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Category: chemistry – Page 96

Are you drinking toxic water? Study discovers new chemical that has been present in water for about a century

Is the chemical toxic?

While the scientists are unsure about the toxicity of the chemical, it is concerning since chloronitramide anion bears resemblance to other chemicals that are toxic in nature. David Wahman, one of the study’s authors and a research environmental engineer at the Environmental Protection Agency, said, “It has similarity to other toxic molecules. We looked for it in 40 samples in 10 US chlorinated drinking water systems located in seven states. We did find it in all the samples.”

Repair Proteins Collaborate in “Hubs” to Repair DNA Damage

DNA can be damaged by normal cellular processes as well as external factors such as UV radiation and chemicals. Such damage can lead to breaks in the DNA strand. If DNA damage is not properly repaired, mutations can occur, which may result in diseases like cancer. Cells use repair systems to fix this damage, with specialized proteins locating and binding to the damaged regions. Now, researchers from the Kind Group at the Hubrecht Institute have mapped the activity of repair proteins in individual human cells. The study demonstrates how these proteins collaborate in so-called “hubs” to repair DNA damage. These findings may lead to new cancer therapies and other treatments where DNA repair is essential.

The researchers published their findings in Nature Communications in an article titled, “Genome-wide profiling of DNA repair proteins in single cells.”

“Accurate repair of DNA damage is critical for maintenance of genomic integrity and cellular viability,” the researchers wrote. “Because damage occurs non-uniformly across the genome, single-cell resolution is required for proper interrogation, but sensitive detection has remained challenging. Here, we present a comprehensive analysis of repair protein localization in single human cells using DamID and ChIC sequencing techniques.”

Accelerating Scientific Discovery with AI

How can scientific discoveries based on large volumes of experimental data be accelerated by artificial intelligence (AI)? This can be achieved in heterogeneous catalysis, according to a recent study led by Prof. Weixue Li from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, published in Science.

The researchers developed a comprehensive theory of metal-support interaction (MSI), a key aspect of catalysis, by combining interpretable AI with domain knowledge, experimental data, and first-principles simulations.

Supported metal catalysts are widely used in industrial chemical production, petrochemical refining, and environmental control systems like exhaust catalysts. MSI influences interfacial activities, such as charge transfer, chemical composition, perimeter sites, particle shape, and suboxide encapsulation, in addition to stabilizing dispersed catalysts. As a result, modifying MSI is one of the few ways to enhance catalyst performance.

Demis Hassabis, Nobel Prize winner in Chemistry: ‘We will need a handful of breakthroughs before we reach artificial general intelligence’

However, Hassabis’ true breakthrough came just a month ago, when he and two colleagues from DeepMind won the Nobel Prize in Chemistry for their development of AlphaFold, an AI tool capable of predicting the structure of the 200 million known proteins. This achievement would have been nearly impossible without AI, and solidifies Hassabis’ belief that AI is set to become one of the main drivers of scientific progress in the coming years.

Hassabis — the son of a Greek-Cypriot father and a Singaporean mother — reflects on the early days of DeepMind, which he founded in 2010, when “nobody was working on AI.” Over time, machine learning techniques such as deep learning and reinforcement learning began to take shape, providing AI with a significant boost. In 2017, Google scientists introduced a new algorithmic architecture that enabled the development of AGI. “It took several years to figure out how to utilize that type of algorithm and then integrate it in hybrid systems like AlphaFold, which includes other components,” he explains.

“During our first years, we were working in a theoretical space. We focused on games and video games, which were never an end in themselves. It gave us a controlled environment in which to operate and ask questions. But my passion has always been to use AI to accelerate scientific understanding. We managed to scale up to solving a real-world problem, such as protein folding,” recalls the engineer and neuroscientist.

The Periodic Table Just Got Wilder: Scientists Unveil the Secrets of the Heaviest Element Ever — Moscovium

A large field-of-view, single-cell-resolution two-and…

Research on moscovium and nihonium shows they are more reactive than flerovium and subject to notable relativistic effects, broadening our understanding of superheavy elements and their potential uses.

An international team led by scientists from GSI/FAIR in Darmstadt, Johannes Gutenberg University Mainz, and the Helmholtz Institute Mainz has successfully determined the chemical properties of the artificially produced superheavy elements moscovium and nihonium (elements 115 and 113).

Moscovium is now the heaviest element ever to be chemically studied. Their research, published in Frontiers in Chemistry, reveals that both elements are more chemically reactive than flerovium (element 114), which was previously studied at GSI/FAIR.

Photon qubits challenge AI, enabling more accurate quantum computing without error-correction techniques

In an era where AI and data are driving the scientific revolution, quantum computing technology is emerging as another game-changer in the development of new drugs and new materials.

Dr. Hyang-Tag Lim’s research team at the Center for Quantum Technology at the Korea Institute of Science and Technology (KIST) has implemented a quantum computing algorithm that can estimate interatomic bond distances and ground state energies with chemical accuracy using fewer resources than conventional methods, and has succeeded in performing accurate calculations without the need for additional quantum error mitigation techniques.

The work is published in the journal Science Advances.

New language encodes shape and structure to help machine learning models predict nanopore properties

A large number of 2D materials like graphene can have nanopores—small holes formed by missing atoms through which foreign substances can pass. The properties of these nanopores dictate many of the materials’ properties, enabling the latter to sense gases, filter out seawater, and even help in DNA sequencing.

“The problem is that these 2D materials have a wide distribution of nanopores, both in terms of shape and size,” says Ananth Govind Rajan, Assistant Professor at the Department of Chemical Engineering, Indian Institute of Science (IISc). “You don’t know what is going to form in the material, so it is very difficult to understand what the property of the resulting membrane will be.”

Machine learning models can be a powerful tool to analyze the structure of nanopores in order to uncover tantalizing new properties. But these models struggle to describe what a looks like.

Observations inspect double-lined spectroscopic binary HD 34736

Using various telescopes, an international team of astronomers has conducted a comprehensive study of a double-lined spectroscopic binary known as HD 34736. The study, published November 6 in the Monthly Notices of the Royal Astronomical Society, delivers important insights into the properties of this system.

So far, the majority of binaries have been detected by Doppler shifts in their , hence these systems are called spectroscopic binaries. Observations show that in some spectroscopic binaries, spectral lines from both stars are visible, and these lines are alternately double and single. These systems are known as double-lined spectroscopic binaries (SB2).

HD 34,736 is an SB2 system consisting of two chemically peculiar late B-type , located some 1,215 light years away. Previous of HD 34,736 have found that the system has an extraordinarily strong magnetic field exceeding 4.5 kG. The effective temperatures of the primary and secondary star were found to be 13,700 and 11,500 K, respectively.

Light-activated, drug-carrying liposomes show potential for minimally invasive glaucoma treatments

More than 4 million people in the U.S. have glaucoma, a group of eye diseases that can damage the optic nerve and lead to vision loss. It’s the second-leading cause of blindness worldwide and there’s currently no cure, but there’s a way to help prevent vision loss through early detection and treatment.

The two main treatment options, however, are inefficient and have downsides. Medicated eyedrops are noninvasive but can’t be absorbed for full effectiveness. Repeated injections into the eye can lead to infections or inflammation, not to mention patient discomfort.

Researchers at Binghamton University are exploring several new glaucoma treatments that would be less invasive. In a study recently published in the Journal of Materials Chemistry B, Assistant Professor Qianbin Wang and Ph.D. student Dorcas Matuwana from the Thomas J. Watson College of Engineering and Applied Science’s Department of Biomedical Engineering shared their findings for drug-carrying liposomes that could be activated in the eye using near-infrared light.

Korean Scientists Achieve Unprecedented Real-Time Capture of Quantum Information

DGIST and UNIST researchers have discovered a new quantum state, the exciton-Floquet synthesis state, enabling real-time quantum information control in two-dimensional semiconductors.

A research team led by Professor Jaedong Lee from the Department of Chemical Physics at DGIST (President Kunwoo Lee) has unveiled a groundbreaking quantum state and an innovative mechanism for extracting and manipulating quantum information through exciton and Floquet states.

Collaborating with Professor Noejung Park from UNIST’s Department of Physics (President Chongrae Park), the team has, for the first time, demonstrated the formation and synthesis process of exciton and Floquet states, which arise from light-matter interactions in two-dimensional semiconductors. This study captures quantum information in real-time as it unfolds through entanglement, offering valuable insights into the exciton formation process in these materials, thereby advancing quantum information technology.

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