymer matrix elasticity and interfacial sliding response. Notably, the hydroplasticization method enables the attainment of a high flexural strength (483 MPa), surpassing that of mechanically compressed wood and traditional materials like steel and aluminum alloys. These findings introduce new possibilities for developing complex load-bearing structures that are previously unachievable with conventional wood.

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 score0.075
Threshold uncertainty score0.874

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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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