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Record W4312499318 · doi:10.1109/tbcas.2022.3230668

A High Dynamic Range Dual 8×16 Capacitive Sensor Array for Life Science Applications

2022· article· en· W4312499318 on OpenAlex
Hamed Osouli Tabrizi, Saghi Forouhi, Ebrahim Ghafar‐Zadeh

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

VenueIEEE Transactions on Biomedical Circuits and Systems · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsYork University
Fundersnot available
KeywordsCapacitive sensingCMOSMultiplexingDynamic rangeCapacitanceCalibrationElectronic engineeringChipMaterials scienceComputer scienceElectrical engineeringEngineeringElectrodePhysics

Abstract

fetched live from OpenAlex

This paper presents a fully integrated complementary metal-oxide-semiconductor (CMOS) capacitive sensor array for life science applications. This sensing device consists of an array of 16 × 16 interdigitated electrodes (IDEs) integrated with a charge-based readout and multiplexing circuitries on the same chip. This chip was implemented in 0.35 μm AMS CMOS process. This sensing device has a wide input capacitance range (ICR) of about 100 fF and a resolution of 150 aF, and the capability of temporal, spatial, and dielectric sensing. It makes it possible to develop a low-cost, multimodal, calibration-free sensing platform for life science applications. Here, we demonstrate and discuss the functionality and applicability of the proposed sensing device by introducing various chemical solvents including ethanol, methanol, and pure water. The simulation and experimental results achieved in this work have taken us one step closer to a fully automated calibration-free multimodal capacitive sensing platform for high-throughput drug development and other purposes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.241
Teacher spread0.224 · 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