Research Theme 1:

Precision Synthesis

Research Theme 1: Precision Synthesis

Research Theme 1 (RT-1) includes the teams that are developing new chemical reactions to synthesize the building blocks that make up the next generation of optoelectronic devices.

Transforming optical technologies with colloidal quantum dots begins with synthesizing novel materials that have superior performance and can be easily handled and incorporated into devices and applications. Members of RT-1 are advancing the fundamental science underpinning colloidal semiconductors.

Combining multi-level theory and experimentation the team engaged in RT-1 are innovating techniques to control the precision synthesis of colloidal materials and their surfaces to produce quantum dots with advanced combinations of color purity (linewidth), stability, brightness, and processability from ensembles down to single dot precision.

RT-1’s collaboration with RT-2 revolves around the design of new materials that enable accurate and reliable placement of the new materials in device architectures. RT-1’s collaboration with RT-3 uses the feedback from device engineers to innovate on new materials that have properties desired in new device structures.

Find out more about the IMOD members participating in RT-1 research, and check out some of the recent RT-1 publications.

RT-1 Research Groups

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Recent RT-1 Publications

Engineering the Surface Chemistry of Colloidal InP Quantum Dots for Charge Transport

Engineering the Surface Chemistry of Colloidal InP Quantum Dots for Charge Transport

Chem. Mater., 2022, 34, 18, 8306-8315

https://doi.org/10.1021/acs.chemmater.1c04382

Ordered Mixed-Spacer 2D Bromide Perovskites and the Dual Role of 1,2,4-Triazolium Cation

Ordered Mixed-Spacer 2D Bromide Perovskites and the Dual Role of 1,2,4-Triazolium Cation

Chem. Mater. 2022, 34, 14, 6541–6552

https://doi.org/10.1021/acs.chemmater.2c01432

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Chem. Mater. 2022, 34, 14, 6296–6311

https://doi.org/10.1021/acs.chemmater.2c00640

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Preprint: ChemRxiv

https://doi.org/10.26434/chemrxiv-2022-b3fgw-v2

Coherent Spin Dynamics in Vapor-Deposited CsPbBr3 Perovskite Thin Films

Coherent Spin Dynamics in Vapor-Deposited CsPbBr3 Perovskite Thin Films

Chem. Mater., 2022, 34, 4, 1937 – 1945

https://doi.org/10.1021/acs.chemmater.1c04382