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Record W2735007708 · doi:10.1002/9781118729588.ch12

Dynamics of Nanolattices: Polymer‐Nanometal Lattices

2017· other· en· W2735007708 on OpenAlex
Craig A. Steeves, G.D. Hibbard, Manan Arya, Ante T. Lausic

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicAnodic Oxide Films and Nanostructures
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanocrystalline materialPolymerLattice (music)Materials scienceFlexibility (engineering)Topology (electrical circuits)NanotechnologyPhysicsMathematicsComposite material

Abstract

fetched live from OpenAlex

It is possible to fabricate lattice structures using polymer 3D printing for convenient generation of complex geometries, enhanced with electrodeposition of nanocrystalline metals for high strength. These hybrid polymer–nanometal structures have excellent mechanical properties relative to their mass when optimally designed. Because of the nearly limitless geometric flexibility made available through 3D printing, such lattices can further be designed to achieve additional functional goals. This chapter examines the use of polymer–nanometal hybrids in conditions where wave propagation is significant. First, the techniques used to fabricate the lattices is described. Second, the dispersive wave propagation properties of the resulting lattices are analysed using Floquet–Bloch methods. Finally, some concepts for linking this analysis to the design of lattices with desirable properties are outlined.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.040
Threshold uncertainty score0.966

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0350.000

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 head0.011
GPT teacher head0.252
Teacher spread0.240 · 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

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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