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Record W2058760662 · doi:10.1039/c4tb01475h

Chitosan-modified silver@ruthenium hybrid nanoparticles: evaluation of physico-chemical properties and bio-affinity with sialic acid

2014· article· en· W2058760662 on OpenAlexafffund
Murugan Veerapandian, X. X. Zhu, Suzanne Giasson

Bibliographic record

VenueJournal of Materials Chemistry B · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsRutheniumChitosanSialic acidSilver nanoparticleQuenching (fluorescence)NanoparticleFluorescenceMaterials scienceCombinatorial chemistryNuclear chemistryChemical engineeringChemistryNanotechnologyOrganic chemistryBiochemistryCatalysis

Abstract

fetched live from OpenAlex

/CS was evaluated in aqueous media using sialic acid (SA) as the target molecule in the presence of different monosaccharides and anionic biomolecules as interferents. A significant fluorescence quenching of hybrid NPs was observed in aqueous solutions of SA with interferents, while no significant quenching effect was detected in SA-free interferent solutions. The selective binding of SA to the particles resulted from favorable electrostatic interactions and inter-molecular hydrogen bonding with the functional groups of CS. The hybrid NP system displayed a good sensitivity for SA with a detection limit of 5.1 nM and a concentration dependent fluorescence quenching for SA concentrations ranging from 25 nM to 3.2 μM. This hybrid NP system represents a promising alternative probe for detecting sialic acid in complex samples.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.249
Teacher spread0.230 · 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 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

Citations10
Published2014
Admission routes2
Has abstractyes

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