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Record W2323388387 · doi:10.1021/jp301735c

Laser-Generated Au–Ag Nanoparticles For Plasmonic Nucleic Acid Sensing

2012· article· en· W2323388387 on OpenAlexaff
Anne‐Marie Dallaire, David Rioux, Alexandre Rachkov, Sergiy Patskovsky, Michel Meunier

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBiosensorMaterials scienceSurface plasmon resonanceColloidal goldPlasmonOligonucleotideNanoparticleLaser ablationNucleic acidNanotechnologyAptamerLaserDNAChemistryOptoelectronicsOptics

Abstract

fetched live from OpenAlex

The development of nanoplasmonic sensing approaches for DNA detection based on the localized plasmonic properties of different metallic NPs fabricated by femtosecond laser ablation with an application for synthetic oligonucleotides as specific probes for genetic sequence variations is presented. The planar surface plasmon resonance (SPR) technique has been used to test oligonucleotide probes specific to rpoB genes of Mycobacterium tuberculosis . Optimal experimental conditions providing efficiency of hybridization between immobilized probe and cDNA target and performance of the SPR method were obtained and applied to the nanoplasmonic biosensing based on colloidal nanoparticles. Gold and silver/gold alloy nanoparticles were fabricated by the “pure” laser ablation method and have shown faster conjugation to thiol-modified DNA and higher stability in hybridization buffer than nanoparticles produced by chemical synthesis. Nanoparticle-enhanced and spectral SPR methods were used to confirm the efficiency of DNA-modified laser-generated gold nanoparticles in biosensing. Numerical estimation shows a higher sensitivity of nanoalloy materials application in dimer aggregate configurations. The described approaches could be proposed as a basis for an optical biosensor for sensitive and real-time detection of nucleic acid samples, for example, nucleotide sequences related to drug-resistant tuberculosis.

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 score0.002
Threshold uncertainty score0.366

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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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

Citations38
Published2012
Admission routes1
Has abstractyes

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