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Record W2977759039 · doi:10.1115/1.4045007

Repeatable and High-Efficiency Patterning of Silver Nanowires on PDMS Using Micromilled Aluminum Molds

2019· article· en· W2977759039 on OpenAlexaff
Rick Helgason, Ayan Choudhury, Yongjun Lai

Bibliographic record

VenueJournal of Micro and Nano-Manufacturing · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsPolydimethylsiloxaneMaterials scienceMoldNanowireNanotechnologyAluminiumComposite material

Abstract

fetched live from OpenAlex

Abstract We present an inexpensive, repeatable, and efficient method of patterning silver nanowires onto polydimethylsiloxane (PDMS) using a mold-based approach. A micromilling machine is used to prepare an aluminum mold with a raised pattern so that PDMS cured in these molds is imprinted with the design. A solution of silver nanowires and ethanol can then be injected into the pattern. This method can be used to pattern silver nanowires onto PDMS in any two-dimensional (2D) layout, meaning it can be extended to produce a wide range of PDMS/silver nanowire-based sensors and devices. We demonstrate this by the development of two separate patterns. An intricate logo is developed in order to demonstrate the capability of patterning curved and sharp edges, and a strain gauge is developed in order to demonstrate a functional device.

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 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 score0.044
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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