Sanchez Derek, Macdonald Robert, Mitchell Brendan, Wade James, Wilkerson McKay, Hinnen Hunter, Rawlins Marshall, Nordin Gregory P, Woolley Adam T, Munro Troy R
Department of Mechanical Engineering, Brigham Young University, Provo, 84602, UT, USA.
Department of Electrical and Computer Engineering, Brigham Young University, Provo, 84602, UT, USA.
Int J Heat Mass Transf. 2025 Dec 1;252. doi: 10.1016/j.ijheatmasstransfer.2025.127395. Epub 2025 Jun 25.
Temperature sensitive quantum dots (QDs) (CdTe and CdSe/ZnS) are investigated as internal temperature sensors for the growing field of 3D printed microfluidic devices. Two devices were created, one for using CdTe as the temperature sensor and another for using CdSe/ZnS. The QDs were mixed with poly (ethelyne glycol) diacrylate (PEGDA) resin and a thermal curing initiator, inserted into their devices, and cured in place. The fluorescence-to-temperature correlation was calibrated across the span of 30-90° and then tested using a 3 order fit of photoluminescence peak intensity (PLPI) to temperature and a feed-forward neural network (FFNN) combining multiple features of the fluorescence into a single temperature. This results in an improved temperature reconstruction accuracy of ±0.13° for a FFNN of 27 inputs to one output, compared to ±0.29° for PLPI as the single input.
温度敏感量子点(QDs)(碲化镉和硒化镉/硫化锌)被作为3D打印微流控设备这一新兴领域的内部温度传感器进行研究。制作了两个设备,一个使用碲化镉作为温度传感器,另一个使用硒化镉/硫化锌。将量子点与聚(乙二醇)二丙烯酸酯(PEGDA)树脂和热固化引发剂混合,插入到它们的设备中,并在原位固化。在30 - 90°的范围内校准荧光与温度的相关性,然后使用光致发光峰值强度(PLPI)对温度的三阶拟合以及将荧光的多个特征组合成单一温度的前馈神经网络(FFNN)进行测试。与作为单一输入的PLPI的±0.29°相比,对于一个具有27个输入到一个输出的FFNN,这使得温度重建精度提高到了±0.13°。