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Record W2969386438 · doi:10.1002/adom.201900669

Assembly of Topographical Micropatterns with Optoelectronic Tweezers

2019· article· en· W2969386438 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Optical Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
FundersCanada First Research Excellence FundUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicrofabricationMaterials scienceTweezersNanotechnologyMicrofluidicsPDMS stampRange (aeronautics)Optical tweezersOptoelectronicsFabricationOptics

Abstract

fetched live from OpenAlex

Abstract Topographical micropatterns (TMPs), or ordered arrays of 3D features on a flat surface, have become important for a wide range of applications. A new optofluidic method based on optoelectronic tweezers to assemble TMPs from suspensions of microparticles in fluid is reported. After assembly, TMPs can be freeze‐dried and then transferred to alternate substrates. 3D simulations are carried out to clarify the experimental results and techniques are developed to evaluate pattern‐transfer fidelity, which is found to be >90% for a wide range of different structures. The optofluidic assembly method described here is facile and accessible, suggesting utility for a wide range of microfabrication and microassembly applications in the future.

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 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 score0.006
Threshold uncertainty score0.514

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 head0.003
GPT teacher head0.188
Teacher spread0.185 · 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