Lifeboat Foundation

Newcastle University research turns to ancient hot springs to explore the origins of life on Earth.

The research team investigated how the emergence of the first living systems from inert geological materials happened on Earth more than 3.5 billion years ago. Scientists at Newcastle University found that mixing hydrogen, bicarbonate, and iron-rich magnetite under conditions mimicking relatively mild hydrothermal vent results in forming a spectrum of organic molecules, most notably including fatty acids stretching up to 18 carbon atoms in length.

Published in the journal Communications Earth & Environment, their findings potentially reveal how some key molecules needed to produce life are made from inorganic chemicals, which is essential to understanding a key step in how life formed on the Earth billions of years ago.

The two-step process also produces hydrogen gas as a by-product, which could also be used as a zero-emission fuel.

“We are looking at active sites and how these sites are bonding with the reaction intermediates,” said Ping Liu of Brookhaven’s Chemistry Division. “By determining the barriers, or transition states, from one step to another, we learn exactly how the catalyst is functioning during the reaction.”

The researchers found that the iron-cobalt alloy works sequentially in the second stage and gets pushed to the side as the nanofiber grows. Using this information, the team could leach the catalysts using acid and reuse them again. If the entire process could be fueled by renewable energy, the process would be a carbon-negative approach to CO₂ mitigation.

Continue reading “Researchers trap CO2 from air into nanofibers to prevent its release” »

Part 3: This is the last of a three-part series on how Stanford Medicine researchers are designing vaccines that protect people from not merely individual viral strains but broad ranges of them. The ultimate goal: a vaccine with coverage so broad it can protect against viruses never before encountered.

Until now, vaccine efforts have mainly focused on stimulating B cells, described and discussed in Part 1 and Part 2. These antibody-producing immune cells’ virtue of being highly specific in what they target is also a vice. An antibody against influenza is unlikely to ever bind to, say, a coronavirus or a rabies virus.

Even when a virus mutates in some small way that distorts or disguises one of its biochemical bull’s-eyes, antibodies that worked before (because they aimed at that particular bull’s-eye) are now unemployed.

The transcription factor FOXP3’s interactions with DNA present more evidence of the importance of disorder.

Since its earliest days, supramolecular chemistry has taken inspiration from biology. To create a ‘chemistry beyond the molecule’, supramolecular chemists can learn from the way nature builds hierarchies of organisation from the selective and orderly interactions of molecular components. At least, that’s what Jean-Marie Lehn and I argued in an overview of the subject in 2000.¹ Yet while I still believe that today, I’m less sure that nature’s molecular principles can be easily translated into what Lehn has called a rational ‘science of informed matter’² – and even less so that the principles used in supramolecular chemistry to create wonderful edifices of molecular order and design will by themselves give us anything like proto-living systems.

The reason is that life’s molecular principles are far less transparent than we thought even a few decades ago, and certainly less amenable to rational bottom-up design. An example is supplied by a new study of how a transcription-factor protein called FOXP3 interacts with DNA to influence the differentiation of regulatory T (T_reg) cells, key components of the immune system, from their precursor cells. Transcription factors regulate gene expression, and one way FOXP3 seems to do this is by binding directly to DNA as dimers in which two of the proteins sit in ‘head-to-head’ contact.

Li-fi, a communication technology harnessing visible light for data transmission, has a potential to surpass Wi-Fi’s speed by more than 100 times and boasts a high bandwidth, facilitating the simultaneous transmission of copious information. Notably, Li-fi ensures robust security by exclusively transmitting data to areas illuminated by light.

Most important, it capitalizes on existing indoor lighting infrastructure, such as LEDs, eliminating the need for separate installations. However, implementing visible light communication (VLC) in practical lighting systems poses an issue of diminished stability and accuracy in data transmission.

Recently, a collaborative team led by Professor Dae Sung Chung, from the Department of Chemical Engineering at Pohang University of Science and Technology (POSTECH), with researcher Dowan Kim, Professor Dong-Woo Jee and Hyung-Jun Park from the Department of Intelligence Semiconductor Engineering at Ajou University, and Professor Jeong-Hwan Lee from the Department of Materials Science and Engineering at Inha University, succeeded in utilizing indoor lighting for wireless communication by reducing light interference with a novel light source. Their findings were published in Advanced Materials.

A new study shows how quantum computing can be harnessed to discover new properties of polymer systems central to biology and material science.

The advent of quantum computing is opening previously unimaginable perspectives for solving problems deemed beyond the reach of conventional computers, from cryptography and pharmacology to the physical and chemical properties of molecules and materials. However, the computational capabilities of present-day quantum computers are still relatively limited. A newly published study in Science Advances fosters an unexpected alliance between the methods used in quantum and traditional computing.

The research team, formed by Cristian Micheletti and Francesco Slongo of SISSA in Trieste, Philipp Hauke of the University of Trento, and Pietro Faccioli of the University of Milano-Bicocca, used a mathematical approach called QUBO (from “Quadratic Unconstraint Binary Optimization”) that is ideally suited for specific quantum computers, called “quantum annealers.”

Soft robots, medical devices, and wearable devices have permeated our daily lives. KAIST (Korea Advanced Institute of Science and Technology) researchers have developed a fluid switch using ionic polymer artificial muscles that operates at ultra-low power and produces a force 34 times greater than its weight. Fluid switches control fluid flow, causing the fluid to flow in a specific direction to invoke various movements.

KAIST announced on the 4th of January that a research team under Professor IlKwon Oh from the Department of Mechanical Engineering has developed a soft fluidic switch that operates at ultra-low voltage and can be used in narrow spaces.

The results have been published in Science Advances (“Polysulfonated Covalent Organic Framework as Active Electrode Host for Mobile Cation Guests in Electrochemical Soft Actuator”).

Electricity could be used to enhance a specific chemical reaction in the synthesis of prospective medicinal drugs.

As the world moves away from gas towards electricity as a greener power source, the to-do list goes beyond cars. The vast global manufacturing network that makes everything from our batteries to our fertilizers needs to flip the switch, too.

A study from UChicago chemists found a way to use electricity to boost a type of chemical reaction often used in synthesizing new candidates for pharmaceutical drugs.

Continue reading “Using electricity, scientists find promising new method of boosting chemical reactions” »

In fields such as physics and engineering, partial differential equations (PDEs) are used to model complex physical processes to generate insight into how some of the most complicated physical and natural systems in the world function.

To solve these difficult equations, researchers use high-fidelity numerical solvers, which can be very time consuming and computationally expensive to run. The current simplified alternative, data-driven surrogate models, compute the goal property of a solution to PDEs rather than the whole solution. Those are trained on a set of data that has been generated by the high-fidelity solver, to predict the output of the PDEs for new inputs. This is data-intensive and expensive because complex physical systems require a large number of simulations to generate enough data.

In a new paper, “Physics-enhanced deep surrogates for partial differential equations,” published in December in Nature Machine Intelligence, a new method is proposed for developing data-driven surrogate models for complex physical systems in such fields as mechanics, optics, thermal transport, fluid dynamics, physical chemistry, and climate models.