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Category: chemistry

Relationships between electronegativity and genotoxicity

The mean electronegativity of chemicals tested for mutagenicity, genotoxicity, clastogenicity and toxicity was determined. It was found that, as expected, chemicals with ‘structural alerts’ for DNA reactivity, and/or capable of inducing mutations in Salmonella and/or unscheduled DNA synthesis in hepatocytes, as a group, were significantly more electronegative than the molecules lacking these attributes. Molecules capable of inducing somatic mutations and recombinations in Drosophila melanogaster also exhibited this characteristic although it was of borderline statistical significance. Inducers of chromosomal aberrations and sister-chromatid exchanges in cultured CHO cells showed the same trend, however the differences between inducers and non-inducers were not statistically significant. In contrast to the above, inducers of bone marrow micronuclei, as a group, were significantly less electronegative than non-inducers. This is a property they shared with chemicals that exhibited systemic or cellular toxicity or that induced lethality in minnows. These findings suggest that in addition to genotoxicity, cellular and/or systemic toxicity may also contribute to the induction of micronuclei.

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Scientists use lightning to make ammonia out of thin air

University of Sydney researchers have harnessed human-made lightning to develop a more efficient method of generating ammonia—one of the world’s most important chemicals. Ammonia is also the main ingredient of fertilizers that account for almost half of all global food production.

The research was published in Angewandte Chemie International edition.

The team have successfully developed a more straightforward method to produce (NH3) in gas form. Previous efforts by other laboratories produced ammonia in a solution (ammonium, NH4+), which requires more energy and processes to transform it into the final gas product.

Magnetism recharged: A new method for restoring magnetism in thin films

Modern low-power solutions to computer memory rely heavily on the manipulation of the magnetic properties of materials. Understanding the influence of the chemical properties of these materials on their magnetization ability is of key importance in developing the field.

A study published in Applied Physics Letters, led by researchers from SANKEN at The University of Osaka, has revealed a technique for recovering magnetism in a degraded spintronics device. This method can be applied to improve the robustness of next-generation semiconductor memory.

Spintronics exploits the spin (and charge) of electrons to process and store memory, and this is achieved practically by stacking layers of thin material films that behave differently under the influence of a magnetic field.

AI and biophysics unite to forecast high-risk viral variants before outbreaks

When the first reports of a new COVID-19 variant emerge, scientists worldwide scramble to answer a critical question: Will this new strain be more contagious or more severe than its predecessors? By the time answers arrive, it’s frequently too late to inform immediate public policy decisions or adjust vaccine strategies, costing public health officials valuable time, effort, and resources.

In a pair of recent publications in Proceedings of the National Academy of Sciences, a research team in the Department of Chemistry and Chemical Biology combined biophysics with artificial intelligence to identify high-risk viral variants in record time—offering a transformative approach for handling pandemics. Their goal: to get ahead of a virus by forecasting its evolutionary leaps before it threatens public health.

“As a society, we are often very unprepared for the emergence of new viruses and pandemics, so our lab has been working on ways to be more proactive,” said senior author Eugene Shakhnovich, Roy G. Gordon Professor of Chemistry. “We used fundamental principles of physics and chemistry to develop a multiscale model to predict the course of evolution of a particular variant and to predict which variants will become dominant in populations.”

A new quantum dot photoreductant uses 99% less light energy for organic reactions

Chemists at the School of Science of the Hong Kong University of Science and Technology (HKUST) have recently made significant progress in photocatalysis by unveiling a “super” photoreductant, marking a major advancement in organic synthesis.

Quantum dots (QDs) hold great promise as photocatalysts for promoting photoredox chemistry. However, their application in photocatalytic organic transformations has lagged behind that of small molecule photosensitizers due to the limited understanding of their photophysics.

While various studies have explored the generation of hot electrons from QDs as a strategy to enhance photoreduction efficiencies, achieving effective hot-electron generation under has posed a significant challenge.

AI helps discover optimal new material for removing radioactive iodine contamination

Managing radioactive waste is one of the core challenges in the use of nuclear energy. In particular, radioactive iodine poses serious environmental and health risks due to its long half-life (15.7 million years in the case of I-129), high mobility, and toxicity to living organisms.

A Korean research team has successfully used artificial intelligence to discover a new material that can remove iodine for nuclear environmental remediation. The team plans to push forward with commercialization through various industry–academia collaborations, from iodine-adsorbing powders to contaminated water treatment filters.

Professor Ho Jin Ryu’s research team from the Department of Nuclear and Quantum Engineering, in collaboration with Dr. Juhwan Noh of the Digital Chemistry Research Center at the Korea Research Institute of Chemical Technology, developed a technique using AI to discover new materials that effectively remove contaminants. Their research is published in the Journal of Hazardous Materials.

AI predicts material properties using electron-level information without costly quantum mechanical computations

Researchers in Korea have developed an artificial intelligence (AI) technology that predicts molecular properties by learning electron-level information without requiring costly quantum mechanical calculations. The research was presented at ICLR 2025.

A joint research team led by Senior Researcher Gyoung S. Na from the Korea Research Institute of Chemical Technology (KRICT) and Professor Chanyoung Park from the Korea Advanced Institute of Science and Technology (KAIST) has developed a novel AI method—called DELID (Decomposition-supervised Electron-Level Information Diffusion)—that accurately predicts using electron-level information without performing quantum mechanical computations.

The method achieved state-of-the-art prediction accuracy on real-world datasets consisting of approximately 30,000 experimental molecular data.

Surprising discovery shows a strong link between Earth’s magnetic field and atmospheric oxygen levels

Every breath we take in contains 21% oxygen, the gas that makes life on Earth possible. Oxygen, in its combined oxide state, has always been abundant in Earth’s crust, but elemental diatomic oxygen became part of our atmosphere around 2.4 to 2.5 billion years ago as a gift from cyanobacteria, which triggered the Great Oxidation Event and breathed life into Earth.

A joint venture between NASA Goddard Space Flight Center and the University of Leeds discovered that the Earth’s magnetic field strength and atmospheric oxygen levels over the past 540 years have seemed to spike and dip at the same time, showing a strong, statistically significant correlation between the two.

This correlation could arise from unexpected connections between geophysical processes in Earth’s deep interior, redox reactions on Earth’s surface, and biogeochemical cycling.

Ultrafast 12-minute MRI maps brain chemistry to spot disease before symptoms

Illinois engineers fused ultrafast imaging with smart algorithms to peek at living brain chemistry, turning routine MRIs into metabolic microscopes. The system distinguishes healthy regions, grades tumors, and forecasts MS flare-ups long before structural MRI can. Precision-medicine neurology just moved closer to reality.