Quantum Dots-Based Biological Fluorescent Probes for In Vitro and In Vivo Imaging

He, Yao

doi:10.5772/36544

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…1 One avenue of research has been the use of fluorescent dyes as thermometers because of their emission being sensitive to temperature variations. Fluorescent dyes have been used in many instances including labeling structures in cells, 2 single photon imaging, 3 and as temperature sensors, relating the individual spectral features such as peak intensity (PI), peak wavelength (PW or PWL), or the ratio of different spectral peaks, 4 as well as fluorescent lifetime measurements. 5 Specifically, Rhodamine B dye has been used as a non-contact temperature sensor in microfluidic devices, 6,7 to probe the surface temperature between two plates, 8 and as an additive in polydimethylsiloxane (PDMS) to measure the thermal conductivity of carbon nanotubes.…”

Section: Introductionmentioning

confidence: 99%

CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction

Munro

Liu

Glorieux

et al. 2016

Journal of Applied Physics

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Articles you may be interested in Fluorescence resonance energy transfer measured by spatial photon migration in CdSe-ZnS quantum dots colloidal systems as a function of concentration Appl. Phys. Lett. 105, 203108 (2014) As a system of interest gets small, due to the influence of the sensor mass and heat leaks through the sensor contacts, thermal characterization by means of contact temperature measurements becomes cumbersome. Non-contact temperature measurement offers a suitable alternative, provided a reliable relationship between the temperature and the detected signal is available. In this work, exploiting the temperature dependence of their fluorescence spectrum, the use of quantum dots as thermomarkers on the surface of a fiber of interest is demonstrated. The performance is assessed of a series of neural networks that use different spectral shape characteristics as inputs (peak-based-peak intensity, peak wavelength; shape-based-integrated intensity, their ratio, full-width half maximum, peak normalized intensity at certain wavelengths, and summation of intensity over several spectral bands) and that yield at their output the fiber temperature in the optically probed area on a spider silk fiber. Starting from neural networks trained on fluorescence spectra acquired in steady state temperature conditions, numerical simulations are performed to assess the quality of the reconstruction of dynamical temperature changes that are photothermally induced by illuminating the fiber with periodically intensitymodulated light. Comparison of the five neural networks investigated to multiple types of curve fits showed that using neural networks trained on a combination of the spectral characteristics improves the accuracy over use of a single independent input, with the greatest accuracy observed for inputs that included both intensity-based measurements (peak intensity) and shape-based measurements (normalized intensity at multiple wavelengths), with an ultimate accuracy of 0.29 K via numerical simulation based on experimental observations. The implications are that quantum dots can be used as a more stable and accurate fluorescence thermometer for solid materials and that use of neural networks for temperature reconstruction improves the accuracy of the measurement. Published by AIP Publishing.

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