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Record W2332152103 · doi:10.1021/la400975b

An Electrochemically Controlled Microcantilever Biosensor

2013· article· en· W2332152103 on OpenAlexafffund
Yoshihiko Nagai, Jorge Dulanto Carbajal, John H. White, Robert Sladek, Peter Grütter, R. Bruce Lennox

Bibliographic record

VenueLangmuir · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsRoyal Victoria HospitalMcGill UniversityMcGill University Health Centre
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchGenome Canada
KeywordsAptamerBiosensorSurface stressOligonucleotideAdsorptionNanotechnologyElectrodeSurface chargeMaterials scienceSquare waveChemistryAnalytical Chemistry (journal)Physical chemistryChromatographyVoltageSurface energyComposite material

Abstract

fetched live from OpenAlex

An oligonucleotide-based electrochemically controlled gold-coated microcantilever biosensor that can transduce specific biomolecular interactions is reported. The derivatized microcantilever exhibits characteristic surface stress time course patterns in response to an externally applied periodic square wave potential. Experiments demonstrate that control of the surface charge density with an electrode potential is essential to producing a sensor that exhibits large, reproducible surface stress changes. The time course of surface stress changes are proposed to be linked to an electrochemically mediated competition between the adsorption of solution-based ions and the single- or double-stranded oligonucleotides tethered to the gold surface. A similar potential-actuated change in surface stress also results from the interaction between an oligonucleotide aptamer and its cognate ligand, demonstrating the broad applicability of this methodology.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.003
GPT teacher head0.242
Teacher spread0.239 · 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

Citations16
Published2013
Admission routes2
Has abstractyes

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