Because
of the vital role of temperature in many biological processes
studied in microfluidic devices, there is a need to develop improved
temperature sensors and data analysis algorithms. The photoluminescence
(PL) of nanocrystals (quantum dots) has been successfully used in
microfluidic temperature devices, but the accuracy of the reconstructed
temperature has been limited to about 1 K over a temperature range
of tens of degrees. A machine learning algorithm consisting of a fully
connected network of seven layers with decreasing numbers of nodes
was developed and applied to a combination of normalized spectral
and time-resolved PL data of CdTe quantum dot emission in a microfluidic
device. The data used by the algorithm were collected over two temperature
ranges: 10–300 K and 298–319 K. The accuracy of each
neural network was assessed via a mean absolute error of a holdout
set of data. For the low-temperature regime, the accuracy was 7.7
K, or 0.4 K when the holdout set is restricted to temperatures above
100 K. For the high-temperature regime, the accuracy was 0.1 K. This
method provides demonstrates a potential machine learning approach
to accurately sense temperature in microfluidic (and potentially nanofluidic)
devices when the data analysis is based on normalized PL data when
it is stable over time.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.