Тип публикации: статья из журнала
Год издания: 2024
Идентификатор DOI: 10.3390/ma17205056
Аннотация: <jats:p>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 LПоказать полностьюong 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.</jats:p>
Журнал: Materials
Выпуск журнала: Т. 17, № 20
Номера страниц: 5056
ISSN журнала: 19961944
Место издания: Basel