Advancing the Applications of 3D Printed Microfluidics: Utilizing Quantum Dots to Measure Internal Temperature

Area of Science:

• Materials Science
• Nanotechnology
• Microfluidics

Background:

• 3D printed microfluidic devices require accurate internal temperature monitoring.
• Quantum dots (QDs) exhibit temperature-dependent fluorescence, making them suitable for sensing applications.

Purpose of the Study:

• To investigate the use of Cadmium Telluride (CdTe) and Cadmium Selenide/Zinc Sulfide (CdSe/ZnS) quantum dots as internal temperature sensors in 3D printed microfluidic devices.
• To compare the temperature reconstruction accuracy of photoluminescence peak intensity (PLPI) with a feed-forward neural network (FFNN) approach.

Main Methods:

• Quantum dots (CdTe and CdSe/ZnS) were embedded in a poly (ethelyne glycol) diacrylate (PEGDA) resin within custom-designed 3D printed microfluidic devices.
• The fluorescence-to-temperature correlation was calibrated between 30-90°C.
• Temperature was reconstructed using both a 3rd order fit of PLPI and a FFNN integrating multiple fluorescence features.

Main Results:

• The study demonstrated successful integration of QDs as internal temperature sensors within 3D printed microfluidic devices.
• The FFNN approach achieved a significantly improved temperature reconstruction accuracy of ±0.13°C.
• The PLPI method yielded a lower accuracy of ±0.29°C.

Conclusions:

• Quantum dots are effective internal temperature sensors for 3D printed microfluidic systems.
• Feed-forward neural networks enhance temperature sensing accuracy in microfluidic devices by utilizing multiple fluorescence features.
• This advancement holds promise for improved control and monitoring in microfluidic applications.