Effect of atomic layer deposition and alumina actions of optoelectronic behaviour of polyfluorene OLEDS
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Gopal Kaliyaperumal
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Subbarayan Sathiyamurthy
Abstract
With unique properties such as enhanced photoluminescence (PL) efficiency, improved thermal stability, and favourable optical properties, polyfluorenes (PFs) are well-suited for organic light-emitting diode (OLED) applications. However, the conventional PF layers are found to have drawbacks, including variation in charge transport, which minimizes the overall PL efficiency due to uneven coating and photo-oxidation. Current research aims to overcome the above difficulties and to synthesize PF reinforced with alumina (Al2O3) nanoparticles (3 wt%) along with encapsulation coating via Atomic Layer Deposition (ALD) and investigates the influence of varying encapsulation coating thicknesses (0, 10, 30, and 50 nm) in the enhancement of optoelectronic performances and operational stability. The fabricated devices were characterized using electroluminescence (EL) spectra, external quantum efficiency (EQE), current–voltage (I–V) plots, and encapsulation effectiveness tests. The investigational results indicate that an encapsulation thickness of 30 nm yields the maximum EL intensity, with a peak wavelength of 470 nm and an external quantum efficiency (EQE) of 8.1 %. This configuration exhibited a low turn-on voltage of 3 V. The I–V plot demonstrated a maximum current density of 8.2 mA/cm2. The Hall mobility was increased to 1.0 × 10−4 cm2/V.s with the observed carrier concentration of 2.6 × 1016 cm−3. The alumina encapsulation significantly improved the durability and stability, with a device lifetime of 312 hours, and reduced the oxygen permeation rate to 5 cm3/m2/day/atm. The findings highlight the crucial role of optimizing alumina encapsulation thickness in enhancing the functional performance of PF-based OLED devices.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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