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Record W2129554562 · doi:10.1088/0960-1317/25/7/075005

Robust thermal control for CMOS-based lab-on-chip systems

2015· article· en· W2129554562 on OpenAlexafffund
Jose Martinez-Quijada, Tianchi Ma, Gordon H. Hall, Matt Reynolds, David Sloan, Saul Caverhill-Godkewitsch, D. Moira Glerum, Dan Sameoto, D.G. Elliott, C. Backhouse

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of WaterlooUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectronicsChipCMOSMicrofluidicsSubstrate (aquarium)Lab-on-a-chipElectronic engineeringIntegrated circuitTemperature controlThermal conductivityElectrical engineeringMaterials scienceThermal massInstrumentation (computer programming)OptoelectronicsThermalEngineeringNanotechnologyMechanical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

The need for precise temperature control at small scales has provided a formidable challenge to the lab-on-chip community. It requires, at once, good thermal conductivity for high speed operation, good thermal isolation for low power consumption and the ability to have small (mm-scale) thermally independent regions on the same substrate. Most importantly, and, in addition to these conflicting requirements, there is a need to accurately measure the temperature of the active region without the need for device-to-device calibrations. We have developed and tested a design that enables thermal control of lab-on-chip devices atop silicon substrates in a way that could be integrated with the standard methods of mass-manufacture used in the electronics industry (i.e. CMOS). This is a significant step towards a single-chip lab-on-chip solution, one in which the microfluidics, high voltage electronics, optoelectronics, instrumentation electronics, and the world-chip interface are all integrated on a single substrate with multiple, independent, thermally-controlled regions based on active heating and passive cooling.

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: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.719

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 head0.015
GPT teacher head0.189
Teacher spread0.174 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

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