Depth prediction of nanotags in tissue using Surface Enhanced Spatially Offset Raman Scattering (SESORS)

Matthew E. Berry; Samantha M. McCabe; Neil C. Shand; Duncan Graham; Karen Faulds (She/Her)

doi:10.1039/D1CC04455A

Depth prediction of nanotags in tissue using Surface Enhanced Spatially Offset Raman Scattering (SESORS)

Matthew E. Berry, Samantha M. McCabe, Neil C. Shand, Duncan Graham, Karen Faulds (She/Her)

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

12 Downloads (Pure)

Abstract

A model for the prediction of the depth of two 'flavours' of surface enhanced Raman scattering (SERS) active nanotags embedded within porcine tissue is demonstrated using ratiometric analysis of the nanotag and tissue intensities in spatially offset Raman spectroscopy (SORS) measurements.

Original language	English
Pages (from-to)	1756-1759
Number of pages	4
Journal	Chemical Communications
Volume	58
Issue number	11
Early online date	14 Jan 2022
DOIs	https://doi.org/10.1039/D1CC04455A
Publication status	Published - 14 Jan 2022

Keywords

depth prediction
nanotags
tissue
Surface Enhanced Spatially Offset Raman Scattering (SESORS)

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10.1039/D1CC04455ALicence: CC BY 4.0

Berry-etal-CC-2022-Depth-prediction-of-nanotags-in-tissue-using-Surface-Enhanced-Spatially-Offset-Raman-ScatteringFinal published version, 2.12 MBLicence: CC BY 4.0

EPSRC Centre for Doctoral Training in Optical Medical Imaging - Training the next generation of scientific entrepreneurs in healthcare technologies (OPTIMA)
Graham, D. & Faulds, K.
EPSRC (Engineering and Physical Sciences Research Council)
1/05/14 → 31/10/23
Project: Research - Studentship

Data for: “Depth Prediction of Nanotags in Tissue Using Surface Enhanced Spatially Offset Raman Scattering (SESORS)”
Berry, M. (Creator) & Faulds, K. (Supervisor), University of Strathclyde, 13 Jan 2022
DOI: 10.15129/057607c4-fe19-4546-8ef1-12bf5ed574d2
Dataset

Cite this

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title = "Depth prediction of nanotags in tissue using Surface Enhanced Spatially Offset Raman Scattering (SESORS)",

abstract = "A model for the prediction of the depth of two 'flavours' of surface enhanced Raman scattering (SERS) active nanotags embedded within porcine tissue is demonstrated using ratiometric analysis of the nanotag and tissue intensities in spatially offset Raman spectroscopy (SORS) measurements.",

keywords = "depth prediction, nanotags, tissue, Surface Enhanced Spatially Offset Raman Scattering (SESORS)",

author = "Berry, {Matthew E.} and McCabe, {Samantha M.} and Shand, {Neil C.} and Duncan Graham and {Faulds (She/Her)}, Karen",

year = "2022",

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AU - Berry, Matthew E.

AU - McCabe, Samantha M.

AU - Shand, Neil C.

AU - Graham, Duncan

AU - Faulds (She/Her), Karen

PY - 2022/1/14

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N2 - A model for the prediction of the depth of two 'flavours' of surface enhanced Raman scattering (SERS) active nanotags embedded within porcine tissue is demonstrated using ratiometric analysis of the nanotag and tissue intensities in spatially offset Raman spectroscopy (SORS) measurements.

AB - A model for the prediction of the depth of two 'flavours' of surface enhanced Raman scattering (SERS) active nanotags embedded within porcine tissue is demonstrated using ratiometric analysis of the nanotag and tissue intensities in spatially offset Raman spectroscopy (SORS) measurements.

KW - depth prediction

KW - nanotags

KW - tissue

KW - Surface Enhanced Spatially Offset Raman Scattering (SESORS)

U2 - 10.1039/D1CC04455A

DO - 10.1039/D1CC04455A

M3 - Article

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EP - 1759

JO - Chemical Communications

JF - Chemical Communications

IS - 11

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Depth prediction of nanotags in tissue using Surface Enhanced Spatially Offset Raman Scattering (SESORS)

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EPSRC Centre for Doctoral Training in Optical Medical Imaging - Training the next generation of scientific entrepreneurs in healthcare technologies (OPTIMA)

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Data for: “Depth Prediction of Nanotags in Tissue Using Surface Enhanced Spatially Offset Raman Scattering (SESORS)”

Cite this