FES Funded ProjectsOutputs
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How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic PhotovoltaicsThis widely publicized paper, in the form of a “Perspective,” was the result of an ongoing collaboration between the Buriak and Mar group, illustrating how machine learning is applied to optimize several experimental conditions so that efficiencies of photovoltaic devices can be rapidly improved. [To date, this paper has been viewed over 7100 times in just over a year.]T12-P01, T12-P04 University of Alberta | Publication | 2018-07-01 | Bing Cao, Lawrence A Adutwum, Anton O Oliynyk, Erik J Luber, Brian C Olsen, Mar, A., Jillian Mary Buriak | Design of Experiments and Machine Learning-Assisted Organic Solar Cell Efficiency OptimizationOrganic solar cells (OSCs) represent a cost-effective way to transform solar energy into electricity due to their potential for low-cost and high-throughput roll-to-roll production.[1] Improving OSC efficiency and stability are two of the most important tasks on the way toward commercialization. While much effort has been focused on developing new materials, there is enormous room with respect to optimization of the processing of OSCs to achieve optimal performance of a particular photoactive material. Morphology of the bulk heterojunction, the most important layer within an OSC, results from nanoscale phase segregation that depends extensively upon processing. However, the optimization process for an OSC is tedious, time-consuming and expensive involving many parameters. Conventional optimization uses unimodal approach where one parameter is optimized while all others are kept constant. This can take months or years. Moreover, there is an associated risk of missing the optimal results because all experimental combinations for all parameters cannot be performed. Herein, we report an approach that uses Design of Experiments (DOE) along with machine learning statistical data analysis to effectively and efficiently optimize solar cell efficiency. Machine learning algorithms are trained to find patterns in datasets which could greatly assist data analysis and parameter importance evaluation, hence predict the results for future experiments.[2] DOE methods allow experimentalists to explore a larger parameter space with fewer experimental trials while obtaining valid and objective conclusions. We show that, using the principles of DOE to plan experiments combined with using machine learning assist in the prediction of optimized solar cells. Specific examples of concrete improvement of the power conversion efficiency of OSCs will be described. T12-P04 University of Alberta | Activity | 2018-05-25 | Bing Cao, Adutwum, L., Brian Olsen, Oliynyk, A., Erik Luber, Tate Hauger, Mar, A., Jillian Mary Buriak | Tailoring Morphology Compatibility and Device Stability by Adding PBDTTPD-COOH as Third Component to Fullerene-Based Polymer Solar CellsT12-P04 | Publication | 2020-02-18 | "Dan Yang", Bing Cao, "Volker Körstgens", "Nitin Saxena", "Nian Li", "Christoph Bilko", "Sebastian Grott", "Wei Chen", "Xinyu Jiang", Julian Eliah Heger, "Sigrid Bernstorff", "Peter Müller-Buschbaum" | Energy Talk: New Year, New TechExplore a series of new energy technologies with the University of Alberta Future Energy Systems program. Researchers will present technologies in 5 minutes or less -- using understandable language -- then answer your energy questions!
1. Make lithium ion batteries better – a new anode technology
The world is electrifying. Global demand for electric vehicles and consumer electronics continues to grow. Better, longer-lasting, and more economical batteries are critical to the advancement of renewable energy and electric vehicles, reducing reliance on fossil fuels, and reducing greenhouse gas (GHG) emissions worldwide.
-Dr. Bing Cao holds a PhD in Chemistry from the University of Alberta. She has almost 10 years of experience in the research and development of developing new renewable energy materials and devices such as organic solar cells and lithium-ion batteries. She is the CEO and co-founder of Nanode Battery Technologies which is developing new lithium-ion battery components.
2. Evolution of the electric grid
From the of the Current War between Nikola Tesla and Thomas Edison, to the present-day alternating current (AC) grid, and the future direct current (DC) smart grid, this talk introduces the fascinating stories and technologies behind the evolution of the grid and how we can make our grid system more efficient and stable.
-Zhongyi Quan received the PhD degree in Energy Systems in 2019 from University of Alberta. After a year of postdoc research, he founded Electronic Grid Systems, a spinoff startup of UofA, and he is now trying to commercialize the microgrid technologies to make our grid systems greener.
3. From waste grease to your next flight trip
Can kitchen grease and waste cooking oil be something other than a nuisance in need of disposal? An exciting project at the University of Alberta is focusing on converting waste greases into high-quality jet fuel using a patented Alberta-developed technology. After processing in high temperature and pressurized vessels, kitchen grease, crop oil, and tallow could all help fuel your next flight!
-Yeling Zhu is a postdoctoral researcher from the University of Alberta. Dedicated to providing solutions for agricultural and industrial operators, he has expertise in developing technologies that create values from Alberta-sourced sawdust, waste materials in the cattle rendering industry, and waste grease. He believes that the derived smart materials and low-carbon fuels help support Alberta's sustainable development.
This online speaker series is presented in partnership with Future Energy Systems. Future Energy Systems was launched in 2016 with $75 million from the Government of Canada’s Canada First Research Excellence fund, to help Canada transition to a low net-carbon energy economy. They focus on multidisciplinary research that develops the energy technologies of the near future, integrates them into today’s infrastructure, and examines possible consequences for our society, economy, and environment. They also contribute to the development of solutions for challenges presented by current energy systems. Energy Talks provides an opportunity for you to engage with researchers and learn more about their work. Visit their website for more informationT01-P06, T06-GS01, T06-GS02 University of Alberta | Activity | 2021-01-27 | | GreenSTEM Entrepreneurial FellowshipGreenSTEM is an entrepreneurial pilot program funded by the Government of Alberta for Science, Technology, Engineering, and Math (STEM) Masters and Ph.D. graduates. The pilot program enables entrepreneurship and provides a two-year commercialization runway for “deep technology” companies. Upon receiving the fellowship, Dr. Bing Cao co-founded Nanode Battery Technologies Ltd. to commercialize the free-standing and high-performance anodes for lithium and sodium-ion batteries. The Nanode product reduces the anode volume by 75% and increases the energy density by 50%.T12-P04 | Award | 2020-07-01 | Bing Cao | Optimization of the Bulk Heterojunction of All–Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches.T12-P04 | Publication | 2020-11-23 | Aaron Kirkey, Erik Luber, Bing Cao, Brian Olsen, Jillian Mary Buriak | Starting a Startup: Challenges and SuccessesT06-GS01 | Activity | 2022-04-29 | Bing Cao | Tin Alloy Sheets as Negative Electrodes for Non-Aquesous Li- and Na-ion BatteriesFiled PCT patent, application No. PCT/CA2022/050376
This invention relates to materials for the negative electrode in non-aqueous rechargeable alkali-ion batteries in free-standing form. In particular, this invention relates to the use of metal ribbon that is produced by melt spinning directly as a battery electrode. The invention also relates to a method producing a highly dispersed, multiphase composite material in a single step, as well as a way to generate porosity while maintaining the 'binder-free' and 'additive-free´ characterization of the electrode.T06-GS01 | IP Management | 2022-09-22 | "Peter Kalisvaart", Bing Cao, Sayed Youssef Sayed Nagy, Jillian Mary Buriak |
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