Each year, Clarivate™ identifies the world’s most influential researchers ─ the select few who have been most frequently cited by their peers over the last decade. In 2020, fewer than 6,200, or about 0.1%, of the world’s researchers, in 21 research fields and across multiple fields, have earned this exclusive distinction.
https://recognition.webofscience.com/awards/highly-cited/2020/
We are proud to be a part of this academia-industry collaboration, working on a high throughput design and synthesis of new materials for a range of applications: catalysis for water splitting and CO2 conversion, and LEDs.
Welcoming our fresh PhD student Milad, and a cohort of undergraduate researchers, Abhishek, Tony, Ihor, Usama, Akshay, Dave. Shadi is also returning to our group as a postdoc!
In collaboration with Prof. Makhsud Saidaminov from the University of Victoria we are starting a new NFRF project targeting the synthesis of new solution-processed semiconductors.
Our initial goal is to improve the stability of fully-inorganic Pb-based perovskites for applications in solar cells and X-ray detectors. The next milestone is discovering new Pb-free perovskite-like materials.
Welcoming a visiting PhD student, Pimsuda, who will lead this project!
Congratulations, Kamal!
ACS Materials Letters, 2020, just accepted manuscript.
The oxygen evolution capabilities of a manganese metal-organic framework (MOF) are demonstrated in acid (pH 1.3) for the first time. The MOF/carbon black composite significantly outperforms MnO2, a known non-noble acidic OER catalyst, exhibiting overpotentials of 539 mV and 764 mV (vs. 715 mV and 898 mV for MnO2) for a current density of 10 mA/cm2 and 50 mA/cm2 respectively.
Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis
In this work, we applied Bayesian optimization methods to facilitate the search in a multi-dimensional parameter space of quantum dot synthesis and achieved the optimal results in a smaller number of experimental trials compared to conventional methods (random search, grid search, or gradient descent).