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Record W2057174738 · doi:10.1039/c4lc01000k

Rapid discrimination of DNA strands using an opto-calorimetric microcantilever sensor

2014· article· en· W2057174738 on OpenAlexaff
Dongkyu Lee, Kyo Seon Hwang, Seonghwan Kim, Thomas Thundat

Bibliographic record

VenueLab on a Chip · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsBiomoleculeBiosensorDNANucleotideInfrared spectroscopyMaterials scienceAnalytical Chemistry (journal)MoleculeNucleobaseInfraredChemistryNanotechnologyChromatographyBiochemistryOptics

Abstract

fetched live from OpenAlex

A rapid technique for quantitative detection and discrimination of DNA strands without using immobilized probe molecules is demonstrated using an opto-calorimetric, self-powered sensor based on a Pb(Zr(0.52)Ti(0.48))O3 (PZT) microcantilever. Microcalorimetric infrared (IR) spectroscopy provides excellent chemical selectivity based on the unique molecular vibrational characteristics of each nucleotide in the mid IR region. The piezoelectric and pyroelectric properties of the PZT microcantilever were exploited in the quantitative detection and discrimination of adsorbed DNA strands with their spectral characteristics. We report the unique spectral characteristics of different DNA nucleotides that are monitored by wavelength-dependent temperature variations for different relative molar ratio of each nucleotide. This approach offers a fast, label-free technique which is highly sensitive and selective for the detection of single nucleotide differences in DNA strands and has the potential to be used as a rapid prescreening biosensor for various biomolecules.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.441

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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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