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Category: chemistry

Found in everything from kitchen appliances to sustainable energy infrastructure, stainless steels are used extensively due to their excellent corrosion (rusting) resistance. They’re an important material in many industries, including manufacturing, transportation, oil and gas, nuclear power and chemical processing.

However, stainless steels can undergo a process called sensitization when subjected to a certain range of high temperatures—like during welding—and this substantially deteriorates their resistance. Left unchecked, corrosion can lead to cracking and structural failure.

“This is a major problem for stainless steels,” says Kumar Sridharan, a professor of nuclear engineering and engineering physics and materials science and engineering at the University of Wisconsin–Madison. “When gets corroded, components need to be replaced or remediated. This is an expensive process and causes extended downtime in industry.”

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Cancer creates an immunosuppressive environment that hampers immune responses, allowing tumors to grow and resist therapy. One way the immune system fights back is by inducing ferroptosis, a type of cell death, in tumor cells through CD8 + T cells. This involves lipid peroxidation and enzymes like lysophosphatidylcholine acyltransferase 3 (Lpcat3), which makes cells more prone to ferroptosis. However, the mechanisms by which cancer cells avoid immunotherapy-mediated ferroptosis are unclear. Our study reveals how cancer cells evade ferroptosis and anti-tumor immunity through the upregulation of fatty acid-binding protein 7 (Fabp7).

To explore how cancer cells resist immune cell-mediated ferroptosis, we used a comprehensive range of techniques. We worked with cell lines including PD1-sensitive, PD1-resistant, B16F10, and QPP7 glioblastoma cells, and conducted in vivo studies in syngeneic 129 Sv/Ev, C57BL/6, and conditional knockout mice with Rora deletion specifically in CD8+ T cells, Cd8 cre; Rorafl mice. Methods included mass spectrometry-based lipidomics, targeted lipidomics, Oil Red O staining, Seahorse analysis, quantitative PCR, immunohistochemistry, PPARγ transcription factor assays, ChIP-seq, untargeted lipidomic analysis, ROS assay, ex vivo co-culture of CD8+ T cells with cancer cells, ATAC-seq, RNA-seq, Western blotting, co-immunoprecipitation assay, flow cytometry and Imaging Mass Cytometry.

PD1-resistant tumors upregulate Fabp7, driving protective metabolic changes that shield cells from ferroptosis and evade anti-tumor immunity. Fabp7 decreases the transcription of ferroptosis-inducing genes like Lpcat3 and increases the transcription of ferroptosis-protective genes such as Bmal1 through epigenetic reprogramming. Lipidomic profiling revealed that Fabp7 increases triglycerides and monounsaturated fatty acids (MUFAs), which impede lipid peroxidation and ROS generation. Fabp7 also improves mitochondrial function and fatty acid oxidation (FAO), enhancing cancer cell survival. Furthermore, cancer cells increase Fabp7 expression in CD8+ T cells, disrupting circadian clock gene expression and triggering apoptosis through p53 stabilization. Clinical trial data revealed that higher FABP7 expression correlates with poorer overall survival and progression-free survival in patients undergoing immunotherapy.

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The first genetically engineered synapses have been implanted in a mammal’s brain. Chemical brain signals have been bypassed in the brains of mice and replaced with electrical signals, changing their behaviour in incredible ways. Not only did they become more sociable, they were also less anxious and exhibited fewer OCD-like symptoms. This work has sparked hope that one day we could use this technology to help humans with mental health conditions. But would you want someone making permanent edits to your brain?

For the first time, climate scientists can now link specific fossil fuel companies to climate-related economic damages in particular places. A new method has been developed that can show the exact impact these companies are having on our environment — which the world’s top five emitters linked to trillions of dollars of economic losses. Find out how scientists have managed to piece this together — and whether these companies are about to face massive lawsuits.

As we reflect on the death of Pope Francis, we explore his legacy on scientific issues and his transformative stance on climate change. As the spiritual leader of 1.4 billion Catholics, he became an influential figure in advocating for better care to be taken of our planet. Will his legacy continue with the next Pope?

Chapters:
00:00 Intro.
00:28 First brain engineering in a mammal.
10:57 Landmark in fossil fuel lawsuits.
19:33 Climate legacy of Pope Francis.

Hosted by Rowan Hooper and Penny Sarchet, with guests Alexandra Thompson, James Dinneen, William Schafer, Chris Callahan, Justin Mankin and Miles Pattenden.
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Learn more ➤ https://www.newscientist.com/podcasts.

Subscribe ➤ https://bit.ly/NSYTSUBS

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PRESS RELEASE — Quantum computers promise to speed calculations dramatically in some key areas such as computational chemistry and high-speed networking. But they’re so different from today’s computers that scientists need to figure out the best ways to feed them information to take full advantage. The data must be packed in new ways, customized for quantum treatment.

Researchers at the Department of Energy’s Pacific Northwest National Laboratory have done just that, developing an algorithm specially designed to prepare data for a quantum system. The code, published recently on GitHub after being presented at the IEEE International Symposium on Parallel and Distributed Processing, cuts a key aspect of quantum prep work by 85 percent.

While the team demonstrated the technique previously, the latest research addresses a critical bottleneck related to scaling and shows that the approach is effective even on problems 50 times larger than possible with existing tools.

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Understanding the origin of heavy elements on the periodic table is one of the most challenging open problems in all of physics. In the search for conditions suitable for these elements via “nucleosynthesis,” a Los Alamos National Laboratory-led team is going where no researchers have gone before: the gamma-ray burst jet and surrounding cocoon emerging from collapsed stars.

As proposed in an article in The Astrophysical Journal, photons produced deep in the jet could dissolve the outer layers of a star into neutrons, causing a series of physical processes that result in the formation of heavy elements.

“The creation of heavy elements such as uranium and plutonium necessitates extreme conditions,” said Matthew Mumpower, physicist at Los Alamos. “There are only a few viable yet rare scenarios in the cosmos where these elements can form, and all such locations need a copious amount of neutrons. We propose a new phenomenon where those neutrons don’t pre-exist but are produced dynamically in the star.”

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The willingness of the 4f orbitals of lanthanide metals to participate in chemical reactions is as rare as their presence in Earth’s crust. A recent study, however, witnessed the 4f orbital in a cerium-based compound actively participate in bond formation, triggering a unique chemical reaction.

The researchers observed that a cerium-containing cyclic complex formed a 4f-covalent interaction, leading to a ring-opening isomerization from cyclopropene to allene. The findings are published in Nature Chemistry.

Lanthanides are heavy, rare-earth , occupying positions 57 through 71 in the —from lanthanum to lutetium—and are widely used in modern technologies ranging from electronics to clean energy. In nature, these elements are usually found together in their ore form and separating them using current methods is extremely challenging and energy-intensive. Understanding how these elements bond or interact with other atoms at an electronic level could help us to distinguish between lanthanides and design effective separation strategies.

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In every scientific discovery in the movies, a scientist observes something unexpected, scratches the side of his or her forehead and says “hmmmmm.” In just such a moment in real life, scientists from Canada observed unexpected flashes of curved green light from a red light-emitting polymer above its surface. The flashes were reminiscent of the colored arcs that auroras take above Earth’s poles, providing a clue as to their provenance.

Their resulting investigation of the new phenomenon could find applications towards understanding the failures of polymer materials and more. Their work has been published in Physical Review Letters.

Jun Gao, a professor and chair of Engineering Physics at the Engineering Physics and Astronomy Department at Queen’s University in Ontario, Canada, and graduate student Dongze Wang were investigating the performance of semiconductors called polymer light-emitting electrochemical cells, or PLECs.

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As artificial intelligence (AI) continues to advance, researchers at POSTECH (Pohang University of Science and Technology) have identified a breakthrough that could make AI technologies faster and more efficient.

Professor Seyoung Kim and Dr. Hyunjeong Kwak from the Departments of Materials Science & Engineering and Semiconductor Engineering at POSTECH, in collaboration with Dr. Oki Gunawan from the IBM T.J. Watson Research Center, have become the first to uncover the hidden operating mechanisms of Electrochemical Random-Access Memory (ECRAM), a promising next-generation technology for AI. Their study is published in the journal Nature Communications.

As AI technologies advance, data processing demands have exponentially increased. Current computing systems, however, separate data storage (memory) from data processing (processors), resulting in significant time and due to data transfers between these units. To address this issue, researchers developed the concept of in-memory computing.

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A U of A engineering researcher is using sunlight and semiconductor catalysts to produce hydrogen by splitting apart water molecules into their constituent elements.

“The process to form the semiconductor, called thermal condensation polymerization, uses cheap and Earth-abundant materials, and could eventually lead to a more efficient, economical path to clean energy than existing ,” says project lead Karthik Shankar of the Department of Electrical and Computer Engineering, an expert in the field of photocatalysis.

In a collaboration between the U of A and the Technical University of Munich, results of the research were published in the Journal of the American Chemical Society.

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It’s obvious when a dog has been poorly trained. It doesn’t respond properly to commands. It pushes boundaries and behaves unpredictably. The same is true with a poorly trained artificial intelligence (AI) model. Only with AI, it’s not always easy to identify what went wrong with the training.

Research scientists globally are working with a variety of AI models that have been trained on experimental and theoretical data. The goal: to predict a material’s properties before taking the time and expense to create and test it. They are using AI to design better medicines and industrial chemicals in a fraction of the time it takes for experimental trial and error.

But how can they trust the answers that AI models provide? It’s not just an academic question. Millions of investment dollars can ride on whether AI model predictions are reliable.

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