ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of <italic>Ag</italic><sub>2</sub><italic>S</italic> Colloidal Quantum Dots

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of <italic>Ag</italic><sub>2</sub><italic>S</italic> Colloidal Quantum Dots

The study delves into the temporal dynamics of luminescence in colloidal <inline-formula> <tex-math notation="LaTeX">$Ag_{2}S$ </tex-math></inline-formula> quantum dots, utilizing time series forecasting techniques. Through an analysis of intensity measurements take...

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Bibliographic Details
Main Authors: Ivan P. Malashin, Daniil S. Daibagya, Vadim S. Tynchenko, Vladimir A. Nelyub, Aleksei S. Borodulin, Andrei P. Gantimurov, Sergey A. Ambrozevich, Alexandr S. Selyukov
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Ag₂S
luminescence
machine learning
prediction
quantum dots
temporal dynamics
Online Access:https://ieeexplore.ieee.org/document/10496051/

Internet

https://ieeexplore.ieee.org/document/10496051/

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