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Record W3197954319 · doi:10.1126/sciadv.abi8919

Hygromechanical mechanisms of wood cell wall revealed by molecular modeling and mixture rule analysis

2021· article· en· W3197954319 on OpenAlexaff
Chi Zhang, Mingyang Chen, Sinan Keten, Benoît Coasne, Dominique Derome, Jan Carmeliet

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité de Sherbrooke
FundersOffice of Naval ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsHemicelluloseLigninCelluloseComposite materialMaterials scienceCell wallSwellingFiberPolymerSecondary cell wallNanoscopic scaleMicromechanicsMatrix (chemical analysis)AnisotropyChemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Despite the thousands of years of wood utilization, the mechanisms of wood hygromechanics remain barely elucidated, owing to the nanoscopic system size and highly coupled physics. This study uses molecular dynamics simulations to systematically characterize wood polymers, their mixtures, interface, and composites, yielding an unprecedented micromechanical dataset including swelling, mechanical weakening, and hydrogen bonding, over the full hydration range. The rich data reveal the mechanism of wood cell wall hygromechanics: Cellulose fiber dominates the mechanics of cell wall along the longitudinal direction. Hemicellulose glues lignin and cellulose fiber together defining the cell wall mechanics along the transverse direction, and water severely disturbs the hemicellulose-related hydrogen bonds. In contrast, lignin is rather hydration independent and serves mainly as a space filler. The moisture-induced highly anisotropic swelling and weakening of wood cell wall is governed by the interplay of cellulose reinforcement, mechanical degradation of matrix, and fiber-matrix interface.

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 score0.198
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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

Citations80
Published2021
Admission routes1
Has abstractyes

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