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Record W1877081167 · doi:10.1117/1.jbo.20.9.097001

Reflected light microspectroscopy for single-nanoparticle biosensing

2015· article· en· W1877081167 on OpenAlexaff
Sergiy Patskovsky, Michel Meunier

Bibliographic record

VenueJournal of Biomedical Optics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceBiosensorMicroscopyMicrofluidicsMiniaturizationNanoparticleDark field microscopyRefractive indexNanotechnologyOpticsMicroscopeOptical microscopeOptoelectronicsDifferential interference contrast microscopyHolographyScanning electron microscope

Abstract

fetched live from OpenAlex

Conventional and dark-field microscopy in the transmission mode is extensively used for single plasmonic nanoparticle (NP) imaging and spectral analysis. However, application of the transmission mode for realtime biosensing to single NP poses strict limitations on the size and material properties of the microfluidic system. This article proposes a simple optical technique based on reflected light microscopy to perform microspectroscopy of a single NP placed in a conventional, nontransparent liquid delivery system. The insertion of a variable spot diaphragm in the optical path reduces the interference effect that occurs at the NP-substrate interface and improves the signal-to-noise ratio in NP imaging. Using this method, we demonstrated spatial imaging and spectral analyses of 60-, 80-, and 100-nm single gold NPs. A single-NP sensor based on a 100-nm NP was used for real-time measurement of bulk refractive index changes in the microfluidic channel and for detection of fast dynamic poly(ethylene glycol) attachment to the NP surface. Finally, electrochemical single-particle microspectroscopy was demonstrated by using a methylene blue electroactive redox tag. The proposed optical approach is expected to significantly improve the miniaturization and multiplexing capabilities of high-throughput biosensing based on single NP.

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)0.001
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 score0.043
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.295
Teacher spread0.250 · 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
Published2015
Admission routes1
Has abstractyes

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