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Record W2071910361 · doi:10.1109/jssc.2014.2312571

Nanostructured CMOS Wireless Ultra-Wideband Label-Free PCR-Free DNA Analysis SoC

2014· article· en· W2071910361 on OpenAlexaff
Hamed Mazhab Jafari, Karim Abdelhalim, Leyla Soleymani, Edward H. Sargent, Shana O. Kelley, Roman Genov

Bibliographic record

VenueIEEE Journal of Solid-State Circuits · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsCMOSWidebandChipMicrosystemMaterials scienceTransmitterDynamic rangeChopperChannel (broadcasting)Electronic engineeringElectrical engineeringComputer scienceOptoelectronicsVoltageNanotechnologyEngineering

Abstract

fetched live from OpenAlex

A fully integrated 54-channel wireless fast-scan cyclic voltammetry DNA analysis SoC is presented. The microsystem includes 546 3D nanostructured and 54 2D gold DNA sensing microelectrodes as well as 54 pH sensors. Each channel consists of a chopper-stabilized current conveyer with dynamic element matching. It is utilized as the amperometric readout circuit with a linear resolution from 8.6 pA to 350 nA. The on-chip programmable waveform generator provides a wide range of user-controlled rate and amplitude parameters with a maximum scan range of 1.2 V, and scan rate ranging between 0.1 mV/sec to 300 V/sec. A digital ultra-wideband transmitter based on a delay line architecture provides wireless data communication with data rates of up to 50 Mb/sec while consuming 400 µW. The 3 mm × 3 mm prototype fabricated in a 0.13 µm standard CMOS technology has been validated in prostate cancer synthetic DNA detection with 10 aM label-free PCR-free detection limit. Each channel occupies an area of only 0.06 mm 2 and consumes 42 µW of power from a 1.2 V supply.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations60
Published2014
Admission routes1
Has abstractyes

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Same venueIEEE Journal of Solid-State CircuitsSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207