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

Abstract

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.

Photoacoustic temperature imaging based on multi-wavelength excitation

“…In the latter cases, the temperature dependence of the PA amplitudes is affected, while the temperature dependence of the ratio of PA amplitudes at different optical wavelengths remains reliable. Moreover, by designing thermochromic entities with non-trivial temperature induced spectral changes [ 50 , 51 ], neural network recognition of the full spectrum can be used to give additional robustness to the reconstructed temperature images [ [52] , [53] , [54] ].

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Thermophysical properties of thin fibers via photothermal quantum dot fluorescence spectral shape-based thermometry

“…The "sum bands" consisted of the summed values of five ≈5 nm wide bands of the smoothed spectra between 536 nm and 664 nm, similar to how photodiodes with notch filters would perform. Finally, the intensity values of 40 evenly spaced wavelengths of the spectra between 536 nm and 664 nm were normalized to the peak intensity and these values (in addition to the peak intensity) were ultimately selected as inputs for the neural network investigated in this work, based on providing the lowest RMS of the trained neural network (0.29 K) [12].…”

“…Based on previous research and simulation [12], the neural network that provided the best accuracy was one where the inputs were based on 40 peak-normalized intensities and the peak intensity, containing 2 hidden nodes. The performance of this neural network is compared to the traditional use of peak intensity as a means to reconstruct the temperature in the time and frequency domains.…”

“…However, the temperature resolution with state-of-the-art quantum dots is on the order of 1 K [4] and methods to improve this accuracy are desired. Previous work [10,12] has demonstrated the potential for neural networks to improve this accuracy.…”

“…They have the potential to improve the temperature accuracy of the quantum-dot shape-based thermometry. The current study seeks to expand shape-based, neural network reconstructed, time-domain fluorescence thermometry [10,12] with quantum dots. The reconstructed temperature evolution is then mapped into the frequency domain to determine the decay and phase delay of the thermal wave along a thin fiber caused by intensity-modulated laser heating for the purpose of determining thermal diffusivity.…”