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Record W4390401085 · doi:10.1021/acs.nanolett.3c03856

Damage-Tolerant Wood Layers for Corrosion Protection of Metal Structures

2023· article· en· W4390401085 on OpenAlexaff
Sicen Yu, Yu Liu, Qiongyu Chen, Xiaolu Yu, Gayea Hyun, Shen Wang, Yuhang Ye, Jiaqi Feng, Zheng Chen, Feng Jiang, Joseph A. King, Teng Li, Liangbing Hu, Ping Liu

Bibliographic record

VenueNano Letters · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of British Columbia
FundersAdvanced Research Projects Agency - Energy
KeywordsCorrosionMaterials scienceUltimate tensile strengthComposite materialVeneerMetallurgy

Abstract

fetched live from OpenAlex

Mechanically strong and damage-tolerant corrosion protection layers are of great technological importance. However, corrosion protection layers with high modulus (>1.5 GPa) and tensile strength (>100 MPa) are rare. Here, we report that a 130 μm thick densified wood veneer with a Young's modulus of 34.49 GPa and tensile strength of 693 MPa exhibits both low diffusivity for metal ions and the ability of self-recovery from mechanical damage. Densified wood veneer is employed as an intermediate layer to render a mechanically strong corrosion protection structure, referred to as "wood corrosion protection structure", or WCPS. The corrosion rate of low-carbon steel protected by WCPS is reduced by 2 orders of magnitude than state-of-the-art corrosion protection layers during a salt spray test. The introduction of engineered wood veneer as a thin and mechanically strong material points to new directions of sustainable corrosion protection design.

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 score0.009
Threshold uncertainty score0.353

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 head0.031
GPT teacher head0.268
Teacher spread0.237 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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