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Category: chemistry – Page 38

Photosynthesis is one of the most efficient natural processes for converting light energy from the sun into chemical energy vital for life on earth. Proteins called photosystems are critical to this process and are responsible for the conversion of light energy to chemical energy.

Combining one kind of these proteins, called photosystem I (PSI), with platinum nanoparticles, microscopic particles that can perform a chemical reaction that produces hydrogen — a valuable clean energy source — creates a biohybrid catalyst. That is, the light absorbed by PSI drives hydrogen production by the platinum nanoparticle.

In a recent breakthrough, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and Yale University have determined the structure of the PSI biohybrid solar fuel catalyst. Building on more than 13 years of research pioneered at Argonne, the team reports the first high-resolution view of a biohybrid structure, using an electron microscopy method called cryo-EM. With structural information in hand, this advancement opens the door for researchers to develop biohybrid solar fuel systems with improved performance, which would provide a sustainable alternative to traditional energy sources.

Argonne and Yale researchers shed light on the structure of a photosynthetic hybrid for the first time, enabling advancements in clean energy production.

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A team of engineers is reimagining one of the essential processes in modern manufacturing. Their goal? To transform how a chemical called acrylonitrile (ACN) is made—not by building world-scale manufacturing sites, but by using smaller-scale, modular reactors that can work if they let the catalyst, in a sense, “breathe.”

Their article, titled “Propene Ammoxidation over an Industrial Bismuth Molybdate-Based Catalyst Using Forced Dynamic Operation,” is published in Applied Catalysis A: General.

ACN is everywhere, from carbon fibers in sports equipment to acrylics in car parts and textiles. Traditionally, producing it requires a continuous, energy-intensive process. But now, researchers at the University of Virginia and the University of Houston have shown that by pausing to “inhale” fresh oxygen, a chemical can produce ACN more efficiently. This discovery could open the door to smaller, versatile production facilities that adapt to fluctuating needs.

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Finding a reasonable hypothesis can pose a challenge when there are thousands of possibilities. This is why Dr. Joseph Sang-II Kwon is trying to make hypotheses in a generalizable and systematic manner.

Kwon, an associate professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University, published his work on blending traditional physics-based scientific models with to accurately predict hypotheses in the journal Nature Chemical Engineering.

Kwon’s research extends beyond the realm of traditional chemical engineering. By connecting physical laws with machine learning, his work could impact , smart manufacturing, and health care, outlined in his recent paper, “Adding big data into the equation.”

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At just a few atoms of thickness, 2D materials offer revolutionary possibilities for new technologies that are microscopically sized but have the same capabilities as existing machines.

Florida State University researchers have unlocked a new method for producing one class of 2D material and for supercharging its magnetic properties. The work was published in Angewandte Chemie.

Experimenting on a metallic magnet made from the elements iron, germanium and tellurium and known as FGT, the research team made two breakthroughs: a collection method that yielded 1,000 times more material than typical practices, and the ability to alter FGT’s magnetic properties through a chemical treatment.

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The sun, the essential engine that sustains life on Earth, generates its tremendous energy through the process of nuclear fusion. At the same time, it releases a continuous stream of neutrinos—particles that serve as messengers of its internal dynamics. Although modern neutrino detectors unveil the sun’s present behavior, significant questions linger about its stability over periods of millions of years—a timeframe that spans human evolution and significant climate changes.

Finding answers to this is the goal of the LORandite EXperiment (LOREX) that requires a precise knowledge of the solar neutrino cross section on thallium. This information has now been provided by an international collaboration of scientists using the unique facilities at GSI/FAIR’s Experimental Storage Ring ESR in Darmstadt to obtain an essential measurement that will help to understand the long-term stability of the sun. The results of the measurements have been published in the journal Physical Review Letters.

LOREX is the only long-time geochemical solar neutrino experiment still actively pursued. Proposed in the 1980s, it aims to measure solar neutrino flux averaged over a remarkable four million years, corresponding to the geological age of the lorandite ore.

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UNIVERSITY PARK, Pa. — A recently developed electronic tongue is capable of identifying differences in similar liquids, such as milk with varying water content; diverse products, including soda types and coffee blends; signs of spoilage in fruit juices; and instances of food safety concerns. The team, led by researchers at Penn State, also found that results were even more accurate when artificial intelligence (AI) used its own assessment parameters to interpret the data generated by the electronic tongue.

(Many people already posted this. This is the press release from Penn Sate who did the research)

The tongue comprises a graphene-based ion-sensitive field-effect transistor, or a conductive device that can detect chemical ions, linked to an artificial neural network, trained on various datasets. Critically, Das noted, the sensors are non-functionalized, meaning that one sensor can detect different types of chemicals, rather than having a specific sensor dedicated to each potential chemical. The researchers provided the neural network with 20 specific parameters to assess, all of which are related to how a sample liquid interacts with the sensor’s electrical properties. Based on these researcher-specified parameters, the AI could accurately detect samples — including watered-down milks, different types of sodas, blends of coffee and multiple fruit juices at several levels of freshness — and report on their content with greater than 80% accuracy in about a minute.

“After achieving a reasonable accuracy with human-selected parameters, we decided to let the neural network define its own figures of merit by providing it with the raw sensor data. We found that the neural network reached a near ideal inference accuracy of more than 95% when utilizing the machine-derived figures of merit rather than the ones provided by humans,” said co-author Andrew Pannone, a doctoral student in engineering science and mechanics advised by Das. “So, we used a method called Shapley additive explanations, which allows us to ask the neural network what it was thinking after it makes a decision.”

This approach uses game theory, a decision-making process that considers the choices of others to predict the outcome of a single participant, to assign values to the data under consideration. With these explanations, the researchers could reverse engineer an understanding of how the neural network weighed various components of the sample to make a final determination — giving the team a glimpse into the neural network’s decision-making process, which has remained largely opaque in the field of AI, according to the researchers. They found that, instead of simply assessing individual human-assigned parameters, the neural network considered the data it determined were most important together, with the Shapley additive explanations revealing how important the neural network considered each input data.

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Here on planet Earth, as well as in most locations in the Universe, everything we observe and interact with is made up of atoms. Atoms come in roughly 90 different naturally occurring species, where all atoms of the same species share similar physical and chemical properties, but differ tremendously from one species to another. Once thought to be indivisible units of matter, we now know that atoms themselves have an internal structure, with a tiny, positively charged, massive nucleus consisting of protons and neutrons surrounded by negatively charged, much less massive electrons. We’ve measured the physical sizes of these subatomic constituents exquisitely well, and one fact stands out: the size of atoms, at around 10-10 meters apiece, are much, much larger than the constituent parts that compose them.

Protons and neutrons, which compose the atom’s nucleus, are roughly a factor of 100,000 smaller in length, with a typical size of only around 10-15 meters. Electrons are even smaller, and are assumed to be point-like particles in the sense that they exhibit no measurable size at all, with experiments constraining them to be no larger than 10-19 meters across. Somehow, protons, neutrons, and electrons combine together to create atoms, which occupy much greater volumes of space than their components added together. It’s a mysterious fact that atoms, which must be mostly empty space in this regard, are still impenetrable to one another, leading to enormous collections of atoms that make up the solid objects we’re familiar with in our macroscopic world.

So how does this happen: that atoms, which are mostly empty space, create solid objects that cannot be penetrated by other solid objects, which are also made of atoms that are mostly empty space? It’s a remarkable fact of existence, but one that requires quantum physics to explain.

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Researchers have developed a device that can simultaneously measure six markers of brain health. The sensor, which is inserted through the skull into the brain, can pull off this feat thanks to an artificial intelligence (AI) system that pieces apart the six signals in real time.

Being able to continuously monitor biomarkers in patients with traumatic brain injury could improve outcomes by catching swelling or bleeding early enough for doctors to intervene. But most existing devices measure just one marker at a time. They also tend to be made with metal, so they can’t easily be used in combination with magnetic resonance imaging.

Simultaneous access to measurements could improve outcomes for brain injuries.

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The interactions between light and nitroaromatic hydrocarbon molecules have important implications for chemical processes in our atmosphere that can lead to smog and pollution. However, changes in molecular geometry due to interactions with light can be very difficult to measure because they occur at sub-Angstrom length scales and femtosecond time scales.

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Different types of cancer have unique molecular “fingerprints” which are detectable in early stages of the disease and can be picked up with near-perfect accuracy by small, portable scanners in just a few hours, according to a study published today in the journal Molecular Cell.

The discovery by researchers at the Centre for Genomic Regulation (CRG) in Barcelona sets the foundation for creating new, non-invasive diagnostic tests that detect different types of cancer faster and earlier than currently possible.

The study centers around the ribosome, the protein factories of a cell. For decades, ribosomes were thought to have the same blueprint across the human body. However, researchers discovered a hidden layer of complexity—tiny chemical modifications which vary between different tissues, developmental stages, and disease.

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