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Record W2330562742 · doi:10.1021/la200212c

Phospholipid Membrane Encapsulation of Nanoparticles for Surface-Enhanced Raman Scattering

2011· article· en· W2330562742 on OpenAlexafffund
Shell Ip, Christina M. MacLaughlin, Nikhil Gunari, Gilbert C. Walker

Bibliographic record

VenueLangmuir · 2011
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRaman scatteringRaman spectroscopyNanoparticleFluorescenceChemical engineeringDynamic light scatteringTernary operationLipid bilayerMaterials scienceChemistryMembraneAqueous solutionPhospholipidAnalytical Chemistry (journal)NanotechnologyChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Lipid-encapsulated surface-enhanced Raman scattering (SERS) nanoparticles, with promising applications in biomedical diagnostics, were produced. Gold nanoparticles, 60 nm in diameter, were coated with a ternary mixture of DOPC, sphingomyelin, and cholesterol. The lipid layer is versatile for engineering the chemical and optical properties of the particles. The stability of the lipid-encapsulated particles is demonstrated over a period of weeks. The versatility of the layer is demonstrated by the incorporation of three different Raman-active species using three different strategies. The lipid layer was directly observed by TEM, and the SERS spectrum of the three dye species was confirmed by Raman spectroscopy. UV-vis absorption and dynamic light scattering provide additional evidence of lipid encapsulation. The encapsulation is achieved in aqueous solution, avoiding phase transfer and possible contamination from organic solvents. Furthermore, when fluorescent dye-labeled lipids were employed in the encapsulant, the fluorescence and SERS activity of the particles were controlled by the use of dissolved ions in the preparation solution.

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 score0.005
Threshold uncertainty score0.348

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 head0.039
GPT teacher head0.241
Teacher spread0.202 · 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

Citations62
Published2011
Admission routes2
Has abstractyes

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