Theoretical Discovery of Dirac Half Metal in Experimentally Synthesized Two Dimensional Metal-Organic Frameworks

Cheng Tang

PhD Student, Aijun Du Group, QUT

Abstract: Dirac half metal is rare in nature but crucial for future spintronic devices. Although this unique feature has been demonstrated in several theoretically designed metal-organic frameworks (MOFs), none of these have been synthesized. Therefore, the exploration of half metallic Dirac features in experimentally synthesized MOFs is extremely significant. Via density functional theory (DFT), we investigate two recently synthesized two-dimensional (2D) metal-semiquinoid frameworks (V-SF and Ti-SF) as novel Dirac materials with ultrahigh Fermi velocities, which are comparable to that of graphene. Notably, Ti-SF exhibits a Dirac dispersion in only one spin channel, while it is semiconducting with a bandgap of 1.67 eV in the other spin channel. This is the first report of a half-metallic Dirac feature in experimentally synthesized MOFs. Furthermore, we adopted a molecular orbital model to analyse the magnetism of MOFs with D3 symmetry. The model accurately describes the magnetism of 3d transition metal-semiquinoid frameworks, and we expected it to instruct further research focused on magnetic complexes.

Bio: Cheng Tang received his bachelor’s degree in Physics at Nankai University (China) and his master’s degree in Materials Physics and Chemistry at Shandong University (China). Then, he joined Prof. Aijun Du’s group at Queensland University of Technology as a PhD student in November 2017. His current research is mainly focused on theoretical discovery of two dimensional materials with novel electronic, magnetic and optical properties.

Inclusive Modelling of Biological Age through the Interpretation of Bioimage and Genomic Data

Ebony Watson

PhD student, Mar Group, AIBN, UQ

Abstract: Recent advances in imaging technologies have allowed for the visualisation of biological phenotypes from the molecular to whole-organism scale. The integration of such images with multi-omic data presents a promising framework to explore molecular drivers of these phenotypes. Integration of multiple data types is particularly applicable to the investigation into the mechanisms underlying biological ageing, due to the complexity of the aging process.

Whilst several models exist to predict biological age, these are based on molecular factors like gene expression or DNA methylation. This project aims to create a more comprehensive by incorporating image data from tissues or cells. Deep neural networks will be used to extract age-associated features from histological tissue images as a pilot project. These quantitfied features will then be integrated with gene expression data from matching samples and labelled with relevant clinical data. A second neural network will be trained on this new integrated dataset to create the final predictive model for biological age. Determining what factors influence the rate of ageing enables us to explore potential interventions and therapies to reduce the incidence of age-associated decline and disease.

Bio: Ebony Watson received her Bachelor of Science (Honours) degree in Genetics from the University of Queensland in 2018. In January 2019, she commenced her PhD with Associate Professor Jessica Mar at the Institute for Bioengineering and Nanotechnology, University of Queensland. Ebonys research is focused on investigating the mechanisms behind ageing and age-associated diseases through the application of machine learning and image analysis techniques.

Date:  Thursday, 18 July 2019

Time:  9.30 – 10.30am

Venue:  AIBN Seminar Room, Level 1 (Bldg 75, UQ)




Level 1 (Bldg 75, UQ)
AIBN Seminar Room