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Record W1794495127 · doi:10.1002/pssa.201431401

Raman tags derived from dyes encapsulated inside carbon nanotubes for Raman imaging of biological samples

2014· article· en· W1794495127 on OpenAlexaff
Nicolas Cottenye, Nathalie Tang, Étienne Gaufrès, Annie Leduc, Jean Barbeau, Richard Martel

Bibliographic record

Venuephysica status solidi (a) · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsRegroupement Québécois sur les Matériaux de PointeUniversité de Montréal
Fundersnot available
KeywordsRaman spectroscopyCarbon nanotubeMaterials scienceNanotechnologyBiological imagingMultispectral imagePolyethylene glycolFluorescenceChemistryOpticsComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

This work reports recent advances in Raman imaging of biological samples tagged with a new contrast agent made of dyes encapsulated inside a single-walled carbon nanotube (dyes@SWNT). By capitalizing on the enhanced cross-section in Raman of the dyes@SWNT, we first demonstrate their use as multispectral Raman tags for imaging. Simple chemical modifications to the nanotubes' sidewall using functionalized polyethylene glycol are shown to provide good dispersion in water media and selective attachment to biological materials. Multicolors tagging, bio-labelling, as well as protein assay applications are also demonstrated. This work presents a thorough characterization of these new tags for biological imaging and highlights new demonstrations of their use in two colors multiplexing. Optical and Raman multiplexed images of Candida albicans (upper left and center, respectively) labelled with two different dyes@SWNT-PEG-NH2 (schematic representation, right), each tag presents a fingerprint band that is specific to the encapsulated dyes (bottom).

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)0.001
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 score0.021
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.310
Teacher spread0.290 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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