Carbon-Dot-Based White-Light-Emitting Diodes with Adjustable Correlated Color Temperature Guided by Machine Learning

Area of Science:

• Materials Science
• Nanotechnology
• Optoelectronics

Background:

• Carbon dots (CDs) are versatile nanomaterials with tunable optical properties.
• Developing efficient and color-tunable light-emitting diodes (LEDs) is crucial for advanced lighting applications.
• Perylene-3,4,9,10-tetracarboxylic dianhydride (PTCDA) is a precursor for novel carbon-based materials.

Purpose of the Study:

• To synthesize carbon dots (CDs) with distinct green and orange emissions from PTCDA.
• To fabricate and characterize carbon dot-based light-emitting diodes (LEDs).
• To utilize machine learning for predicting and achieving tunable white photoluminescence (PL) and electroluminescence (EL) with adjustable correlated color temperature (CCT).

Main Methods:

• Fabrication of carbon dots (CDs) using PTCDA as a precursor.
• Construction of single-color (orange/green) and white LEDs utilizing the synthesized CDs.
• Application of machine learning models to predict and control the correlated color temperature (CCT) of white light emission.

Main Results:

• Successfully synthesized green and orange emitting CDs from PTCDA.
• Fabricated CDs-based LEDs exhibiting electroluminescence (EL) at 560 nm (orange) and 498 nm (green) with external quantum efficiencies (EQE) up to 1.98%.
• Achieved tunable white photoluminescence (PL) emission with CCT ranging from 3093 K to 11018 K by combining blue, green, and orange CDs, and demonstrated warm and cold white LEDs with precise CCT control.

Conclusions:

• PTCDA-derived carbon dots are effective emitters for fabricating efficient single-color and white LEDs.
• Machine learning provides a powerful tool for precisely controlling the color properties of white light-emitting devices.
• The developed technology enables the creation of tunable white LEDs for diverse lighting applications.