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A Simple, Versatile Integration Platform based on a Printed Circuit Board for Lab-on-a-Chip Systems

2021· article· en· W4205153866 on OpenAlexaff
Simon Dallaire, Paul-Vahé Cicek

Bibliographic record

Venue2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMicrofluidicsEmbedded systemPrinted circuit boardSystem on a chipChipComputer scienceProcess (computing)Lab-on-a-chipProcess integrationSimple (philosophy)System integrationRapid prototypingComputer hardwareEngineeringNanotechnologyProcess engineeringMaterials scienceMechanical engineeringTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

This work presents a novel process to embed an active silicon chip into a PCB for integrated microfluidic applications. The integration process is fast, affordable, and makes use of standard equipment and materials. It is ideal for quick prototyping but could also eventually be adapted for mass production since it is compatible with industrial technologies. A significant benefit of the proposed method is the ability to directly combine heterogeneous components, whether they be microfluidic, microelectromechanical, or electronic, within an integrated, compact system. This platform aims to serve as a further step towards realizing intelligent lab-on-a-chip systems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.259
Teacher spread0.215 · 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.

Study designSimulation or modeling
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

Citations1
Published2021
Admission routes1
Has abstractyes

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