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Record W2888781968 · doi:10.1109/ccece.2018.8447694

High Throughput Core-CBCM CMOS Capacitive Sensor for Life Science Applications

2018· article· en· W2888781968 on OpenAlexaff
Saghi Forouhi, Rasoul Dehghani, Ebrahim Ghafar‐Zadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsCapacitive sensingCMOSElectronic engineeringThroughputComputer scienceSensitivity (control systems)Electrical engineeringTime domainEngineeringWirelessTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a throughput capacitive biosensor using charge based capacitive measurement (CBCM) technique suitable for lab-on-chip (LOC) applications. The proposed biosensor consists of a 10×10 array of core-CBCM capacitive sensors with digital outputs working in current mode. Each capacitive sensor consists of a differential current mirror, a current-controlled oscillator and an up/down counter using linear feedback shift register (LFSR). By the conversion of the current response of the core-CBCM circuit to pulse frequencies and counting the number of the output pulses during a specific time interval, we can do the required integration in the digital domain instead of the analog domain and thus prevent operation in voltage mode. This approach helps to obtain a wide dynamic range more than 100fF (about 80 dB) with a controllable sensitivity about 118 pulse/fF. Based on the simulated results, the proposed sensor offers great advantages for high throughput drug screening applications.

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: none
Teacher disagreement score0.674
Threshold uncertainty score0.360

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 studies0.0000.001
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 head0.027
GPT teacher head0.252
Teacher spread0.225 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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