Work

Modeling Temperature-Dependent Photoluminescence Dynamics of Colloidal CdS Quantum Dots Using Long Short-Term Memory (LSTM) Networks

Year: 2024

Type: article

Abstract: This study addresses the challenge of modeling temperature-dependent photoluminescence (PL) in CdS colloidal quantum dots (QD), where PL properties fluctuate with temperature, complicating traditional modeling approaches. The objective is to develop a predictive model capable of accurately capturing these variations using Long Short-Term Memory (LSTM) networks, which are well suited for managing temporal dependencies in time-series data. The methodology involved training the LSTM model on experimental time-series data of PL intensity and temperature. Through numerical simulation, the model's performance was assessed. Results demonstrated that the LSTM-based model effectively predicted PL trends under different temperature conditions. This approach could be applied in optoelectronics and quantum dot-based sensors for enhanced forecasting capabilities.

Source: Materials

DOI: 10.3390/ma17205056

Authors: Ivan Malashin, Daniil Daibagya, Vadim Tynchenko, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Alexandr Selyukov, Sergey Ambrozevich, Mikhail Smirnov, Oleg Ovchinnikov

Institutions: Bauman Moscow State Technical University, P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Far Eastern Federal University, Voronezh State University

Language: English

Topic: Quantum Dots Synthesis And Properties

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

OpenAlex ID: W4403455835

FWCI: 0.576

Cites: 94

Cited by: 6

Related to: 10