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 intensity-modulated 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.

Neural network recognition of features of the fluorescence spectrum of a thermosensitive probe is exploited in order to achieve fluorescence-based thermometry with an accuracy of 200 mK with 100 MHz bandwidth, and with high robustness against fluctuations of the probe laser intensity used. The concept is implemented on a rhodamine B dyed mixture of copper chloride and glycerol, and the temperature dependent fluorescence is investigated in the temperature range between 234 K and 311 K. The spatial dependence of the calibrated amplitude and phase of photothermally induced temperature oscillations along the axis of the excitation laser are determined at different modulation frequencies. The spatial and frequency dependence of the extracted temperature signals is well fitted by a 1D multi-layer thermal diffusion model. In a time domain implementation of the approach, the gradual temperature rise due to the accumulation of the DC component of the heat flux supplied by repetitive laser pulses as well the immediate transient temperature evolution after each single pulse is extracted from acquired temporal sequences of fluorescence spectra induced by a CW green laser. A stroboscopic implementation of fluorescence thermometry, using a pulsed fluorescence evoking probe laser, is shown to achieve remote detection of temperature changes with a time resolution of 10 ns.