NYUAD Special Seminar: Yasser Hassan
"Synthesis and Application of Functional Nanomaterials for Decarbonization Using Self-Driving Labs"
Host: Alexej Jerschow
Zoom Link: https://nyu.zoom.us/j/98736776257
NYUAD Special Seminar: Yasser Hassan
"Synthesis and Application of Functional Nanomaterials for Decarbonization Using Self-Driving Labs"
Host: Alexej Jerschow
Zoom Link: https://nyu.zoom.us/j/98736776257
Abstract: The global transition of society from legacy fossil fuels to low-carbon sources requires advanced and functional materials to harvest renewable energy resources and design devices that consume energy more efficiently. In a single hour, the sun strikes the Earth with power to supply global annual energy consumption. However, scaling up capturing and storing this renewable energy faces long-term challenges due to material efficiency and cost limitations. A smooth transition to a low-carbon economy using efficient devices for clean energy generation and storage is an example of such challenges. Accordingly, effective transition to decarbonization requires taking practical, cost- effective measures that do not exacerbate ecological problems. Research addressing these challenges has a significant scope and substantial impact on the economy, where technology leapfrogging is the way forward to reach global goals and objectives rapidly. Today, one of the biggest bottlenecks for advancing green technologies (e.g., optoelectronics, batteries, sensors, catalysts, superconductors, and next-generation quantum computers) is developing novel materials with optimal properties. While there is significant progress in the field, we are still in dire need of innovative ways to accelerate sustainable materials development processes. I strongly argue that a synergistic multi- disciplinary approach is needed to accelerate material discovery and development. My research focuses on developing efficient synthetic techniques for automated experimentation (using robotics) to accelerate the discovery of advanced, novel, and functional nanomaterials. My proposed research is meant for incubating a breakthrough that disrupts conventional experimentation and accelerates the discovery of urgent nanomaterials. The ideas generated from our laboratory at NYU will potentially accelerate the innovation of advanced functional nanomaterials. Governments (like the US) are continued to invest in the partway-applied research and the fundamentals of nanotechnologies. It is expected that the global nanotechnology market will exceed US 125 billion by 2024.
Bio: Yasser Hassan (he/his)
Research Fellow at the University of Toronto, Canada.
Long-term Visiting Research Fellow at the University of Oxford.
Dr. Hassan obtained his Ph.D. in Chemical Engineering and Applied Chemistry with Professor Greg Scholes and Professor Mitch Winnik at the University of Toronto in 2016. He acquired extensive knowledge of materials, nanochemistry, applied physics, solar energy conversion, photodetection, and photocatalytic applications. During his doctoral program, he developed reliable and reproducible synthetic procedures to fabricate monodispersed quantum dots and hybrid perovskite 2D materials with functionalized surfaces suitable for optoelectronic applications. In 2016, Dr. Hassan joined the University of Oxford as a postdoctoral researcher collaborating with Professor Henry Snaith to undertake experimental and modeling research work to solve various scientific challenges in perovskite nanomaterials-based thin film technology. During his tenure at Oxford, Dr. Hassan launched and developed a nanomaterials laboratory, initiating an innovative and exciting research direction that continues to this day with several scientists he mentored. Yasser's research work at Oxford resulted in multiple academic journal publications, one of which appeared in the renowned scientific journal of Nature. He also co-authored 17 peer reviewed papers and five patents. Yasser's current work at UofT in Prof. Edward Sargent's lab focuses on detecting infrared rays using advanced engineering approaches and machine learning.