Luminescence Study of CdSe Quantum Dots Using Machine Learning Techniques

Abstract

The photoluminescence study of synthesized CdSe quantum dots and comparison with literature based on
machine learning (ML) techniques are studied in this paper. Quantum dot is a promising photovoltaic material
for thin film solar cells and also shows power superior lasing efficiency higher than existing quantum-well
devices. It is possible to create a quantum dot that can generate and absorb energy across the whole solar
spectrum. Quantum dots (QDs) are special in a variety of applications due to their distinct size-dependent band
gap. Since their photo luminescent properties can be considerably enhanced by optimization of the techniques
by which they are synthesized, and are useful in application of optoelectronic disciplines. X-ray diffraction and
photoluminescence were used to determine how CdSe quantum dots formed. Later the particle size is calculated
by the Debye-Scherrer equation. Because of the quantum confinement effect and size variation, the FWHM of
the CdSe samples exhibit greater values in photoluminescence than an ordinary bulk semiconductor, which is
also in accordance to the literature based on ML techniques.