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Record W2105004126 · doi:10.1039/b312812a

Surface-enhanced resonance Raman scattering as an analytical tool for single molecule detection

2004· article· en· W2105004126 on OpenAlexaff
Benissa Tolaieb, Carlos José Leopoldo Constantino, Ricardo F. Aroca

Bibliographic record

VenueThe Analyst · 2004
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Windsor
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsRaman scatteringRaman spectroscopyPeryleneChemistryResonance (particle physics)MoleculeAnalytical Chemistry (journal)Absorption (acoustics)ScatteringSpectroscopyX-ray Raman scatteringInfrared spectroscopyMaterials scienceOpticsAtomic physicsOrganic chemistry

Abstract

fetched live from OpenAlex

A perylene derivative, n-(n-butyl)-n′-(4-aminobutyl) perylene-3,4,9,10-tetracarboxylic acid diimide (simplified as nBu-PTCD-(CH2)4-NH2) has been chosen as the target molecule for studies involving single molecule detection (SMD) using Raman scattering. The enhancement of the Raman signal is the result of the multiplicative effects of two phenomena, resonance Raman scattering (RRS) and surface-enhanced Raman scattering (SERS), which leads to the resulting surface-enhanced resonance Raman scattering (SERRS) process. The SERRS spectra from a single molecule have been collected using both silver and gold colloids. The SMD detection of the fundamental vibrational frequencies characteristic of nBu-PTCD-(CH2)4-NH2 is complemented with the detection of some overtones and combinations from ring stretching modes at the single molecule level. The background characterization of the ensemble vibrational spectroscopy of the target perylene and its SERRS is also presented, which includes the UV-vis absorption, experimental and calculated Raman scattering and infrared absorption, and molecular organization using reflection-absorption infrared spectroscopy (RAIRS).

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 score0.011
Threshold uncertainty score0.266

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 head0.026
GPT teacher head0.276
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

Citations51
Published2004
Admission routes1
Has abstractyes

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