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.

Many microfluidic processes rely heavily on precise temperature control. Though internally-contained heaters have been developed using traditional fabrication methods, they are limited in their ability to isothermally heat a precisely...

As analysis systems shrink in size to microfluidic scales and devices, there is a need to improve temperature control in the microscale for temperature-sensitive processes. Technology that combines accurate temperature measurement and 3D spatial control of the temperature distribution is limited by common 2D layer-based microfluidic fabrication techniques but can be realized with 3D printed microfluidic chips. This work presents an iterative process to create a microfluidic chip using multi-material 3D printing to improve temperature sensing and create an even temperature around a target volume. Through an iterative process, verification is presented of fluorophore viability (specifically CdTe quantum dots) after being secured in place by cured PR48 3D printing resin, thus confirming the possibility of fluorescent thermometry as an accurate non-contact temperature sensing method. Numerical analyses of various geometries of chip design iterations are also presented verifying spatially even heating due to the placement of heating sources in the microfluidic chip. Combining the fluorescent thermometry and improved heating will lead to improved temperature control in microfluidic devices.