Neural network temperature predictions based on the optical properties of quantum dots

The vital role of temperature in many biological processes creates a need to develop improved microscopic temperature sensors. The photoluminescence (PL) of quantum dots (QDs) has previously been used as a temperature probe in microfluidic devices, but the accuracy of the reconstructed temperature was limited to about 1 K over a temperature range of tens of degrees. In this work we present a machine learning neural network algorithm which uses a combination of normalized spectral and time-resolved PL data of QD emission in a microfluidic device to predict temperature. The neural network was trained with PL collected under known temperatures, then tested with holdout PL data not involved in the training process. The accuracy of the temperature predictions was 0.1 K. We present results for CdTe QDs, as well as recent extensions of this work into other types of QDs. While an ongoing issue has been long term accuracy of the temperature predictions as the properties of the QDs can vary over time, this method demonstrates a potential approach to accurately sense temperature in microfluidic (and possibly nanofluidic) devices via optical measurements.