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Record W1991269668 · doi:10.1115/fedsm-icnmm2010-30222

Nonlinear Localization for Electrowetting-Based Digital Microfluidic Actuation

2010· article· en· W1991269668 on OpenAlexaff
Milad Abolhasani, Kurt D. Devlin, Homayoun Najjaran, Mina Hoorfar, Jonathan F. Holzman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMultiplexerElectrowettingMicrofluidicsMultiplexingDigital microfluidicsVoltageMaterials scienceThreshold voltageNonlinear systemElectronic engineeringComputer scienceOptoelectronicsInterference (communication)NanotechnologyElectrical engineeringEngineeringPhysicsTransistorChannel (broadcasting)Telecommunications

Abstract

fetched live from OpenAlex

The method described in this paper introduces a new multiplexing format for cross-referencing of DMF systems through the simultaneous use of threshold-based voltage actuation (which sets a minimum voltage to initiate droplet motion) and bi-polar voltage activation on the overlying and underlying electrodes. The design makes use of bi-polar voltage activation and threshold effects to eliminate inter-droplet interference and overcome addressability limitations. In the proposed DMF multiplexer structure, these two requirements must both be satisfied for 2-D multiplexed addressability. Experimental characterization of the threshold voltage associated with the first requirement is presented. With regard to requirement two, the bi-polar voltage activation scheme is applied to a fabricated DMF multiplexer, and independent microdroplet motion is shown. The technique can be applied in actuating isolated microdroplets or microdroplet groups (simultaneously) in large-scale/highly-parallel DMF devices.

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: none
Teacher disagreement score0.702
Threshold uncertainty score0.553

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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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