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Record W4407530918 · doi:10.1038/s41598-025-89082-1

A portable and cost-effective system for electronic nucleic acid mass measurement

2025· article· en· W4407530918 on OpenAlexafffund
Abbas Panahi, Firouz Abbasian, Giancarlo Ayala‐Charca, Hamed Osouli Tabrizi, Ahmad Roshanfar, Morteza Ghafar-Zadeh, Yasaman Tahernezhad, Sebastian Magierowski, Ebrahim Ghafar‐Zadeh

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCMC Microsystems
KeywordsNucleic acidComputer scienceComputational biologyBiologyGenetics

Abstract

fetched live from OpenAlex

This paper presents a low-cost, portable sensing platform for rapid DNA mass measurement, addressing a critical need in life science research. The platform features a novel interdigital open-gate junction field-effect transistor (ID-OGJFET) with a large sensing area that converts negatively charged DNA mass into an electrical current. The system enables DNA mass detection in under ten seconds with a resolution of less than 1 µA, demonstrating sensitivity across a range from 0.48 ng to 29.5 ng, achieving a Limit of Detection as low as 1.18-1.25 ng/µL. A custom-designed electronic reader and fluidic sample holder facilitate efficient operation. Simulation studies using molecular dynamics and finite element methods provide further insights into the sensor's DNA detection mechanism. This highly sensitive system is significantly more cost-effective than commercially available semiconductor characterization alternatives. The device's high performance and affordability make it a valuable tool for molecular biology applications, and it holds potential for advancing FET-based sensing instrumentation and measurement research.

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)0.001
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.261
Teacher spread0.253 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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