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Record W2913791609

Metallization and Characterization of Nanocrystalline Nickel Coated Jetted Photopolymer Structures

2018· dissertation· en· W2913791609 on OpenAlexfundno aff
Adam Allan Yaremko

Bibliographic record

VenueTSpace · 2018
Typedissertation
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDivision of Materials ResearchUniversity of Toronto
KeywordsMaterials scienceNickelPhotopolymerNanocrystalline materialPolymerCoatingComposite materialMetallizingCore (optical fiber)MetalMetallurgyNanotechnologyPolymerization
DOInot available

Abstract

fetched live from OpenAlex

Nanocrystalline nickel can be electrodeposited on a structurally efficient polymer core to create lightweight load bearing structures. The polymer core is made by jetted photopolymer, a type of additive manufacturing that can produce polymer parts of almost any geometry with a high degree of feature resolution. Electrodeposition has the advantage of being able to coat complex structures in metal since it does not rely on coating apparatus line-of-sight. However, the electrodeposition of nickel on a polymer core is challenging because of surface anisotropy effects and the polymer metallization step. In this study, a technique for coating jetted photopolymer parts with nanocrystalline metal is developed and its complications are explored. Simple polymer parts including rods, tensile coupons, and microtrusses are characterised and mechanically tested to investigate the capabilities of the developed metallizing technique for producing high performance hybrid structures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.244
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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