MétaCan
Menu
Back to cohort
Record W2318349221 · doi:10.1115/imece2004-61606

Electrokinetically-Induced Flow Over a Nano-Hole Array Sensor

2004· article· en· W2318349221 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.

Bibliographic record

VenueFluids Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsMicrochannelMicrofabricationMaterials scienceElectrokinetic phenomenaMicrofluidicsNano-Surface plasmon resonancePressure sensorOptoelectronicsNanotechnologyChipFabricationComposite materialNanoparticle

Abstract

fetched live from OpenAlex

A microfluidic device with an embedded surface-plasmon resonance (SPR) sensor was developed. The detector allows real-time monitoring of chemical processes on-chip. Microfabrication of a poly(dimethylsiloxane) chip incorporating a gold-on-glass nano-hole array (SPR sensor) was developed. This was the first time a SPR sensor operating in transmission mode was built in an on-chip format. Electrokinetic transport phenomena in a microchannel over the gold-on-glass plate was modeled using Computational Fluid Dynamics. The modeling results indicated that the flow over the gold-on-glass plate was pressure driven since there was no variation in electrical potential over the conductor (gold). The flow was electrokinetically driven where an electric field was present. The exchange of solutions in the nano-holes was also modeled and it was found that this process took less than a second. This permeation time is expected to scale with the diffusion coefficient. Lastly, preliminary optical measurements were performed to demonstrate the efficiency of the array of nano-holes as on-chip SPR sensors.

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 categoriesMeta-epidemiology (narrow)
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 score0.271
Threshold uncertainty score1.000

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 head0.182
Teacher spread0.178 · 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