MétaCan
Menu
Back to cohort

Thermoregulated Microvalve for Self-Adaptive Microfluidic Cooling

2018· preprint· en· W2907665855 on OpenAlexaff
Amrid Amnache, Louis-Michel Collin, Gerard Laguna, Montse Vilarrubí, Simon Hamel, Jérôme Barrau, Luc G. Fréchette

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMicroelectronicsMicrofluidicsMaterials scienceFluidicsContext (archaeology)Pressure dropDrop (telecommunication)Volumetric flow rateMechanical engineeringOptoelectronicsComposite materialNanotechnologyMechanicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This work aims at developing a passive and thermoregulated microvalve in order to manage a local and adaptive cooling for microelectronics. Such microvalve can serve as flow regulators in an array of liquid cooling cells below a chip. Two Ag valves of 2000 and 1733 μm long each are designed, microfabricated and experimentally tested to validate their proper use in this cooling context. The 1733 μm valve opening has shifted from 2 to 24.7 μm, when exposed to a hot plate at room temperature and at 80 °C respectively, demonstrating a significant fluidic crosssection shift. The valve also stayed in its elastic deformation range up to 160 °C. Finally, the 1733 μm microvalve demonstrated that it was possible to regulate water flow rate from <;0.5 to 3.0 ml/min at a constant pressure drop of 5 kPa. This makes the microvalve a suitable organ for thermoregulating the cooling cells matrices.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.226
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicHeat Transfer and OptimizationFrench-language works237,207