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Record W2891643993 · doi:10.1088/1361-6528/aae283

Hierarchical metal/polymer metamaterials of tunable negative Poisson’s ratio fabricated by initiator-integrated 3D printing (i3DP)

2018· article· en· W2891643993 on OpenAlexafffund
Dongxing Zhang, Junfeng Xiao, Wangli Yu, Qiuquan Guo, Jun Yang

Bibliographic record

VenueNanotechnology · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceTransfer printingPolymerComposite materialCompressive strength3D printingPoisson's ratioNanocompositeMetamaterialPolymerizationFabricationNanotechnologyOptoelectronicsPoisson distribution

Abstract

fetched live from OpenAlex

Metamaterials with artificially designed architectures can achieve unique and even unprecedented physical properties, which show promising applications in actuators, amplifiers and micromechanical controls. An initiator-integrated 3D printing technology (i3DP) was applied in this study to create scalable, metal/polymer meta-mechanical materials, which can gradually achieve negative Poisson's ratio, high strength and ultralow density, as well as high compressive and super-elastic behavior. The i3DP was enabled by integrating an atomic-transfer radical polymerization (ATRP) initiator with UV-curable resin, followed by polyelectrolyte brushes (PMETAC) grafting via surface-initiated ATRP and thereafter electroless plating to form metal coatings. Compared with polymer structures, the compressive stress of metal-polymer structure can be doubled when deposited with a 190 nm copper layer. The hollow metallic materials possess a tunable Poisson's ratio, and the highest average recoverability, which can recover nearly completely to their original shape after over 30% compression. Overall, this i3DP approach provides meta-structures with substantial benefits from the hierarchical design and fabrication flexibility.

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 categoriesMeta-epidemiology (narrow)
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 score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.224
Teacher spread0.215 · 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.

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

Citations18
Published2018
Admission routes2
Has abstractyes

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