Floating “Artificial Leaves” Generate Clean Fuels From Sunlight and Water

Scientists have developed floating ‘artificial leaves’ that generate clean fuels from sunlight and water. They could eventually operate on a large scale at sea.

Abstract

Floating perovskite-BiVO4 devices for scalable solar fuel production Photoelectrochemical (PEC) artificial leaves hold the potential to lower the costs of sustainable solar fuel production by integrating light harvesting and catalysis within one compact device. However, current deposition techniques limit their scalability1, whereas fragile and heavy bulk materials can affect their transport and deployment. Here we demonstrate the fabrication of lightweight artificial leaves by employing thin, flexible substrates and carbonaceous protection layers. Lead halide perovskite photocathodes deposited onto indium tin oxide-coated polyethylene terephthalate achieved an activity of 4,266 µmol H2 g−1 h−1 using a platinum catalyst, whereas photocathodes with a molecular Co catalyst for CO2 reduction attained a high CO:H2 selectivity of 7.2 under lower (0.1 sun) irradiation. The corresponding lightweight perovskite-BiVO4 PEC devices showed unassisted solar-to-fuel efficiencies of 0.58% (H2) and 0.053% (CO), respectively. Their potential for scalability is demonstrated by 100 cm2 stand-alone artificial leaves, which sustained a comparable performance and stability (of approximately 24 h) to their 1.7 cm2 counterparts. Bubbles formed under operation further enabled 30–100 mg cm−2 devices to float, while lightweight reactors facilitated gas collection during outdoor testing on a river. This leaf-like PEC device bridges the gulf in weight between traditional solar fuel approaches, showcasing activities per gram comparable to those of photocatalytic suspensions and plant leaves. The presented lightweight, floating systems may enable open-water applications, thus avoiding competition with land use.

The ultra-thin, flexible devices, which take their inspiration from photosynthesis – the process by which plants convert sunlight into food – were designed by researchers from the University of Cambridge. Since the low-cost, autonomous devices are light enough to float, they could be used to generate a sustainable alternative to gasoline without taking up space on land.

Outdoor tests of the lightweight leaves on the River Cam showed that they can convert sunlight into fuels as efficiently as plant leaves. River Cam is the main river flowing through Cambridge in eastern England, and the testing occurred near iconic Cambridge sites including the Bridge of Sighs, the Wren Library, and King’s College Chapel.

This is the first time that clean fuel has been generated on water. If it were scaled up, the artificial leaves could be used on polluted waterways, in ports, or even at sea, and could help reduce the global shipping industry’s reliance on fossil fuels. The results are reported today (August 17, 2022) in the journal Nature.

Renewable energy technologies, such as wind and solar, have become significantly cheaper and more available in recent years. However, for industries such as shipping, decarbonization is a much taller order. Around 80% of global trade is transported by cargo vessels powered by fossil fuels, yet the sector has received remarkably little attention in discussions related to the climate crisis.

For several years, Professor Erwin Reisner’s research group in Cambridge has been working to address this problem by developing sustainable solutions to gasoline that are based on the principles of photosynthesis. In 2019, they developed an artificial leaf, which makes syngas from sunlight, carbon dioxide, and water. Syngas is a key intermediate in the production of many chemicals and pharmaceuticals.

The earlier prototype produced fuel by combining two light absorbers with suitable catalysts. However, it incorporated thick glass substrates and moisture-protective coatings, which made the device bulky.

“Artificial leaves could substantially lower the cost of sustainable fuel production, but since they’re both heavy and fragile, they’re difficult to produce at scale and transport,” said Dr. Virgil Andrei from Cambridge’s Yusuf Hamied Department of Chemistry, the paper’s co-lead author.

“We wanted to see how far we can trim down the materials these devices use, while not affecting their performance,” said Reisner, who led the research. “If we can trim the materials down far enough that they’re light enough to float, then it opens up whole new ways that these artificial leaves could be used.”

For the new version of the artificial leaf, the scientists took their inspiration from the electronics industry. Miniaturization techniques there have led to the creation of smartphones and flexible displays, revolutionizing the field.

The challenge for the Cambridge research team was how to deposit light absorbers onto lightweight substrates and protect them against water infiltration. To overcome these challenges, the researchers used thin-film metal oxides, and materials known as perovskites, which can be coated onto flexible plastic and metal foils. The devices were covered with micrometer-thin, water-repellent carbon-based layers that prevented moisture degradation. The result was a device that not only works, but also looks like a real leaf.

“This study demonstrates that artificial leaves are compatible with modern fabrication techniques, representing an early step towards the automation and up-scaling of solar fuel production,” said Andrei. “These leaves combine the advantages of most solar fuel technologies, as they achieve the low weight of powder suspensions and the high performance of wired systems.”

Tests of the new artificial leaves demonstrated that they can split water into hydrogen and oxygen, or reduce CO2 to syngas. While additional improvements will need to be made before they are ready for commercial applications, the scientists say this development opens whole new avenues in their work.

“Solar farms have become popular for electricity production; we envision similar farms for fuel synthesis,” said Andrei. “These could supply coastal settlements, remote islands, cover industrial ponds, or avoid water evaporation from irrigation canals.”

“Many renewable energy technologies, including solar fuel technologies, can take up large amounts of space on land, so moving production to open water would mean that clean energy and land use aren’t competing with one another,” said Reisner. “In theory, you could roll up these devices and put them almost anywhere, in almost any country, which would also help with energy security.”

Reference: “Floating perovskite-BiVO4 devices for scalable solar fuel production” 17 August 2022, Nature. DOI: 10.1038/s41586-022-04978-6 The research was supported in part by the European Research Council, the Cambridge Trust, the Winton Programme for the Physics of Sustainability, the Royal Academy of Engineering, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Virgil Andrei and Erwin Reisner are Fellows of St John’s College, Cambridge.

Predicting Others’ Behavior on the Road With Artificial Intelligence

M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction

Abstract

Predicting future motions of road participants is an important task for driving autonomously in urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet it remains an open question to jointly predict scene compliant trajectories over multiple agents. The challenge is due to exponentially increasing prediction space as a function of the number of agents. In this work, we exploit the underlying relations between interacting agents and decouple the joint prediction problem into marginal prediction problems. Our proposed approach M2I first classifies interacting agents as pairs of influencers and reactors, and then leverages a marginal prediction model and a conditional prediction model to predict trajectories for the influencers and reactors, respectively. The predictions from interacting agents are combined and selected according to their joint likelihoods. Experiments show that our simple but effective approach achieves state-of-the-art performance on the Waymo Open Motion Dataset interactive prediction benchmark.

Researchers have created a machine-learning system that efficiently predicts the future trajectories of multiple road users, like drivers, cyclists, and pedestrians, which could enable an autonomous vehicle to more safely navigate city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, cyclists, and pedestrians are going to do next. Credit: MIT

A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, pedestrians, and cyclists in real-time.

Humans may be one of the biggest roadblocks to fully autonomous vehicles operating on city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, pedestrians, and cyclists are going to do next.

Behavior prediction is a tough problem, however, and current artificial intelligence solutions are either too simplistic (they may assume pedestrians always walk in a straight line), too conservative (to avoid pedestrians, the robot just leaves the car in park), or can only forecast the next moves of one agent (roads typically carry many users at once.)

MIT researchers have devised a deceptively simple solution to this complicated challenge. They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time.

These simulations show how the system the researchers developed can predict the future trajectories (shown using red lines) of the blue vehicles in complex traffic situations involving other cars, bicyclists, and pedestrians. Credit: MIT

Their behavior-prediction framework first guesses the relationships between two road users — which car, cyclist, or pedestrian has the right of way, and which agent will yield — and uses those relationships to predict future trajectories for multiple agents.

These estimated trajectories were more accurate than those from other machine-learning models, compared to real traffic flow in an enormous dataset compiled by autonomous driving company Waymo. The MIT technique even outperformed Waymo’s recently published model. And because the researchers broke the problem into simpler pieces, their technique used less memory.

“This is a very intuitive idea, but no one has fully explored it before, and it works quite well. The simplicity is definitely a plus. We are comparing our model with other state-of-the-art models in the field, including the one from Waymo, the leading company in this area, and our model achieves top performance on this challenging benchmark. This has a lot of potential for the future,” says co-lead author Xin “Cyrus” Huang, a graduate student in the Department of Aeronautics and Astronautics and a research assistant in the lab of Brian Williams, professor of aeronautics and astronautics and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Joining Huang and Williams on the paper are three researchers from Tsinghua University in China: co-lead author Qiao Sun, a research assistant; Junru Gu, a graduate student; and senior author Hang Zhao PhD ’19, an assistant professor. The research will be presented at the Conference on Computer Vision and Pattern Recognition.

The researchers’ machine-learning method, called M2I, takes two inputs: past trajectories of the cars, cyclists, and pedestrians interacting in a traffic setting such as a four-way intersection, and a map with street locations, lane configurations, etc.

Using this information, a relation predictor infers which of two agents has the right of way first, classifying one as a passer and one as a yielder. Then a prediction model, known as a marginal predictor, guesses the trajectory for the passing agent, since this agent behaves independently.

A second prediction model, known as a conditional predictor, then guesses what the yielding agent will do based on the actions of the passing agent. The system predicts a number of different trajectories for the yielder and passer, computes the probability of each one individually, and then selects the six joint results with the highest likelihood of occurring.

M2I outputs a prediction of how these agents will move through traffic for the next eight seconds. In one example, their method caused a vehicle to slow down so a pedestrian could cross the street, then speed up when they cleared the intersection. In another example, the vehicle waited until several cars had passed before turning from a side street onto a busy, main road.

While this initial research focuses on interactions between two agents, M2I could infer relationships among many agents and then guess their trajectories by linking multiple marginal and conditional predictors.

The researchers trained the models using the Waymo Open Motion Dataset, which contains millions of real traffic scenes involving vehicles, pedestrians, and cyclists recorded by lidar (light detection and ranging) sensors and cameras mounted on the company’s autonomous vehicles. They focused specifically on cases with multiple agents.

To determine accuracy, they compared each method’s six prediction samples, weighted by their confidence levels, to the actual trajectories followed by the cars, cyclists, and pedestrians in a scene. Their method was the most accurate. It also outperformed the baseline models on a metric known as overlap rate; if two trajectories overlap, that indicates a collision. M2I had the lowest overlap rate.

“Rather than just building a more complex model to solve this problem, we took an approach that is more like how a human thinks when they reason about interactions with others. A human does not reason about all hundreds of combinations of future behaviors. We make decisions quite fast,” Huang says.

Another advantage of M2I is that, because it breaks the problem down into smaller pieces, it is easier for a user to understand the model’s decision-making. In the long run, that could help users put more trust in autonomous vehicles, says Huang

But the framework can’t account for cases where two agents are mutually influencing each other, like when two vehicles each nudge forward at a four-way stop because the drivers aren’t sure who should be yielding.

They plan to address this limitation in future work. They also want to use their method to simulate realistic interactions between road users, which could be used to verify planning algorithms for self-driving cars or create huge amounts of synthetic driving data to improve model performance.

“Predicting future trajectories of multiple, interacting agents is under-explored and extremely challenging for enabling full autonomy in complex scenes. M2I provides a highly promising prediction method with the relation predictor to discriminate agents predicted marginally or conditionally which significantly simplifies the problem,” wrote Masayoshi Tomizuka, the Cheryl and John Neerhout, Jr. Distinguished Professor of Mechanical Engineering at University of California at Berkeley and Wei Zhan, an assistant professional researcher, in an email. “The prediction model can capture the inherent relation and interactions of the agents to achieve the state-of-the-art performance.” The two colleagues were not involved in the research.

Reference: “M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction” by Qiao Sun, Xin Huang, Junru Gu, Brian C. Williams and Hang Zhao. 28 March 2022, Computer Science > Robotics. arXiv:2202.11884

Artificial Intelligence Computing Using Networks of Tiny Nanomagnets

Abstract

Strongly interacting artificial spin systems are moving beyond mimicking naturally occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically, artificial spin systems comprise nanomagnets with a single magnetization texture: collinear macrospins or chiral vortices. By tuning nanoarray dimensions we have achieved macrospin–vortex bistability and demonstrated a four-state metamaterial spin system, the ‘artificial spin-vortex ice’ (ASVI). ASVI can host Ising-like macrospins with strong ice-like vertex interactions and weakly coupled vortices with low stray dipolar field. Vortices and macrospins exhibit starkly differing spin-wave spectra with analogue mode amplitude control and mode frequency shifts of Δf = 3.8 GHz. The enhanced bitextural microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex injection and history-dependent non-linear fading memory when driven through global magnetic field cycles. We employed spin-wave microstate fingerprinting for rapid, scalable readout of vortex and macrospin populations, and leveraged this for spin-wave reservoir computation. ASVI performs non-linear mapping transformations of diverse input and target signals in addition to chaotic time-series forecasting.

The new technology, developed by a team led by Imperial College London researchers, could significantly reduce the energy cost of artificial intelligence (AI), which is currently doubling globally every 3.5 months.

In a paper published today (May 5, 2022) in the journal Nature Nanotechnology, the international team has produced the first proof that networks of nanomagnets can be used to perform AI-like processing. The researchers showed nanomagnets can be used for ‘time-series prediction’ tasks, such as predicting and regulating insulin levels in diabetic patients.

Artificial intelligence that uses ‘neural networks’ aims to replicate the way parts of the brain work, where neurons talk to each other to process and retain information. A lot of the maths used to power neural networks was originally invented by physicists to describe the way magnets interact, but at the time it was too difficult to use magnets directly as researchers didn’t know how to put data in and get information out.

Instead, software run on traditional silicon-based computers was used to simulate the magnet interactions, in turn simulating the brain. Now, the team have been able to use the magnets themselves to process and store data – cutting out the middleman of the software simulation and potentially offering enormous energy savings.

Nanomagnetic states

Nanomagnets can come in various ‘states’, depending on their direction. Applying a magnetic field to a network of nanomagnets changes the state of the magnets based on the properties of the input field, but also on the states of surrounding magnets.

The team, led by Imperial Department of Physics researchers, were then able to design a technique to count the number of magnets in each state once the field has passed through, giving the ‘answer’.

Co-first author of the study Dr. Jack Gartside said: “We’ve been trying to crack the problem of how to input data, ask a question, and get an answer out of magnetic computing for a long time. Now we’ve proven it can be done, it paves the way for getting rid of the computer software that does the energy-intensive simulation.”

Co-first author Kilian Stenning added: “How the magnets interact gives us all the information we need; the laws of physics themselves become the computer.”

Team leader Dr. Will Branford said: “It has been a long-term goal to realize computer hardware inspired by the software algorithms of Sherrington and Kirkpatrick. It was not possible using the spins on atoms in conventional magnets, but by scaling up the spins into nanopatterned arrays we have been able to achieve the necessary control and readout.”

Slashing energy cost

AI is now used in a range of contexts, from voice recognition to self-driving cars. But training AI to do even relatively simple tasks can take huge amounts of energy. For example, training AI to solve a Rubik’s cube took the energy equivalent of two nuclear power stations running for an hour.

Much of the energy used to achieve this in conventional, silicon-chip computers is wasted in inefficient transport of electrons during processing and memory storage. Nanomagnets however don’t rely on the physical transport of particles like electrons, but instead process and transfer information in the form of a ‘magnon’ wave, where each magnet affects the state of neighboring magnets.

This means much less energy is lost, and that the processing and storage of information can be done together, rather than being separate processes as in conventional computers. This innovation could make nanomagnetic computing up to 100,000 times more efficient than conventional computing.

AI at the edge

The team will next teach the system using real-world data, such as ECG signals, and hope to make it into a real computing device. Eventually, magnetic systems could be integrated into conventional computers to improve energy efficiency for intense processing tasks.

Their energy efficiency also means they could feasibly be powered by renewable energy, and used to do ‘AI at the edge’ – processing the data where it is being collected, such as weather stations in Antarctica, rather than sending it back to large data centers.

It also means they could be used on wearable devices to process biometric data on the body, such as predicting and regulating insulin levels for diabetic people or detecting abnormal heartbeats.

Reference: “Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting” by Jack C. Gartside, Kilian D. Stenning, Alex Vanstone, Holly H. Holder, Daan M. Arroo, Troy Dion, Francesco Caravelli, Hidekazu Kurebayashi and Will R. Branford, 5 May 2022, Nature Nanotechnology. DOI: 10.1038/s41565-022-01091-7

Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting