Lifeboat Foundation

Reactive molecules, such as free radicals, can be produced in the body after exposure to certain environments or substances and go on to cause cell damage. Antioxidants can minimize this damage by interacting with the radicals before they affect cells.

Led by Enrique Gomez, professor of chemical engineering and materials science and engineering, Penn State researchers have applied this concept to prevent imaging damage to conducting polymers that comprise soft electronic devices, such as organic solar cells, organic transistors, bioelectronic devices and flexible electronics. The researchers published their findings in Nature Communications today (Jan. 8).

According to Gomez, visualizing the structures of conducting polymers is crucial to further develop these materials and enable commercialization of soft electronic devices—but the actual imaging can cause damage that limits what researchers can see and understand.

The Sachdev-Ye-Kitaev (SYK) model, an exactly solvable model devised by Subir Sachdev and Jinwu Ye, has recently proved useful for understanding the characteristics of different types of matter. As it describes quantum matter without quasiparticles and is simultaneously a holographic version of a quantum black hole, it has so far been adopted by both condensed matter and high-energy physicists.

Researchers at University of Pisa and the Italian Institute of Technology (IIT) have recently used the SYK model to examine the charging protocols of quantum batteries. Their paper, published in Physical Review Letters, offers evidence of the potential of quantum mechanical resources for boosting the charging process of batteries.

“Previous theoretical studies laid down the idea that entanglement can be used to greatly speed up the charging process of a quantum battery,” Davide Rossini and Gian Marcello Andolina, two of the researchers who carried out the study, told Phys.org, via email. “However, a concrete solid-state model displaying such fast charging was missing, until now.”

This engine will let us stay there for months, if not years.

Could the key to interstellar exploration be a nuclear-powered flyer that circles Jupiter?

Lightmatter bets that optical computing can solve AI’s efficiency problem.

In a study published in Physical Review Letters, a team led by academician Guo Guangcan from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences (CAS) has made progress in high dimensional quantum teleportation. The researchers demonstrated the teleportation of high-dimensional states in a three-dimensional six-photon system.

To transmit unknown quantum states from one location to another, quantum teleportation is one of the key technologies to realize long-distance transmission.

Compared with two-dimensional systems, high-dimensional system quantum networks have the advantages of higher channel capacity and better security. In recent years more and more researchers of the quantum information field have been working on generating efficient generation of high-dimensional quantum teleportation to achieve efficient high-dimensional quantum networks.

Elon Musk’s Neuralink has a straightforward outlook on artificial intelligence: “If you can’t beat em, join em.” The company means that quite literally — it’s building a device that aims to connect our brains with electronics, which would enable us, in theory, to control computers with our thoughts.

But how? What material would companies like Neuralink use to connect electronics with human tissue?

One potential solution was recently revealed at the American Chemical Society’s Fall 2020 Virtual Meeting & Expo. A team of researchers from the University of Delaware presented a new biocompatible polymer coating that could help devices better fuse with the brain.

Inside cells, droplets of biomolecules called condensates merge, divide and dissolve. Their dance may regulate vital processes.

Mercedes’ new AI screen is extremely wide! Check out how it could up the stakes against Tesla.

Deep learning has come a long way since the days when it could only recognize handwritten characters on checks and envelopes. Today, deep neural networks have become a key component of many computer vision applications, from photo and video editors to medical software and self-driving cars.

Roughly fashioned after the structure of the brain, neural networks have come closer to seeing the world as humans do. But they still have a long way to go, and they make mistakes in situations where humans would never err.

These situations, generally known as adversarial examples, change the behavior of an AI model in befuddling ways. Adversarial machine learning is one of the greatest challenges of current artificial intelligence systems. They can lead to machine learning models failing in unpredictable ways or becoming vulnerable to cyberattacks.