Keywords
Nanoscale, thermometer, quantum dots, neural network
Abstract
For hundreds of years, humans have built thermometers based on the principles of thermal expansion and contraction. In the modern world, there is a need to scale down our thermometers to monitor cellular reactions, both within the human body and within artificial organ chips. Lewis et al. (2020) introduces a nanoscale thermometer based on the thermal expansion of CdTe quantum dots. When ex panded, these dots exhibit systematic changes in photoluminescence (PL) which are recognized by a neural network as an increase in temperature. Our research builds upon the framework of Lewis et al. (2020), testing its feasibility with ZnCuInS/ZnS quantum dots and a physics informed neural network. We find that our updated temperature sensing procedure performs worse than that of Lewis et al. (2020) with respective accuracies of 0.43°C and 0.1°C. Encouragingly, in a self-comparison, we find that implementing physics constraints into our neural network produced our best accuracy of 0.43°C from what was previously 1.01°C.
BYU ScholarsArchive Citation
Physics and Astronomy, REU/FRI Program and Banks, Amber, "Temperature Predictions with ZnCuInS/ZnS Quantum Dots" (2026). Student Works. 460.
https://scholarsarchive.byu.edu/studentpub/460
Document Type
Report
Publication Date
2026-08-24
Language
English
College
Computational, Mathematical, & Physical Sciences
Department
Physics and Astronomy
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