Major leap for stable high-efficiency perovskite solar cells

Ion-modulated radical doping of spiro-OMeTAD for more efficient and stable perovskite solar cells

Peer-Reviewed Publication

LINKÖPING UNIVERSITY

 

IMAGE: FENG WANG, JUNIOR LECTURER AT LINKÖPING UNIVERSITY. view more 

CREDIT: ANNA NILSEN

Solar cells manufactured from materials known as “perovskites” are catching up with the efficiency of traditional silicon-based solar cells. At the same time, they have advantages of low cost and short energy payback time. However, such solar cells have problems with stability – something that researchers at Linköping University, together with international collaborators, have now managed to solve. The results, published in Science, are a major step forwards in the quest for next-generation solar cells.

“Our results open new possibilities to develop efficient and stable solar cells. Further, they provide new insights into how the doping of organic semiconductors works,” says Feng Gao, professor in the Department of Physics, Chemistry and Biology (IFM) at Linköping University.

Perovskites are crystalline materials with huge potential to contribute to solving the world’s energy shortage. They are cheap to manufacture, with high efficiency and low weight. However, the perovskite solar cells can degrade quickly, and it has not been possible to build high-efficiency perovskite-based solar cells with the required stability. 

“There seems to be a trade-off between high efficiency and stability in perovskite-based solar cells. High-efficiency perovskite solar cells tend to show low stability, and vice versa,” says Tiankai Zhang, a postdoc at IFM and one of the principal authors of the article published in Science.

When solar energy is converted into electricity in perovskite-based solar cells, one or more charge transport layers are usually needed. These lie directly next to the perovskite layer in the solar cell. The organic charge transport layers often need auxiliary molecules in order to function as intended. The material is described as being “doped”. 

One doped transport layer called Spiro-OMeTAD is a benchmark in perovskite solar cells, and delivers record power conversion efficiencies. But the present method used to dope Spiro-OMeTAD is slow, and causes the stability issue of perovskite solar cells. 

“We have now managed to eliminate the trade-off that has hindered development, using a new doping strategy for Spiro-OMeTAD. This makes it possible for us to obtain both high efficiency and good stability,” says Tiankai Zhang.

Another principal author of the article, Feng Wang, is a junior lecturer at IFM. He points out that perovskite-based solar cells can be used in many ways, and have many areas of applications.

“One advantage of using perovskites is that the solar cells made are thin, which means that they are light and flexible. They can also be semi-transparent. It would be possible, for example, to apply perovskite-based solar cells onto large windows, or building façades. Silicon-based solar cells are too heavy to be used in this way,” says Feng Wang.

The study has been financed by the Swedish Research Council, an ERC Starting Grant, the Knut and Alice Wallenberg Foundation and AFM (the Swedish Government Strategic Research Area in Materials Science on Functional Materials) at Linköping University. Feng Gao is also a Wallenberg Academy Fellow.

CAPTION

Solar cells manufactured from materials known as “perovskites” are catching up with the efficiency of traditional silicon-based solar cells.

CREDIT

Anna Nilsen

---

JOURNAL

Science

DOI

10.1126/science.abo2757 

ARTICLE TITLE

Ion-modulated radical doping of spiro-OMeTAD for more efficient and stable perovskite solar cells

NREL-led breakthrough pushes perovskite cell to greater stability, efficiency

Peer-Reviewed Publication

DOE/NATIONAL RENEWABLE ENERGY LABORATORY

Researchers at the U.S. Department of Energy’s (DOE’s) National Renewable Energy Laboratory (NREL) made a technological breakthrough and constructed a perovskite solar cell with the dual benefits of being both highly efficient and highly stable.

The work was done in collaboration with scientists from the University of Toledo, the University of Colorado–Boulder, and the University of California–San Diego.

A unique architectural structure enabled the researchers to record a certified stabilized efficiency of 24% under 1-sun illumination, making it the highest reported of its kind. The highly efficient cell also retained 87% of its original efficiency after 2,400 hours of operation at 55 degrees Celsius.

The paper, “Surface Reaction for Efficient and Stable Inverted Perovskite Solar Cells,” appears in the journal Nature. The authors from NREL are Qi Jiang, Jinhui Tong, Ross Kerner, Sean Dunfield, Chuanxiao Xiao, Rebecca Scheidt, Darius Kuciauskas, Matthew Hautzinger, Robert Tirawat, Matthew Beard, Joseph Berry, Bryon Larson, and Kai Zhu.

Perovskite, which refers to a crystalline structure, has emerged in the last decade as an impressive means to efficiently capture sunlight and convert it to electricity. Research into perovskite solar cells has been focused to a large degree on how to increase their stability.

“Some people can demonstrate perovskites with high stability, but efficiency is lower,” said Zhu, a senior scientist in the Chemistry and Nanoscience Center at NREL. “You ought to have high efficiency and high stability simultaneously. That’s challenging.”

The researchers used an inverted architecture, rather than the “normal” architecture that has to date yielded the highest efficiencies. The difference between the two types is defined by how the layers are deposited on the glass substrate. The inverted perovskite architecture is known for its high stability and integration into tandem solar cells. The NREL-led team also added a new molecule, 3-(Aminomethyl) pyridine (3-APy), to the surface of the perovskite. The molecule reacted to the formamidinium within the perovskite to create an electric field on the surface of the perovskite layer.

“That suddenly gave us a huge boost of not only efficiency but also stability,” Zhu said.

The scientists reported the 3-APy reactive surface engineering can improve the efficiency of an inverted cell from less than 23% to greater than 25%. They also noted the reactive surface engineering stands out as an effective approach to significantly enhance the performance of inverted cells “to new state-of-the-art levels of efficiency and operational reliability.”

Funding for the research done at NREL came from the Center for Hybrid Organic-Inorganic Semiconductors for Energy (CHOISE), an Energy Frontier Research Center within DOE’s Office of Basic Energy Sciences, and from the DOE’s Solar Energy Technologies Office.

NREL is the U.S. Department of Energy's primary national laboratory for renewable energy and energy efficiency research and development. NREL is operated for the Energy Department by the Alliance for Sustainable Energy, LLC.

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JOURNAL

Nature

DOI

10.1038/s41586-022-05268 

METHOD OF RESEARCH

Experimental study

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Surface reaction for efficient and stable inverted perovskite solar cells

Canadian Researchers develop novel way to prevent waterborne infectious diseases at refugee settlements

Peer-Reviewed Publication

YORK UNIVERSITY

 

IMAGE: YORK UNIVERSITY PHD STUDENT MICHAEL DE SANTI view more 

CREDIT: YORK UNIVERSITY

TORONTO, Sept. 6, 2022 - Waterborne illness is one of the leading causes of infectious disease outbreaks in refugee and internally displaced persons (IDP) settlements, but a team led by York University has developed a new technique to keep drinking water safe using machine learning, and it could be a game changer.

As drinking water is not piped into homes in most settlements, residents instead collect it from public tap stands using storage containers.

“When water is stored in a container in a dwelling it is at high risk of being exposed to contaminants, so it’s imperative there is enough free residual chlorine to kill any pathogens,” says Lassonde School of Engineering PhD student Michael De Santi, part of York’s Dahdaleh Institute for Global Health Research, who led the research.

Recontamination of previously safe drinking water during its collection, transport and storage has been a major factor in outbreaks of cholera, hepatitis E, and shigellosis in refugee and IDP settlements in Kenya, Malawi, Sudan, South Sudan, and Uganda.

“A variety of factors can affect chlorine decay in stored water. You can have safe water at that collection point, but once you bring it home and store it, sometimes up to 24 hours, you can lose that residual chlorine, pathogens can thrive and illness can spread,” says Lassonde Adjunct Professor Syed Imran Ali, a Research Fellow at York's Dahdaleh Institute for Global Health Research, who has first-hand experience working in a settlement in South Sudan.

Using machine learning, the research team, including Associate Professor Usman Khan also of Lassonde, has developed a new way to predict the probability that enough chlorine will remain until the last glass is consumed. They used an artificial neural network (ANN) along with ensemble forecasting systems (EFS), something that is not typically done. EFS is a probabilistic model commonly used to predict the probability of precipitation in weather forecasts.

“ANN-EFS can generate forecasts at the time of consumption that take a variety of factors into consideration that affect the level of residual chlorine, unlike the typically used models. This new probabilistic modelling is replacing the currently used universal guideline for chlorine use, which has been shown to be ineffective,” says Ali.

Factors such as local temperature, how the water is stored and handled from home to home, the type and quality of the water pipes, water quality or did a child dipped their hand in the water container, can all play a role in how safe the water is to drink.

“However, it’s really important that these probabilistic models be trained on data at a specific settlement as each one is as unique as a snowflake,” says De Santi. “Two people could collect the same water on the same day, both store it for six hours, and one could still have all the chlorine remaining in the water and the other could have almost none of it left. Another 10 people could have varying ranges of chlorine.”

The researchers used routine water quality monitoring data from two refugee settlements in Bangladesh and Tanzania collected through the Safe Water Optimization Tool Project. In Bangladesh, the data was collected from 2,130 samples by Médecins Sans Frontières from Camp 1 of the Kutupalong-Balukhali Extension Site, Cox’s Bazaar between June and December 2019 when it hosted 83,000 Rohingya refugees from neighbouring Myanmar.

Determining how to teach the ANN-EFS to come up with realistic probability forecasts with the smallest possible error required out-of-the-box thinking.

“How that error is measured is key as it determines how the model behaves in the context of probabilistic modelling,” says De Santi. “Using cost-sensitive learning, a tool that morphs the cost function towards a targeted behaviour when using machine learning, we found it could improve probabilistic forecasts and reliability. We are not aware of this being done before in this context.”

For example, this model can say that under certain conditions at the tap with a particular amount of free residual chlorine in the water, there is a 90 per cent chance that the remaining chlorine in the stored water after 15 hours will be below the safety level for drinking.

“That’s the kind of probabilistic determination this modelling can give us,” says De Santi. “Like with weather forecasts, if there is a 90 per cent chance of rain, you should bring an umbrella. Instead of an umbrella, we can ask water operators to increase the chlorine concentration so there will be a greater percentage of people with safe drinking water.”

“Our Safe Water Optimization Tool takes this machine learning work and makes it available to aid workers in the field. The only difference for the water operators is we ask them to sample water in the container at the tap and in that same container at the home after several hours,” says Ali.

“This work Michael is doing is advancing the state of practice of machine learning models. Not only can this be used to ensure safe drinking water in refugee and IDP settlements, it can also be used in other applications.”

The paper, Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?, will be published in the journal PLOS Water.

De Santi will deliver a seminar on this paper as part of the Dahdaleh Institute Seminar Series on Sept. 7, from 1 to 2 p.m. EST. It is open to the public and registration is free.

Photos: Headshot of Michael De Santi, Syed Imran Ali and Usman Khan

CAPTION

Lassonde School of Enginneering Adjunct Professor Syed Imran Ali, a Research Fellow at York University's Dahdaleh Institute for Global Health Research

CREDIT

York University

CAPTION

Associate Professor Usman Khan of York University's Lassonde School of Engineering

CREDIT

York University

York University is a modern, multi-campus, urban university located in Toronto, Ontario. Backed by a diverse group of students, faculty, staff, alumni and partners, we bring a uniquely global perspective to help solve societal challenges, drive positive change and prepare our students for success. York's fully bilingual Glendon Campus is home to Southern Ontario's Centre of Excellence for French Language and Bilingual Postsecondary Education. York’s campuses in Costa Rica and India offer students exceptional transnational learning opportunities and innovative programs. Together, we can make things right for our communities, our planet, and our future. 

Media Contact:

Sandra McLean, York University Media Relations, 416-272-6317, sandramc@yorku.ca

---

JOURNAL

PLOS Water

DOI

10.1371/journal.pwat.0000040 

ARTICLE TITLE

Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?

ARTICLE PUBLICATION DATE

6-Sep-2022

Researchers develop new technique to keep drinking water safe using machine learning

by York University

Credit: CC0 Public Domain

Waterborne illness is one of the leading causes of infectious disease outbreaks in refugee and internally displaced persons (IDP) settlements, but a team led by York University has developed a new technique to keep drinking water safe using machine learning, and it could be a game changer. The research is published in the journal PLOS Water.

As drinking water is not piped into homes in most settlements, residents instead collect it from public tap stands using storage containers.

"When water is stored in a container in a dwelling it is at high risk of being exposed to contaminants, so it's imperative there is enough free residual chlorine to kill any pathogens," says Lassonde School of Engineering Ph.D. student Michael De Santi, who is part of York's Dahdaleh Institute for Global Health Research, and who led the research.

Recontamination of previously safe drinking water during its collection, transport and storage has been a major factor in outbreaks of cholera, hepatitis E, and shigellosis in refugee and IDP settlements in Kenya, Malawi, Sudan, South Sudan, and Uganda.

"A variety of factors can affect chlorine decay in stored water. You can have safe water at that collection point, but once you bring it home and store it, sometimes up to 24 hours, you can lose that residual chlorine, pathogens can thrive and illness can spread," says Lassonde Adjunct Professor Syed Imran Ali, a Research Fellow at York's Dahdaleh Institute for Global Health Research, who has firsthand experience working in a settlement in South Sudan.

Using machine learning, the research team—including Associate Professor Usman Khan, also of Lassonde—has developed a new way to predict the probability that enough chlorine will remain until the last glass is consumed. They used an artificial neural network (ANN) along with ensemble forecasting systems (EFS), something that is not typically done. EFS is a probabilistic model commonly used to predict the probability of precipitation in weather forecasts.

"ANN-EFS can generate forecasts at the time of consumption that take a variety of factors into consideration that affect the level of residual chlorine, unlike the typically used models. This new probabilistic modeling is replacing the currently used universal guideline for chlorine use, which has been shown to be ineffective," says Ali.

Factors such as local temperature, how the water is stored and handled from home to home, the type and quality of the water pipes, water quality and whether a child dipped their hand in the water container can all play a role in how safe the water is to drink.

"However, it's really important that these probabilistic models be trained on data at a specific settlement as each one is as unique as a snowflake," says De Santi. "Two people could collect the same water on the same day, both store it for six hours, and one could still have all the chlorine remaining in the water and the other could have almost none of it left. Another 10 people could have varying ranges of chlorine."

The researchers used routine water quality monitoring data from two refugee settlements in Bangladesh and Tanzania collected through the Safe Water Optimization Tool Project. In Bangladesh, the data was collected from 2,130 samples by Médecins Sans Frontières from Camp 1 of the Kutupalong-Balukhali Extension Site, Cox's Bazaar between June and December 2019 when it hosted 83,000 Rohingya refugees from neighboring Myanmar.

Determining how to teach the ANN-EFS to come up with realistic probability forecasts with the smallest possible error required out-of-the-box thinking.

"How that error is measured is key as it determines how the model behaves in the context of probabilistic modeling," says De Santi. "Using cost-sensitive learning, a tool that morphs the cost function towards a targeted behavior when using machine learning, we found it could improve probabilistic forecasts and reliability. We are not aware of this being done before in this context."

For example, this model can say that under certain conditions at the tap with a particular amount of free residual chlorine in the water, there is a 90 percent chance that the remaining chlorine in the stored water after 15 hours will be below the safety level for drinking.

"That's the kind of probabilistic determination this modeling can give us," says De Santi. "Like with weather forecasts, if there is a 90 percent chance of rain, you should bring an umbrella. Instead of an umbrella, we can ask water operators to increase the chlorine concentration so there will be a greater percentage of people with safe drinking water."

"Our Safe Water Optimization Tool takes this machine learning work and makes it available to aid workers in the field. The only difference for the water operators is we ask them to sample water in the container at the tap and in that same container at the home after several hours," says Ali.

"This work Michael is doing is advancing the state of practice of machine learning models. Not only can this be used to ensure safe drinking water in refugee and IDP settlements, it can also be used in other applications."

How to deliver drinking water chlorine-free

More information: Michael De Santi et al, Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?, PLOS Water (2022). DOI: 10.1371/journal.pwat.000004

Provided by York University 

Walking and slithering aren't as different as you think

At least, if you have enough legs

Peer-Reviewed Publication

UNIVERSITY OF MICHIGAN

Images/Video 

Abrahamic texts treat slithering as a special indignity visited on the wicked serpent, but evolution may draw a more continuous line through the motion of swimming microbes, wriggling worms, skittering spiders and walking horses. 

A new study found that all of these kinds of motion are well represented by a single mathematical model.

"This didn't come out of nowhere—this is from our real robot data," said Dan Zhao, first author of the study in the Proceedings of the National Academy of Sciences and a recent Ph.D. graduate in mechanical engineering at the University of Michigan.

"Even when the robot looks like it's sliding, like its feet are slipping, its velocity is still proportional to how quickly it's moving its body."

Unlike the dynamic motion of gliding birds and sharks and galloping horses—where speed is driven, at least in part, by momentum—every bit of speed for ants, centipedes, snakes and swimming microbes is driven by changing the shape of the body. This is known as kinematic motion.

The expanded understanding of kinematic motion could change the way roboticists think about programming many-limbed robots, opening new possibilities for walking planetary rovers, for instance. 

Shai Revzen, professor of electrical and computer engineering at U-M and senior author of the study, explained that two- and four-legged robots are popular because more legs are extremely complex to model using current tools. 

"This never sat well with me because my work was on cockroach locomotion," Revzen said. "I can tell you many things about cockroaches. One of them is that they're not brilliant mathematicians."

And if cockroaches can walk without solving extremely complex equations, there has to be an easier way to program walking robots. The new finding offers a place to start.

Slipping feet complicates typical motion models for robots, and the assumption was that it might add an element of momentum to the motion of many-legged robots. But in the model reported by the U-M team, it is not so different from lizards that "swim" in sand or microbes swimming in water. 

Because microbes are small, the water seems a lot thicker and stickier—as if a human was trying to swim in honey. In all of these cases, the limbs move through the surrounding medium, or slide over a surface, rather than being connected at a stationary point.

The team discovered the connection by taking a known model that describes swimming microbes and then reconfiguring it to use with their multi-legged robots. The model reliably reflected their data, which came from multipods—modular robots that can operate with 6 to 12 legs—and a six-legged robot called BigAnt. 

The team also collaborated with Glenna Clifton, assistant professor of biology at the University of Portland in Oregon, who provided data on ants walking on a flat surface. While the robot legs slip a lot—up to 100% of the time for the multipods—ant feet have much firmer connections with the ground, slipping only 4.7% of the time. 

Even so, the ants and robots followed the same equations, with their speeds proportional to how quickly they moved their legs. It turned out that this kind of slipping didn't alter the kinematic nature of the motion.

As for what this suggests about how walking evolved, the team points to the worm believed to be the last common ancestor for all creatures that have two sides that are mirror images of each other. This worm, wriggling through water, already had the foundations of the motion that enabled the first animals to walk on land, they propose. Even humans begin learning to propel ourselves kinematically, crawling on hands and knees with the three points of contact on the ground at any time.

The skills of managing momentum—running with four legs or fewer, walking or running on two legs, flying or gliding—ladder on top of that older knowledge about how to move, the researchers suggest.

The research was supported by the Army Research Office (grants W911NF-17-1-0243 and W911NF-17-1-0306), the National Science Foundation (grants 1825918 and 2048235) and the D. Dan and Betty Kahn Michigan-Israel Partnership for Research and Education Autonomous Systems Mega-Project.

Zhao is now a senior controls engineer at XPENG Robotics.

Study: Walking is like slithering: a unifying, data-driven view of locomotion (DOI: 10.1073/pnas.202113222)

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JOURNAL

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.202113222 

Sept. 6, 2022

Contact: Katherine McAlpine, kmca@umich.edu

Captions: Walking and slithering aren't as

different as you think 

Produced video for social media MP4 | YouTube

Credit: Hans Anderson, Michigan News

Multipod videos

Data collected using modular robots with six to

twelve legs helped to show that many-legged walking,

slithering and some kinds of swimming follow the same

mathematical model.

Credit: BIRDS Lab, University of Michigan

Ant videos

Data gathered from ants helped to show that many-legged walking,

slithering and some kinds of swimming follow the same mathematical model.

Credit: Clifton Group, University of Portland

BigAnt photos and videos

A six-legged ant-inspired robot. Data collected with this robot

helped to show that many-legged walking, slithering and some

kinds of swimming follow the same mathematical model.

Credit: BIRDS Lab, University of Michigan

The physics of walking is simpler than we thought

Walking for multi-legged creatures is a lot like slithering, researchers find by comparing ants to robots

Peer-Reviewed Publication

UNIVERSITY OF CALIFORNIA - SAN DIEGO

 

VIDEO: B-ROLL OF ANTS WALKING, A MULTIPOD ROBOT WALKING AND THE BIGANT ROBOT WALKING. view more 

CREDIT: UNIVERSITY OF CALIFORNIA SAN DIEGO/UNIVERSITY OF MICHIGAN

The physics of walking for multi-legged animals and robots is simpler than previously thought. That is the finding described by a team of roboticists, physicists and biologists in the Sept. 5 issue of the Proceedings of the National Academy of Sciences, in a paper titled “Walking is like slithering: a unifying, data-driven view of locomotion.” 

“This is important because it will allow roboticists to build much simpler models to describe the way robots walk and move through the world,” said paper coauthor Nick Gravish, a faculty member in the Department of Mechanical and Aerospace Engineering at the University of California San Diego.

The researchers had previously studied ant walking and wanted to see how their findings could be applied to robots. In the process, they discovered a new mathematical relationship between walking, skipping, slithering and swimming in viscous fluids for multi-legged animals and bots. 

The team studied several colonies of Argentine ants at UC San Diego, and two different types of multi-legged robots at the University of Michigan. 

“Argentine ants are very easy to study in the lab,” said paper coauthor Glenna Clifton, a faculty member at the University of Portland, who conducted most of the ant research while she was a postdoctoral scholar in Gravish’s lab at UC San Diego. 

Argentine ants are good walkers that can go long distances over various terrains. These ants also easily acclimate to lab settings, rebuilding their colonies quickly. Researchers then can motivate them to walk by placing food in specific locations. “These ants will set up foraging trails and follow them,” Clifton said. “They bounce back quickly and they don’t hold a grudge.” 

To study these different animals and robots, researchers used an algorithm developed by the research group of Shai Revzen at the University of Michigan, which turns complex body motions into shapes. “This algorithm allows us to create a simple relationship between what posture you’re in and where you are going to move next,” Gravish said. 

The researchers found that the same algorithms could be applied both to ants and the two different types of robots in the study, even though the amount of slipping motions when they walk differs widely. Argentine ants also don’t slip much when they walk – just 4.7% of total motion. By contrast, that slipping percentage is 12% to 22% for the six-legged BigANT robot and 40% to 100% for the multipod robots with six to 12 legs in the study, which sometimes crawl. 

 By using this model, researchers can predict where the insect or robot is going to move next simply based on what posture–or shape–they’re making.  “This provides a universal model for location that applies whenever the movement is dominated by friction with the environment,” the researchers write. 

The mathematics the researchers used aren’t new. But the math was understood to only apply to slithering and swimming in viscous liquids. The team showed that the same equations apply to multi-legged walking, whether the walkers are slipping or not. In addition, the same rules apply from millimeter-scale insects, such as ants, to meter-scale robots. An early version of the paper title was “walking like a worm.” 

“The universality of this approach may have applications in robot design and motion planning, and provides insight into the evolution and control of legged locomotion,” the researchers write. 

Researchers hypothesize that these universal principles may have implications for understanding major evolutionary transitions, for example from swimming to walking. Given that walking, even with slipping, follows the same general control principles as viscous swimming, early land animals might already have had the neural circuitry needed for locomotion on land. 

Researchers didn’t study two-legged creatures, but the model would apply to them as long as they move slowly; have both feet on the ground at the same time; and do not fall. (Picture Michael Jackson doing the moonwalk.) 

The team still has more fine tuning to do, to understand, for example, the role friction forces play in the model. 

“Either way, walking can be much simpler than we usually think,” Gravish said. 

Walking is like slithering: a unifying, data-driven view of locomotion

University of Michigan: Dan Zhao, Brian Bittner, Shai Revzen 

University of Portland: Glenna Clifton 

University of California San Diego: Nick Gravish

 

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JOURNAL

Proceedings of the National Academy of Sciences

METHOD OF RESEARCH

Experimental study

SUBJECT OF RESEARCH

Animals

ARTICLE TITLE

Walking is like slithering: A unifying, data-driven view of locomotion

ARTICLE PUBLICATION DATE

9-Sep-2022

CAPTION

Experimental equipment to study ants walking similar to the one used in the study

CREDIT

David Baillot/University of California San Diego

CAPTION

Researchers Glenna Clifton, from the University of Portland, and Nick Gravish, from the University of California San Diego, harvest ants on the UC San Diego campus.

CREDIT

David Baillot/University of California San Diego