Stanford graduate student Aisulu Aitbekova wins 2021 Melvin P. Klein Award

Aisulu Aitbekova

Aisulu Aitbekova, a 2021 doctoral graduate from Stanford University, discovered her passion for research when she traveled from Kazakhstan to the U.S. for a summer internship as a chemical engineering undergraduate. She said that experience inspired her to go to graduate school.

After earning a master’s in chemical engineering at the Massachusetts Institute of Technology, she continued her studies at Stanford University under the supervision of Matteo Cargnello, an assistant professor of chemical engineering and Aitbekova’s doctoral advisor. Much of her thesis work involved beamline studies at the Stanford Synchrotron Radiation Lightsource (SSRL) at the Department of Energy’s SLAC National Accelerator Laboratory.  

Now, Aitbekova has been selected to receive the 2021 Melvin P. Klein Scientific Development Award, which recognizes outstanding research accomplishments by undergraduates, graduate students and postdoctoral fellows within three years of completing their doctoral degrees.

In a nomination letter for the award, SLAC Distinguished Staff Scientist Simon Bare praised Aitbekova’s initiative. “She has quickly become proficient in the application of X-ray techniques available at the synchrotron at SLAC. This proficiency and mastery include everything from operating the beamline to analyzing and interpreting the data,” he wrote.

Aitbekova said she felt “absolutely thrilled and grateful” to all of her mentors when she found out about winning the award.

“I’m so thankful for my PhD advisor Matteo Cargnello. My success would not have been possible without his mentorship,” Aitbekova said. “I’m also particularly grateful to Simon Bare, who I consider to be my second advisor. His continuous excitement about X-ray absorption spectroscopy has been the driving force for my work at SSRL.” 

Catalyzing change

Aitbekova said she is passionate about finding solutions to combat climate change. She designs materials to convert harmful pollutant gases into useful fuels and chemicals. To perform these chemical transformations, she develops catalysts and studies their properties using X-ray absorption spectroscopy (XAS). Catalysts are substances that increase rates of chemical reactions without being consumed themselves.

“I have identified that a catalyst’s size, shape and composition profoundly affect its performance in eliminating these gases,” but exactly how those properties affect performance remains unknown, she said. “This problem is further complicated by the dynamic nature of catalytic materials. As a catalyst performs chemical transformations, its structure changes, making it challenging to precisely map a catalyst’s properties to its performance.”

To overcome these barriers, she engineers materials the size of one ten-thousandth the diameter of a human hair and then tracks how they change during reactions using XAS.

In one study, Aitbekova and her colleagues engineered a catalyst using a combination of ruthenium and iron oxide nanoparticles, which they think act in a tag-team fashion to improve the synthesis of fuels from carbon dioxide and hydrogen. Using a prototype in the lab, they achieved much higher yields of ethane, propane and butane than previous catalysts.

Switching gears

While engineering catalysts that convert carbon dioxide into chemicals, she developed a new approach for preparing materials, where small particles are encapsulated inside porous oxide materials – for example, encapsulating ruthenium within a sheath of iron.

However, Aitbekova recognized a completely different application for this new approach: creating a palladium-platinum catalyst that works inside a car’s emission control system.

To eliminate the discharge of noxious emission gases, cars are equipped with a catalytic converter. Exhaust gases pass into the catalytic converter, where they are turned into less harmful gases. The catalysts inside these units are platinum and palladium metals, but these metals gradually lose their efficiency due to their extreme working conditions, she said.

“My platinum and palladium catalysts show excellent stability and performance after being subjected to air and steam at 1,100 degrees Celsius, the harshest operating environment automotive exhaust emission control catalysts could be subjected to,” explained Aitbekova. “Further improvements in these materials and successful testing under true exhaust conditions have a potential to revolutionize the field of automotive exhaust emission control.”

Her nominators agreed, citing it as the highlight of her graduate career.

“This work, currently under review for publication, is truly the remarkable result of Aisulu’s hard work and experience in pivoting from one area to another to make an impact and of her ability to connect multiple fields and solve important problems,” Cargnello wrote.

Amplifying impact

Despite this success, Aitbekova is already focused on how to make an even greater impact through mentoring and future research.

Her nominators all applauded her passion and commitment to mentor the next generation of STEM scholars, as demonstrated by mentoring “a countless number of undergraduates” according to Cargnello and by exchanging letters with middle school students from underrepresented groups.

Part of this passion, Cargnello and others wrote, stems from her experiences growing up in a highly conservative environment with the understanding that homemaking would be her eventual job. Aitbekova’s nominators wrote that they admired the fact that she made her way to Stanford and has acted as an ambassador for the values and principles of diversity and inclusion.

Aitbekova said she embraces the role. “Since my first summer research experience in the USA, I’ve wanted to serve as a bridge to science and graduate school to those who, like me, didn’t have access to such knowledge and resources.”

She will continue to act as a bridge in her next endeavor as a Kavli Nanoscience Institute Prize Postdoctoral Fellow at Caltech, where she plans to expand her work of converting carbon dioxide into fuels by running the chemical transformations with solar energy. That will “bring society one step closer to sustainable energy sources,” she said.

Bare and others praised her drive to make an everyday impact. “She has a natural passion for wanting to understand the physical principles behind the phenomena that she has observed in her research. But this passion for understanding is nicely balanced by her desire to discover something new, and to make a real difference — the practicality that is often missing in someone early in their career,” wrote Bare.

The award will be presented to Aitbekova at the 2021 SSRL/LCLS Annual Users’ Meeting during the plenary session on September 24. 

This is a reposting of my news story, courtesy of SLAC National Accelerator Laboratory.

Reassessing the Global Dataset of Wave Climate Projections

A snapshot of significant wave height (defined as the average of the highest 33% of waves) from a simulated realization of the future, submitted to COWCLIP 2.0. Energetic waves (yellow) originating from tropical cyclones can be seen in the North Pacific. Large waves in the southern hemisphere are due to extra-tropical storms, of large spatial extent, that continuously blast the Southern Ocean. Credit: Ben Timmermans

Wind-generated ocean waves — “wind waves” — can be major disruptors of coastal communities, marine ecosystems, offshore industries, and shipping, causing considerable environmental, geophysical, and socioeconomic impacts across the globe. Large waves during past winter storms, for example, stripped volumes of sand from Monterey, California beaches, attacked vulnerable marine terraces, and ultimately caused steep cliffs near Big Sur to crash into the sea. And around the world, these kinds of extreme weather events are becoming increasingly frequent and intense.

So it is critical to understand how global and regional wave conditions may evolve under climate change. This knowledge can then be integrated into comprehensive assessments of future coastal hazards and vulnerabilities to guide climate adaptation strategies.

Waves are generated from wind stress on the ocean surface — stronger storms generate larger waves. However, factors like storm size, intensity, translation speed, and structure combine to create different wave conditions. Modeling how atmospheric wind fields can lead to different spatial patterns of surface waves is critical for forecasts on weather time scales, but predicting how climate change alters wave conditions is much more complicated. Addressing this problem requires high performance computing.

Researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) are tackling this challenge by generating and analyzing ocean wave climate projections using supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), a Department of Energy user facility located at Berkeley Lab. They are also helping compile the results of international wave climate studies, creating a global ensemble dataset for widespread use by stakeholders, governments, and the research community. In the past year, two community-wide papers covering this research — including work done at Berkeley Lab — were published, one in Nature Climate Change and, more recently, in Scientific Data, also a Nature publication.

Modeling Wind-Wave Climate

Scientists use numerical general circulation models (GCMs) to simulate the dynamics and thermodynamics of the Earth’s atmosphere. Increasingly, they employ coupled GCMs to simulate the atmosphere and ocean simultaneously — and even include other components such as land hydrology — allowing feedback between the various systems. These models enable scientists to investigate the properties of the Earth’s weather and climate, both in the past and possible futures.

Only the most recent coupled atmosphere-ocean climate models include in-line wind-driven wave calculations. In addition, many climate models generate data at fairly coarse resolution, which prevents the identification of more intense storms such as tropical cyclones. The research published in Climate Change and Scientific Data compared multiple off-line wind-driven wave calculations.

Among the co-authors on these papers is Ben Timmermans, a researcher at the National Oceanography Centre in the United Kingdom and a former post-doctoral fellow in Berkeley Lab’s Climate and Ecosystems Science Division. As a postdoc, Timmermans worked with Michael Wehner, a senior scientist in Berkeley Lab’s Computational Research Division, to develop a high-resolution climate projection of average wave conditions across the globe. This work relied on simulations that Wehner had previously generated based on atmospheric data, which were collected in three-hour increments with a spatial resolution of either 25-kilometer squared or 100-kilometer squared. Simulating a 25-kilometer dataset took 64 times more computational resources than a 100-kilometer one.

“These atmospheric model calculations would have been impossible without NERSC’s computers, scratch disk, and high performance storage system,” said Wehner. “We used several hundred million hours and about 7,000 processors on Hopper and Cori for the project.”

However, Wehner’s original atmospheric simulations did not model how the atmosphere interacts with wind waves. So Timmermans extended this work to also model and analyze global wave conditions, which represented both present and possible future wave climate. NERSC again played a critical role, supplying three million core hours that ran concurrently on 20 nodes of Edison. Using the high-resolution data and NERSC computing power, the Berkeley team was able to identify tropical storms and extreme waves that the lower-resolution data lacked.

“The abundance of resources at NERSC allowed me to push the wave model almost to its limits in terms of parallel computing capability,” Timmermans said. 

Assembling Wave Climate Projections

This Berkeley Lab project is part of a new generation of global wind-wave studies completed by several international modeling groups. These individual studies, however, use various statistical approaches, dynamical wind-wave models, and data structures, making comparisons between the analyses difficult. In addition, single studies alone cannot be used to quantify total uncertainty given the range and diversity of available wind-wave modeling methods. Without a broader research effort, it remains unclear why the standalone studies sometimes differ in their projected changes in wind-wave characteristics across the world’s oceans.

The Coordinated Ocean Wave Climate Project (COWCLIP) is trying to overcome this problem by creating a consistent multivariate dataset of global wave climate projections for widespread use. Berkeley Lab is one of ten contributing institutions to COWCLIP phase 2, as described in the Scientific Data paper; all ten contributing institutions validated their global wave projection datasets with respect to observations, an important part of the production process.

For example, Timmermans’ validation involved a comparison of his projections of wind speed and wave height distributions against observations from fixed-position oceanic data buoys maintained by the National Oceanic and Atmospheric Administration. However, the COWCLIP2 team also conducted validation on the entire ensemble of datasets, comparing against 26 years of global satellite measurements of significant wave height on a global and regional scale.

“COWCLIP is a coordinated community effort to gather and explore output from state-of-the-art simulations of ocean wave climate, identifying and quantifying the key sources of uncertainty,” Timmermans said. “This new dataset will support future broad-scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.”

Berkeley Lab’s high-resolution climate model output used to drive the wave model is suitable for many other types of analyses and is freely available at https://portal.nersc.gov/c20c/.

This is a reposting of my news feature, courtesy of Lawrence Berkeley National Laboratory.