Improving the Accuracy of Carbon Dot Temperature Sensing Using Multi-Dimensional Machine Learning-Reference-Cited by-同舟云学术

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Improving the Accuracy of Carbon Dot Temperature Sensing Using Multi-Dimensional Machine Learning

Published:2024-01-05 Issue:2 Volume:7 Page:2258-2269
ISSN:2574-0970
Container-title:ACS Applied Nano Materials
language:en
Short-container-title:ACS Appl. Nano Mater.

Author:

Döring Aaron¹^ORCID,Qiu Yuqing¹,Rogach Andrey L.¹²^ORCID

Affiliation:

1. Department of Materials Science and Engineering, and Centre for Functional Photonics (CFP), City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR 999077, P. R. China

2. IT4Innovations, National Supercomputing Center, VSB-TUO Ostrava, Ostrava 708 00, Czech Republic

Funder

Research Grants Council, University Grants Committee

Moravian-Silesian Region

VSB-TUO

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsanm.3c05688

Reference62 articles.

1. Carbon Dots as New Generation Materials for Nanothermometer: Review

2. Thermal sensors based on the seebeck effect

3. Recent progress on small molecular temperature‐sensitive fluorescent probes

4. Standardizing luminescence nanothermometry for biomedical applications

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Construction of multifunctional core-shell structure nanoprobe integrating NIR dual-mode optical thermometer and nuclear magnetic imaging;Materials Today Communications;2024-08

2. Machine learning for carbon dot synthesis and applications;Materials Advances;2024

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