ArticleACS applied nano materials2020
Use of Machine Learning with Temporal Photoluminescence Signals from CdTe Quantum Dots for Temperature Measurement in Microfluidic Devices.
Article in ACS applied nano materials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Luminescence Thermometry 2.0: Machine-Learning Temperature Read-Out with Luminescence of YOF: YbNanomaterials (Basel, Switzerland) · 2026Article
- Advancing the Applications of 3D Printed Microfluidics: Utilizing Quantum Dots to Measure Internal Temperature.International journal of heat and mass transfer · 2025Article
- Luminescence Thermometry Beyond the Biological Realm.ACS nanoscience Au · 2024Review
- Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics.International journal of thermophysics · 2023Article
- Comparison of Performance between Single- and Multiparameter Luminescence Thermometry Methods Based on the MnSensors (Basel, Switzerland) · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Machine Learning Enabled Image Analysis of Time-Temperature Sensing Colloidal Arrays.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data Toward Use in Bio-microfluidics.International journal of thermophysics · 2022Article
- Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry.Light, science & applications · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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 was collected over two temperature ranges: 10 K to 300 K, and 298 K to 319 K. The accuracy of each neural network was assessed via 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.
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