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Record W2773982084 · doi:10.1080/19475411.2017.1409822

Flow velocity and temperature sensing using thermosensitive fluorescent polymer seed particles in water

2017· article· en· W2773982084 on OpenAlexaff
Filippo Cellini, Sean D. Peterson, Maurizio Porfiri

Bibliographic record

VenueInternational Journal of Smart and Nano Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Waterloo
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsOffice of Naval ResearchNational Science Foundation
KeywordsMicroscale chemistryFlow velocityParticle image velocimetryMaterials sciencePolymerFlow (mathematics)VelocimetryTemperature measurementParticle (ecology)FluorescenceFluid dynamicsMechanicsOpticsThermodynamicsComposite materialPhysicsTurbulence

Abstract

fetched live from OpenAlex

Particle image velocimetry (PIV) is an experimental technique that uses microscale particles as tracers to measure the velocity of a fluid flow. In this paper, we seek to extend this technique to simultaneously measure fluid temperature as well, by employing a novel class of thermosensitive polymer particles. Towards this aim, we designed a process to encapsulate highly fluorescent thermosensitive NBD-AE-co-poly(N-isopropylacrylamide) polymers into optically transparent poly(dimethylsiloxane) particles. These novel PIV particles enable direct measurement of water velocity while serving as temperature probes that increase their fluorescence intensity when the temperature rises above 32 °C. To demonstrate the ability of the particles to simultaneously serve as flow tracers and temperature sensors in water, we examine the flow velocity and temperature in the wake of a heated cylinder in a cross flow. Our results indicate the possibility of extending PIV to afford the spatial and temporal resolution of fluid velocity and temperature gradients in water, with potential application to the study of convection problems from life sciences to engineering.

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)0.001
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 score0.033
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.021
GPT teacher head0.268
Teacher spread0.246 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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