Lifeboat Foundation

Drug-target interaction is a prominent research area in drug discovery, which refers to the recognition of interactions between chemical compounds and the protein targets. Chemists estimate that 1,060 compounds with drug-like properties could be made—that’s more than the total number of atoms in the Solar System, as an article reported in the journal Nature in 2017.

Drug development, on average, takes about 14 years and costs up to 1.5 billion dollars. During the journey of drug discovery in this vast “galaxy,” it is apparent that traditional biological experiments for DTI detection are normally costly and time-consuming.

Prof. Hou Tingjun is an expert in computer-aided drug design (CADD) at the Zhejiang University College of Pharmaceutical Sciences. In the past decades, he has been committed to developing drugs using computer technology. “The biggest challenge lies in the interactions between unknown targets and drug molecules. How can we discover them more efficiently? This involves a new breakthrough in method.”

A new study finds the magnetic field generated by a tsunami can be detected a few minutes earlier than changes in sea level and could improve warnings of these giant waves.

Tsunamis generate magnetic fields as they move conductive seawater through the Earth’s magnetic field. Researchers previously predicted that the tsunami’s magnetic field would arrive before a change in sea level, but they lacked simultaneous measurements of magnetics and sea level that are necessary to demonstrate the phenomenon.

The new study provides real-world evidence for using tsunamis’ magnetic fields to predict the height of tsunami waves using data from two real events—a 2009 tsunami in Samoa and a 2010 tsunami in Chile—that have both sets of necessary data. The new study was published in AGU’s Journal of Geophysical Research: Solid Earth, which focuses on the physics and chemistry of the solid Earth.

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.