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Record W2006289574 · doi:10.1038/srep02730

An impedance-based integrated biosensor for suspended DNA characterization

2013· article· en· W2006289574 on OpenAlexaff
Hanbin Ma, Richard W. R. Wallbank, Reza Chaji, Jiahao Li, Yuji Suzuki, Chris D. Jiggins, Arokia Nathan

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersMiddlesex University
KeywordsBiomoleculeBiosensorDielectric spectroscopyDielectricCharacterization (materials science)Materials scienceDNAElectrical impedanceNanotechnologyBiological systemAnalytical Chemistry (journal)ChemistryOptoelectronicsElectrodeChromatographyBiologyPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Herein, we describe a novel integrated biosensor for performing dielectric spectroscopy to analyze biological samples. We analyzed biomolecule samples with different concentrations and demonstrated that the solution's impedance is highly correlated with the concentration, indicating that it may be possible to use this sensor as a concentration sensor. In contrast with standard spectrophotometers, this sensor offers a low-cost and purely electrical solution for the quantitative analysis of biomolecule solutions. In addition to determining concentrations, we found that the sample solution impedance is highly correlated with the length of the DNA fragments, indicating that the sizes of PCR products could be validated with an integrated chip-based, sample-friendly system within a few minutes. The system could be the basis of a rapid, low-cost platform for DNA characterization with broad applications in cancer and genetic disease 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 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: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.502

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)0.0000.000

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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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

Citations62
Published2013
Admission routes1
Has abstractyes

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