Lifeboat Foundation

Method combines quantum mechanics with machine learning to accurately predict oxide reactions at high temperatures when no experimental data is available; could be used to design clean carbon-neutral processes for steel production and metal recycling.

Extracting metals from oxides at high temperatures is essential not only for producing metals such as steel but also for recycling. Because current extraction processes are very carbon-intensive, emitting large quantities of greenhouse gases, researchers have been exploring new approaches to developing “greener” processes. This work has been especially challenging to do in the lab because it requires costly reactors. Building and running computer simulations would be an alternative, but currently there is no computational method that can accurately predict oxide reactions at high temperatures when no experimental data is available.

A Columbia Engineering team reports that they have developed a new computation technique that, through combining quantum mechanics and machine learning, can accurately predict the reduction temperature of metal oxides to their base metals. Their approach is computationally as efficient as conventional calculations at zero temperature and, in their tests, more accurate than computationally demanding simulations of temperature effects using quantum chemistry methods. The study, led by Alexander Urban, assistant professor of chemical engineering, was published on December 1, 2021 by Nature Communications.

Syngas is an important feedstock for modern chemical industries and can be directly used as fuel. Carbon monoxide (CO) is its main component. Direct conversion of widespread renewable biomass resources into CO can help to achieve sustainable development.

Conventionally, bio-syngas is mainly produced through thermal-chemical processes such as pyrolysis, steam reforming or aqueous reforming, which require high temperature and consume a lot of energy.

Recently, a research team led by Prof. Wang Feng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences, in collaboration with Prof. Wang Min from Dalian University of Technology, developed a new method to directly convert bio-polyols into CO.

Quantum and biological systems are seldom discussed together as they seemingly demand opposing conditions. Life is complex, “hot and wet” whereas quantum objects are small, cold and well controlled. Here, we overcome this barrier with a tardigrade — a microscopic multicellular organism known to tolerate extreme physiochemical conditions via a latent state of life known as cryptobiosis. We observe coupling between the animal in cryptobiosis and a superconducting quantum bit and prepare a highly entangled state between this combined system and another qubit. The tardigrade itself is shown to be entangled with the remaining subsystems. The animal is then observed to return to its active form after 420 hours at sub 10 mK temperatures and pressure of $6\times 10^{-6}$ mbar, setting a new record for the conditions that a complex form of life can survive.

The ultimate cooking show experience.

A professor has created Taste the TV (TTTV), a system of chemicals and rolling plastic that lets you lick a screen to taste what it’s displaying. The mind reels with the endless possibilities of this kind of tech.

Continue reading “Take a look at this delicious lickable screen to see the future we knew was coming” »

Let’s nuke our way to the stars!

What is required to get us to other planets? A lot of things but mainly energy. Our current rockets simply can’t produce enough energy to get us that far.

American aerospace engineer, author, and advocate for human exploration of Mars Robert Zubrin has one idea for getting us to space and it’s a rather interesting one. It’s called Nuclear Salt Water Rocket (NSWR) and it replaces traditional chemical propellant with salts of plutonium or 20 p… See more.

Scientists and institutions dedicate more resources each year to the discovery of novel materials to fuel the world. As natural resources diminish and the demand for higher value and advanced performance products grows, researchers have increasingly looked to nanomaterials.

Nanoparticles have already found their way into applications ranging from energy storage and conversion to quantum computing and therapeutics. But given the vast compositional and structural tunability nanochemistry enables, serial experimental approaches to identify new materials impose insurmountable limits on discovery.

Now, researchers at Northwestern University and the Toyota Research Institute (TRI) have successfully applied machine learning to guide the synthesis of new nanomaterials, eliminating barriers associated with materials discovery. The highly trained algorithm combed through a defined dataset to accurately predict new structures that could fuel processes in clean energy, chemical and automotive industries.

AI machine learning presents a roadmap to define new materials for any need, with implications in green energy and waste reduction.

Scientists and institutions dedicate more resources each year to the discovery of novel materials to fuel the world. As natural resources diminish and the demand for higher value and advanced performance products grows, researchers have increasingly looked to nanomaterials.

Nanoparticles have already found their way into applications ranging from energy storage and conversion to quantum computing and therapeutics. But given the vast compositional and structural tunability nanochemistry enables, serial experimental approaches to identify new materials impose insurmountable limits on discovery.

But strangely, this green shade disappears before it reaches the one or two tails trailing behind the comet.

Astronomers, scientists, and chemists have been puzzled by this mystery for almost 90 years. In 1930, it was suggested that this phenomenon was due to sunlight destroying diatomic carbon. The carbon is created from the interaction between sunlight and organic matter on the comet’s head. However, due to the instability of dicarbon, this theory has been hard to test.

Scientists at UNSW Sydney have finally found a way to test this chemical reaction in a laboratory – and in doing so, has proven this 90-year-old theory correct. They solved this mystery with the help of a vacuum chamber, a lot of lasers, and one powerful cosmic reaction.

Some engineered living materials can combine the strength of run-of-the-mill building materials with the responsiveness of living systems. Think self-healing concrete, paint that changes color when a specific chemical is detected or material that could reproduce and fill in a crack when one forms. This would revolutionize construction and maintenance, with wide-reaching economic and environmental implications.

Seeing this new category of adaptive materials on consumer shelves may be a ways off. Still, critical early research from the University of Minnesota sheds new light on this exciting advancement, which shows promise beyond building materials, including biomedical applications.

In a new study in Nature Communications, researchers from the College of Biological Sciences demonstrate how to transform silica — a common material used in plaster and other construction materials — into a self-assembling, dynamic and resilient material.