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Additive Manufacturing of Optical Devices using Inkjet Printing on Optical Nanostructures

2015· article· en· W4378376997 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

VenueTechnical programs and proceedings/Technical program and proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMaterials sciencePixelOptoelectronicsOpticsNanopillarGamutNanotechnologyNanostructurePhysics

Abstract

fetched live from OpenAlex

We present a novel additive strategy to manufacture nano-optical devices using inkjet printing on nanostructured surfaces. To print optically variable devices, silver ink is jetted on the surface of nanopillar arrays to selectively activate or deactivate the structural color pixels. We study the effects of surface chemical properties on inkjet printing and two different printing modes: bright silver mode on hydrophilic surface and dark silver mode on hydrophobic surface. The printed silver film activates the structural color pixels in bright silver mode while deactivates the pixels in dark silver mode. Color images are printed in 120 pixels per inch resolution using both modes. 27 different colors can be achieved from bright silver mode and more than 512 colors from dark silver mode. The color images printed with bright silver mode show high color contrast owing to the index matching process that deactivates unwanted pixels. Color images printed from dark silver mode exhibit brighter colors owing to the high grating efficiency but lower color saturation due to the difficulty in completely deactivating pixels.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.028
GPT teacher head0.263
Teacher spread0.235 · 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