Sustainability, recycling, and lifetime issues of quantum dots

Kalyani, N. Thejo; Nagabhushana, H.; Michalska-Domańska, Marta; Vengadaesvaran, B.; Arof, A.K.; Dhoble, S.J.

doi:10.1016/b978-0-323-85278-4.00012-x

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ag 2 S offer size-tunable energy levels [29] and quantum confinement effects [30], [31], making them suitable for qubits in quantum information processing [32]. Their long coherence times [33], compatibility with diverse materials [34], and efficient light-matter interaction position them for integration into quantum processors and quantum communication devices.…”

Section: Introductionmentioning

confidence: 99%

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag ₂ S Colloidal Quantum Dots

Malashin,

Daibagya,

Tynchenko

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The study delves into the temporal dynamics of luminescence in colloidal Ag 2 S quantum dots, utilizing time series forecasting techniques. Through an analysis of intensity measurements taken at different time intervals, it uncovers temporal trends and utilizes predictive models to anticipate future behaviour of luminescence spectra. The outcomes contribute to a more profound understanding of optimizing experimental conditions and foreseeing the evolution of these nanomaterials over time. Among the tested models, the most robust and effective approaches for predicting the decay of integral intensity within the first hour include polynomial features with regressors, particularly ElasticNetCV, Ridge, and Lasso, with R 2 scores of 0.74, 0.82, and 0.80, respectively. However, upon comparison with the results of additional experiment conducted over a duration of two hours, the Ridge model demonstrated the best performance in predicting the decay of integral intensity.

show abstract

Section: Introductionmentioning

confidence: 99%

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag ₂ S Colloidal Quantum Dots

Malashin,

Daibagya,

Tynchenko

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Sustainability, recycling, and lifetime issues of quantum dots

Cited by 1 publication

References 15 publications

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag ₂ S Colloidal Quantum Dots

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag ₂ S Colloidal Quantum Dots

Contact Info

Product

Resources

About

Sustainability, recycling, and lifetime issues of quantum dots

Cited by 1 publication

References 15 publications

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag 2 S Colloidal Quantum Dots

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag 2 S Colloidal Quantum Dots

Contact Info

Product

Resources

About

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag ₂ S Colloidal Quantum Dots

ML-Based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag ₂ S Colloidal Quantum Dots