1st-Principles+Nano-Device+Computing+Lab
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Novel Material Properties of Hybrid Perovskite Nanostructures for Next-generation Non-linear Electronic Devices
(from left: Juho Lee, Dr. Muhammad Ejaz Khan and Professor Yong-Hoon Kim)
A KAIST research team reported a novel non-linear device with the founding property coming from perovskite nanowires. They showed that hybrid perovskite-derived, inorganic-framework nanowires can acquire semi-metallicity, and proposed negative differential resistance (NDR) devices with excellent NDR characteristics that resulted from a novel quantum-hybridization NDR mechanism, implying the potential of perovskite nanowires to be realized in next-generation electronic devices.
Organic-inorganic hybrid halide perovskites have recently emerged as prominent candidates for photonic applications due to their excellent optoelectronic properties as well as their low cost and facile synthesis processes. Prominent progresses have been already made for devices including solar cells, light-emitting diodes, lasers and photodetectors.
However, research on electronic devices based on hybrid halide perovskites has not been actively pursued compared with their photonic device counterparts.
Professor Yong-Hoon Kim from the School of Electrical Engineering and his team took a closer look at low-dimensional organic-inorganic halide perovskite materials, which have enhanced quantum confinement effects, and particularly focused on the recently synthesized trimethylsulfonium (TMS) lead triiodide (CH3)3SPbI3.
Using supercomputer simulations, the team first showed that stripping the (CH3)3S or TMS organic ligands from the TMS PbI3 perovskite nanowires results in semi-metallic PbI3 columns, which contradicts the conventional assumption of the semiconducting or insulating characteristics of the inorganic perovskite framework.
Utilizing the semi-metallic PbI3 inorganic framework as the electrode, the team designed a tunneling junction device from perovskite nanowires and found that they exhibit excellent nonlinear negative differential resistance (NDR) behavior. The NDR property is a key to realizing next-generation, ultra-low-power, and multivalued non-linear devices. Furthermore, the team found that this NDR originates from a novel mechanism that involves the quantum-mechanical hybridization between channel and electrode states.
Professor Kim said, “This research demonstrates the potential of quantum mechanics-based computer simulations to lead developments in advanced nanomaterials and nanodevices. In particular, this research proposes a new direction in the development of a quantum mechanical tunneling device, which was the topic for which the Nobel Laureate in Physics in 1973 was awarded to Dr. Leo Esaki.
This research, led by Dr. Muhammad Ejaz Khan and PhD candidate Juho Lee, was published online in Advanced Functional Materials (10.1002/adfm.201807620) on January 7, 2019.
Figure. The draft version of the cover page of 'Advanced Functional Materials'
2019.02.22 View 6555 -
Computer Simulation Identifies a Key Principle for Next-generation Carbon Fibers
(from left: Professor Yong-Hoon Kim and PhD candidate Juho Lee)
Performing state-of-the-art computer simulations, a KAIST research team identified an atomistic design principle to produce high-quality, next-generation carbon fibers.
Carbon fibers are light-weight yet excellent in mechanical strength and thermal resistance. Boasting these properties, they can be diversely applied in high-technology sectors, including automotive, aerospace, and nuclear engineering.
They are produced from a polymer precursor through a series of spinning, stabilization, and carbonization processes. However, there is a major obstacle to producing high-quality carbon fibers. That is, when there exist ill-defined regions within the polymer matrixes, they result in disorder and defects within the produced carbon fibers.
As a solution to this problem, it was proposed that the introduction of carbon nanotubes (CNT) could enhance polymer orientation and crystallization. However, although the alignment geometry of the CNT-polymer interface apparently affects the quality of produced fibers, the atomistic understanding of the CNT-polymer interface has been so far lacking, hindering further developments.
To clarify the nature of CNT-polymer interactions, Professor Yong-Hoon Kim from the Graduate School of Energy, Environment, Water and Sustainability and his team employed a multiscale approach that combines first-principles density functional theory (DFT) calculations and force-fields molecular dynamics (MD) simulations and revealed the unique structural and electronic characteristics of polymer-CNT interfaces.
Here, they studied polyacrylonitrile (PAN)-CNT hybrid structures as a representative case of polymer-CNT composites. PAN is the most common polymer precursor, taking more than 90% of carbon fiber production.
Based on their DFT calculations, the team showed that the lying-down PAN configurations give a larger PAN-CNT binding energy than their standing-up counterparts. Moreover, maximizing the lying-down PAN configuration was shown to allow linear alignments of PANs on CNT, enabling the desirable ordered long-range PAN-PAN packing.
They also identified the CNT curvature as another significant factor, giving the largest PAN-CNT binding energy in the zero-curvature graphene limit. Conducting large-scale MD simulations, they then demonstrated that graphene nanoribbons are a promising carbon nano-reinforcement candidate by explicitly showing its strong propensity to induce linear alignments of PANs adsorbed on them.
Professor Kim said, “This research can be an exemplary case where the quantum mechanical simulations identify basic principles for developing advanced materials. Computer simulation studies will play an increasingly greater role thanks to the advances in the simulation theory and computer performance.”
This research, carried out by the PhD candidate Juho Lee, was published in the inside back cover of Advanced Functional Materials on April 11.
Figure 1. Inside back cover of Advanced Functional Materials
Figure 2. Research outline
2018.08.03 View 7877