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Record W2323704819 · doi:10.1021/ac2012976

Advances in Surface Plasmon Resonance Sensing with Nanoparticles and Thin Films: Nanomaterials, Surface Chemistry, and Hybrid Plasmonic Techniques

2011· article· en· W2323704819 on OpenAlexafffund
Olivier R. Bolduc, Jean‐François Masson

Bibliographic record

VenueAnalytical Chemistry · 2011
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversité de Montréal
FundersNational Research Council Canada
KeywordsSurface plasmon resonanceNanomaterialsChemistryNanotechnologyNanoparticlePlasmonSurface plasmonPrismMass spectrometryAdsorptionOptoelectronicsMaterials scienceOpticsPhysical chemistryChromatographyPhysics

Abstract

fetched live from OpenAlex

Nanomaterials developed for localized surface plasmon resonance (LSPR) are increasingly integrated to classical prism-based SPR sensors, providing enhanced sensitivity and lower detection limits. The unique properties of these novel nanomaterials in addition to novel surface chemistry to minimize nonspecific adsorption and surface plasmon-coupled techniques with other spectroscopic or mass spectrometry techniques are highlighted in this article.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations106
Published2011
Admission routes2
Has abstractyes

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