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Record W2347054149 · doi:10.4071/2016cicmt-wa12

Fabrication of LTCC Micro-fluidic Devices For Wireless Lab-On-A-Chip Applications

2016· article· en· W2347054149 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

VenueAdditional Conferences (Device Packaging HiTEC HiTEN & CICMT) · 2016
Typearticle
Languageen
FieldEngineering
TopicElectrical and Thermal Properties of Materials
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsFabricationMaterials scienceCapacitive sensingMicrofluidicsFluidicsCapacitanceCeramicOptoelectronicsChipLaminationMicroelectromechanical systemsCapacitance probePermittivityDielectricElectrical engineeringElectronic engineeringNanotechnologyCapacitorElectrodeEngineeringComposite materialVoltageLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Low temperature co-fired ceramic (LTCC) based microfluidic sensors have been developed for biomedical and environmental sensing systems. This paper introduces a microfluidic chamber based on impedance spectroscopy measurements using LTCC technology for wireless Lab-On-A-Chip (LOC) applications. To overcome the channel sagging during the fabrication process, we used sacrificial carbon tapes as solid inserts, thus guiding the LTCC to follow their shape upon lamination and then formed micro-channels. The measurement chamber was a parallel-plate capacitive structure with 85 μm gap. This platform requires a small fluid sample of less than 4 μL. The sensor formed by the microfluidic channel and capacitance structure was characterized using different dielectric materials such as air, water and acetone. We hereby present the capability of LTCC-based systems in fluid identification by detecting their electrical permittivity using capacitance measurement.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.999

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)0.0020.000

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 head0.229
Teacher spread0.208 · 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