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Record W4376619445 · doi:10.1063/5.0151286

Electro-wetting induced dynamic manipulation of symmetrically coalescing viscoelastic liquid bridges

2023· article· en· W4376619445 on OpenAlex
Rahul Roy, Juan S. Marin Quintero, Rajaram Lakkaraju, Prashant R. Waghmare, Suman Chakraborty

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

VenuePhysics of Fluids · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of Alberta
FundersScience and Engineering Research Board
KeywordsDigital microfluidicsMicrofluidicsPhysicsWettingViscoelasticityDrop (telecommunication)UnificationMechanicsPolymerNanotechnologyElectric fieldChemical physicsBiological systemElectrowettingMechanical engineeringThermodynamicsOptoelectronicsComputer scienceDielectric

Abstract

fetched live from OpenAlex

Merging of isolated liquid drops is a common phenomenon that may greatly be influenced by adding polymeric contents to the liquid. Here, we bring out an exclusive control on the dynamics of the intermediate liquid bridge, thus, formed via exploiting the interactions of an exciting electric field with a trace amount of polymeric inclusions present in the intermingling drops. Our results unveil a unique competition of the elastic recovery and time-oscillatory forcing during the drop-unification at early times. However, damped oscillations as a specific signature of the polymer concentration feature eventual stabilization of the bridge at later instants of time. We rationalize these experimental findings in light of a simple unified theory that holds its critical implications in droplet manipulation in a wide variety of applications encompassing digital microfluidics, chemical processing, and biomedical analytics.

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 score0.032
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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