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

Abstract

To improve predictions of composite behavior under thermal loads, there is a need to measure the axial thermophysical properties of thin fibers. Current methods to accomplish this have prohibitively long lead times due to extensive sample preparation. This work details the use of quantum dots thermomarkers to measure the surface temperature of thin fibers in a non-contact manner and determine the fibers' thermal diffusivity. Neural networks are trained on extracting the temperature of a sample from fluorescence spectra in calibrated, steady-state conditions, based on different spectral features such as peak intensity and peak wavelength. The trained neural networks are then used to reconstruct the evolution of the surface temperature in transient heating experiments. In order to determine the thermal properties of a thin fiber, modulated laser heating is applied and an FFT-based method is used to extract the phase and amplitude response of the temperature field at the modulation frequency. The spatiotemporal dependence of the fluorescence signal, obtained by scanning the distance between the excitation and detection laser spots and varying the frequency response due to an axial scan and a frequency scan, is then curve-fit to the resulting decay curves by a photothermal model in order to determine the thermal diffusivity of the fiber. The measured thermal diffusivity (3.3 ± 0.8 × 10 −7 m 2 s −1) of a synthetic spider silk fiber by the current method has similar properties to other synthetic silk fibers, and demonstrates the ability of the current method to more rapidly measure thermophysical properties of thin fibers.

Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data towards use in Bio-Microfluidics

“…These have been limited to measurements of a single point. Four of them used SCFF trained on features of the emitted fluorescent spectra to train the network to recreate the temperature at a single illumination point [40][41][42][43]. One network that used the intensities of multiple spectral bands resulted in the greatest accuracy, with an RMSE of ±0.3 K or 35 mK•Hz -1/2 when considering the effect of the integration time on the measurement.…”

“…The first architecture implemented for this study was a SCFF network to provide an initial baseline based on existing literature on using neural networks in fluorescent thermometry [40][41][42][43]. However, because this type of network is not easily scalable for image reconstruction, its performance was expected to be highly erroneous.…”

An Improved Method for Accurate Radiation Measurement Based on Dark Output Noise Drift Compensation

“…In the processes of scientific production, the spectral information of the product can be obtained through fiber optic spectrometers, and the quality of the product can be detected quickly [12,13]. Some literature has reported temperature measurements by using fluorescence spectroscopy in specific scenarios [14][15][16][17]. In addition, it has been reported that using fiber optic spectrometers to study the polarization spectrum of an oil slick on the sea and monitoring the pollution of an oil slick on the sea surface through polarization spectrum [18,19].…”

Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics

“…There are several papers that are zero-dimensional studies, namely temperature reconstruction at a single point. The few papers that present temperature reconstruction from fluorescent data generally use simply connected feed-forward networks (SCFF) trained on features of the emitted fluorescent spectra to recreate the temperature at a single illumination location point [21][22][23][24]. One network achieved an RMSE of ±0.3 K or 35 mK•Hz -1/2 by using the intensities of multiple spectral bands, which was twice as accurate as work Sarmanova [25] performed that used the NN to determine both temperature and pH.…”