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Record W4365137764 · doi:10.1088/2053-1591/accc70

On the potential application of surface plasmon-based core-shell particles to study blood functional parameters

2023· article· en· W4365137764 on OpenAlexafffund
Krishnan Sathiyamoorthy, Michael C. Kolios

Bibliographic record

VenueMaterials Research Express · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsNanoshellMaterials sciencePlasmonWavelengthAbsorbanceAbsorption (acoustics)Refractive indexSurface plasmon resonanceOptoelectronicsOpticsAnalytical Chemistry (journal)ChemistryNanotechnologyNanoparticle

Abstract

fetched live from OpenAlex

Abstract We investigated the application of gold nanoshell particles as optical sensors and contrast agents to study the blood functional parameters. Gold nanoshell particles with a core size of 1 μ m that exhibit two prominent plasmonic peaks at 750 and 830 nm were developed. The peaks correspond to the wavelengths typically used to study the oxygen saturation of the blood. The plasmonic properties of gold nanoshells in media with various refractive indices were studied. Glucose samples with concentrations 0, 15, and 20%w/v in water were used. The 750 and 830 nm plasmonic peaks exhibit peak wavelength shifts of 63.77 ± 49.40 nm and 31.18 ± 20.94 nm per unit refractive index change. The optical properties of blood samples mixed with gold nanoshells were also measured. The optical absorption of blood samples increased by 7% at these wavelengths in the presence of the nanoshells. The plasmonic peaks at 750 and 830 nm showed a 3.57 ± 0.56 and 1.44 ± 0.55 percentage variation in absorbance for a 1% change in hematocrit. The enhanced optical absorption at these wavelengths suggests that these particles are effective optical sensors/contrast agents for multimodal optical and photoacoustic sensing and imaging.

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)0.001
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 score0.203
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.058
GPT teacher head0.302
Teacher spread0.244 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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