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Record W2066480700 · doi:10.1177/1099636213515507

Effect of grain size on the optimal architecture of electrodeposited metal/polymer microtrusses

2014· article· en· W2066480700 on OpenAlex

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

VenueJournal of Sandwich Structures & Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsNanocrystalline materialGrain sizeMaterials scienceCrystallitePolymerNickelMetalComposite materialMetallurgyNanotechnology

Abstract

fetched live from OpenAlex

Nanocrystalline microtruss materials are novel cellular hybrids of metal and polymer produced by electrodepositing thin coatings of nanocrystalline metal over rapid prototyped polymer preforms. This study develops an optimisation method for the architectural design of electrodeposited metal/polymer composite microtrusses used as cores in sandwich beams. For an optimally designed structure employing conventional polycrystalline nickel, a direct substitution of nanocrystalline nickel will improve structural performance; however, it is likely that the structure will also become significantly sub-optimal. Achieving optimal design with nanocrystalline nickel entails large geometric changes from the conventional polycrystalline case. The same applies if the polymer preform is removed after electrodeposition. The strong connection between optimal architecture and grain size was therefore examined for the limiting cases of polymer-filled and hollow microtrusses. It was found that grain size reduction was more important than polymer preform removal such that grain size effects dominate over the majority of microtruss design space.

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)0.001
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 score0.003
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.002
GPT teacher head0.193
Teacher spread0.191 · 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