Zinc-air Batteries Powering Electric Vehicles

Graphical abstract. Credit: EcoMat (2023). DOI: 10.1002/eom2.12394

Zinc-air batteries have emerged as a better alternative to lithium in a recent Edith Cowan University (ECU) study into the advancement of sustainable battery systems.

ECU's Dr. Muhammad Rizwan Azhar led the project which discovered lithium-ion batteries, although a popular choice for electric vehicles around the world, face limitations related to cost, finite resources, and safety concerns. The work is published in the journal EcoMat.

"Rechargeable zinc-air batteries (ZABs) are becoming more appealing because of their low cost, environmental friendliness, high theoretical energy density, and inherent safety," Dr. Muhammad Rizwan Azhar said.

"With the emergence of next-generation long-range vehicles and electric aircraft in the market, there is an increasing need for safer, more cost-effective, and high-performance battery systems that can surpass the capabilities of lithium-ion batteries."

Zinc-air: An explainer
A zinc–air battery consists of a zinc negative electrode and an air positive electrode.

The major disadvantage of these has been the limited power output, due to poor performance of air electrodes and short lifespan—until now.

ECU's breakthrough has enabled engineers to use a combination of new materials, such as carbon, cheaper iron and cobalt based minerals to redesign zinc-air batteries.  READ MORE...

Hydrogen From Sunlight

Series of four still images from a sample video showing how a photoreactor from Rice University splits water molecules and generates hydrogen when stimulated by simulated sunlight. Credit: Mohite lab/Rice University

Rice University engineers can turn sunlight into hydrogen with record-breaking efficiency thanks to a device that combines next-generation halide perovskite semiconductors with electrocatalysts in a single, durable, cost-effective and scalable device.

The new technology is a significant step forward for clean energy and could serve as a platform for a wide range of chemical reactions that use solar-harvested electricity to convert feedstocks into fuels.

The lab of chemical and biomolecular engineer Aditya Mohite built the integrated photoreactor using an anticorrosion barrier that insulates the semiconductor from water without impeding the transfer of electrons.

According to a study published in Nature Communications, the device achieved a 20.8% solar-to-hydrogen conversion efficiency.

"Using sunlight as an energy source to manufacture chemicals is one of the largest hurdles to a clean energy economy," said Austin Fehr, a chemical and biomolecular engineering doctoral student and one of the study's lead authors. 

"Our goal is to build economically feasible platforms that can generate solar-derived fuels. Here, we designed a system that absorbs light and completes electrochemical water-splitting chemistry on its surface."

The device is known as a photoelectrochemical cell because the absorption of light, its conversion into electricity and the use of the electricity to power a chemical reaction all occur in the same device. Until now, using photoelectrochemical technology to produce green hydrogen was hampered by low efficiencies and the high cost of semiconductors.  READ MORE...

Hydrogen Fuel Cell

Promising new hydrogen fuel cell technology has up to 50% higher performance than current state-of-the-art technology, with improved durability. 

The grooved electrode design advance may help optimize next-generation fuel cell technology to power emission-free medium- and heavy-duty transportation.

"We had a theory that by reimagining the way electrodes are designed we could achieve improved performance," said Jacob Spendelow, materials scientist with the Los Alamos National Laboratory team that described its results in the journal Nature Energy. 

"One of our biggest takeaways is that novel materials are not the only route to improve performance. The way the materials are put together can be equally important.

"All we did was take conventional commercially available materials and change the way we put them together to change the microscale architecture, and that resulted in substantially higher performance."

Hydrogen fuel cells—and specifically a version of the technology called proton exchange membrane fuel cells—represent an emission-free engine design that uses hydrogen as a fuel. Fuel cells could transform the medium- and heavy-duty transportation sector, which has been difficult to decarbonize.  READ MORE...

Controlling Autonomous Robots

In the film "Top Gun: Maverick," Maverick, played by Tom Cruise, is charged with training young pilots to complete a seemingly impossible mission—to fly their jets deep into a rocky canyon, staying so low to the ground they cannot be detected by radar, then rapidly climb out of the canyon at an extreme angle, avoiding the rock walls. 

Spoiler alert: With Maverick's help, these human pilots accomplish their mission.

A machine, on the other hand, would struggle to complete the same pulse-pounding task. To an autonomous aircraft, for instance, the most straightforward path toward the target is in conflict with what the machine needs to do to avoid colliding with the canyon walls or staying undetected. 

Many existing AI methods aren't able to overcome this conflict, known as the stabilize-avoid problem, and would be unable to reach their goal safely.  MIT researchers have developed a new technique that can solve complex stabilize-avoid problems better than other methods. 

Their machine-learning approach matches or exceeds the safety of existing methods while providing a tenfold increase in stability, meaning the agent reaches and remains stable within its goal region.

In an experiment that would make Maverick proud, their technique effectively piloted a simulated jet aircraft through a narrow corridor without crashing into the ground.  READ MORE...

Fusion's Future in USA

Fusion energy is often hailed as a limitless source of clean energy, but new research from Princeton University suggests that may only be true if the price is right.

In a study led by fusion expert Egemen Kolemen, associate professor of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment, and energy systems expert Jesse Jenkins, assistant professor of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment, Princeton researchers modeled the cost targets that a fusion reactor might have to meet to gain traction in a future U.S. energy grid.

The findings, published in Joule on Mar. 16, illustrated that the engineering challenges of fusion energy are only part of the problem—the other part lies in economics.

"People will not pay an unlimited amount of money for fusion energy if they could spend that money to generate clean energy more cost-effectively," said Jacob Schwartz, a former postdoc with Kolemen and Jenkins who led the modeling for the study and currently works as a research physicist at the Princeton Plasma Physics Laboratory. "Above a certain cost, even if we can engineer them, not many developers will want to build them."

The model results demonstrated that the niche for fusion in the U.S. depends not only on the price of building a reactor but hinges greatly on the energy mix of the future grid and the cost of competing technologies like nuclear fission.

If the market for fusion is favorable, then even with capital costs as high as around $7,000 per kilowatt, fusion could still reach 100 GW capacity—about the current capacity of U.S. nuclear power plants, which supply about a fifth of today's electricity needs. But supposing alternative technologies like nuclear fission, hydrogen, carbon capture and storage, or long-duration battery storage successfully take root, capital costs might have to be less than half that price for fusion to reach the same 100 GW capacity.

"Fusion developers need to keep an eye on the competition," Jenkins explained. "If successfully commercialized, fusion power plants are likely to look a lot like classic nuclear fission plants from the perspective of electricity markets and grids. Both resources are complex technologies with tight engineering margins for safety reasons, which translates to high upfront investment costs. If the variable costs of fusion power plants end up low, fusion plants will likely compete head-to-head with new fission power plants."  READ MORE...

Deep Reinforcement Learning

Scientists have taken a key step toward harnessing a form of artificial intelligence known as deep reinforcement learning, or DRL, to protect computer networks.

When faced with sophisticated cyberattacks in a rigorous simulation setting, deep reinforcement learning was effective at stopping adversaries from reaching their goals up to 95 percent of the time. The outcome offers promise for a role for autonomous AI in proactive cyber defense.

Scientists from the Department of Energy's Pacific Northwest National Laboratory documented their findings in a research paper and presented their work Feb. 14 at a workshop on AI for Cybersecurity during the annual meeting of the Association for the Advancement of Artificial Intelligence in Washington, D.C.

The starting point was the development of a simulation environment to test multistage attack scenarios involving distinct types of adversaries. Creation of such a dynamic attack-defense simulation environment for experimentation itself is a win. The environment offers researchers a way to compare the effectiveness of different AI-based defensive methods under controlled test settings.

Such tools are essential for evaluating the performance of deep reinforcement learning algorithms. The method is emerging as a powerful decision-support tool for cybersecurity experts—a defense agent with the ability to learn, adapt to quickly changing circumstances, and make decisions autonomously. While other forms of AI are standard to detect intrusions or filter spam messages, deep reinforcement learning expands defenders' abilities to orchestrate sequential decision-making plans in their daily face-off with adversaries.

Deep reinforcement learning offers smarter cybersecurity, the ability to detect changes in the cyber landscape earlier, and the opportunity to take preemptive steps to scuttle a cyberattack.  READ MORE...

Three Stage Authentication

In recent years, many computer scientists have been exploring the notion of metaverse, an online space in which users can access different virtual environments and immersive experiences, using VR and AR headsets. While navigating the metaverse, users might also share personal data, whether to purchase goods, connect with other users, or for other purposes.

Past studies have consistently highlighted the limitations of password authentication systems, as there are now many cyber-attacks and strategies for cracking them. To increase the security of users navigating the metaverse, therefore, password-based authentication would be far from ideal.

This inspired a team of researchers at VIT-AP University in India to create MetaSecure, a password-less authentication system for the metaverse. This system, introduced in a paper pre-published on arXiv, combines three different authentication techniques, namely device attestation, facial recognition and physical security keys.

"The concept of metaverse promotes the sustainable growth of human civilizations, enhancing communication on a virtual platform," Sibi Chakkaravarthy, Aditya Mitra and Anisha Ghosh, three of the researchers who carried out the study, told Tech Xplore. "In such a scenario, security of one's digital identity is a main concern. Thus, we came up with MetaSecure, a novel authentication system."

MetaSecure was designed to significantly increase the security of the metaverse, protecting users as they engage in a range of virtual activities. The authentication system can secure a wide range of personal data and possessions, including digital assets, online identities, avatars, and financial information.   TO READ MORE, CLICK HERE...

Limits of Artificial Intelligence

Humans are usually pretty good at recognizing when they get things wrong, but artificial intelligence systems are not. According to a new study, AI generally suffers from inherent limitations due to a century-old mathematical paradox.

Like some people, AI systems often have a degree of confidence that far exceeds their actual abilities. And like an overconfident person, many AI systems don't know when they're making mistakes. Sometimes it's even more difficult for an AI system to realize when it's making a mistake than to produce a correct result.

Researchers from the University of Cambridge and the University of Oslo say that instability is the Achilles' heel of modern AI and that a mathematical paradox shows AI's limitations. Neural networks, the state of the art tool in AI, roughly mimic the links between neurons in the brain. The researchers show that there are problems where stable and accurate neural networks exist, yet no algorithm can produce such a network. Only in specific cases can algorithms compute stable and accurate neural networks.

The researchers propose a classification theory describing when neural networks can be trained to provide a trustworthy AI system under certain specific conditions. Their results are reported in the Proceedings of the National Academy of Sciences.

Deep learning, the leading AI technology for pattern recognition, has been the subject of numerous breathless headlines. Examples include diagnosing disease more accurately than physicians or preventing road accidents through autonomous driving. However, many deep learning systems are untrustworthy and easy to fool.

"Many AI systems are unstable, and it's becoming a major liability, especially as they are increasingly used in high-risk areas such as disease diagnosis or autonomous vehicles," said co-author Professor Anders Hansen from Cambridge's Department of Applied Mathematics and Theoretical Physics. "If AI systems are used in areas where they can do real harm if they go wrong, trust in those systems has got to be the top priority."  READ MORE...