Optimising the displays of the future

: strategies for the improvement of the emissive properties of thermally activated delayed fluorescence emitters for organic light-emitting diodes

Abstract

In order for an organic light-emitting diode to achieve high efficiencies it is vital that both singlet and triplet excitons are harvested by the emitter into light. One of the best methods to achieve this in an organic emitter is thermally activated delayed fluorescence (TADF), but despite intense research activity many challenges in this field remain. In this thesis, strategies to address these outstanding challenges are discussed. A previously reported but overlooked family of polycyclic aromatic heterocyclic emitters are found to exhibit multi-resonant thermally activated delayed fluorescence in doped films in PMMA, with near-UV emission (λ_PL = ca. 375 nm), and simultaneous room-temperature phosphorescence (RTP) with green emission (λ_PL = ca. 500 nm). Following an extensive investigation of this novel emission pathway, it is also shown that the TADF can be switched on in cores lacking delayed fluorescence through decoration of donor moieties. Depending on the position and number of donors, the (delayed) fluorescence can be red-shifted to the violet region (λ_PL = ca. 415 nm) while preserving the RTP, or further red-shifted (λ_PL = ca. 425 nm) at the cost of switching off RTP. Outcoupling, the propensity of a material to extract generated photons to the outside of a device, is also a crucial parameter for the design of highly efficient devices. To provide greater insight into the molecular features that control this property, a series of increasingly complex machine learning models are trained on a dataset of computational and literature reported values, with the best performing achieving near-quantitative accuracy and a mean absolute error in prediction of 4.4%. Finally, a new computational toolkit is described that automates and manages the entire computational pipeline. The functioning of the program is explored in detail, as is the increase in usability that it offers, particularly for users unfamiliar with computational chemistry.

Date of Award	Jun 2025
Original language	English
Awarding Institution	University of St Andrews
Supervisor	Eli Zysman-Colman (Supervisor) & Malte Christian Gather (Supervisor)