Can scenarios inspired by science fiction help anticipate the effects of future technologies?
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Exposing Nuclear Magic
Calculations show how the mysterious “magic numbers” that stabilize nuclear structures emerge naturally from nuclear forces—once these are described with appropriate spatial resolution.
Atomic nuclei have been studied for over a century, yet some of nuclear physics’ most basic questions remain unanswered: How many bound combinations of protons and neutrons, or isotopes, can exist? Where do the limits of nuclear existence lie? How are chemical elements synthetized in the Universe? Clues to solving these puzzles lie in the vast phenomenology of nuclear structure—the measured properties of tens of thousands of nuclear states, their decays, and their reactions. In this bedlam of information, patterns and irregularities in data provide crucial hints. One such irregularity was spotted as early as 1934 [1]: Nuclei containing specific numbers of protons and neutrons (2, 8, 20, 28, 50, 82…) are unexpectedly stable. These “magic numbers” (Fig.
91-qubit Processor Accurately Simulates Many-Body Quantum Chaos
Quantum chaos describes chaotic classical dynamical systems in terms of quantum theory, but simulations of these systems are limited by computational resources. However, one team seems to have found a way by leveraging error mitigation and specialized circuits on a 91-qubit superconducting quantum processor. Their results are published in Nature Physics.
While useful quantum simulations require an ability to eliminate errors, full quantum error correction requires large overheads in qubits and control. Previous work has gotten around this problem by simulating limited quantum many-body systems mostly at smaller scales or with integrable—or less chaotic—models.
The research team involved in the new study opted for a different method. Instead, they used error mitigation, which accepts noise and then corrects errors later, saving computational resources in the process.
Newly identified RNA molecule may drive cancer patient survival
In a recent study, researchers at the Texas A&M University Health Science Center (Texas A&M Health) identify a novel RNA molecule that plays a crucial role in preserving the integrity of a key cellular structure, the nucleolus. Their findings also suggest this molecule may influence patient survival in certain blood cancers. The work is published in the Proceedings of the National Academy of Sciences.
Real-time view inside microreactor reveals 2D semiconductor growth secrets
As the miniaturization of silicon-based semiconductor devices approaches fundamental physical limits, the electronics industry faces an urgent need for alternative materials that can deliver higher integration and lower power consumption. Two-dimensional (2D) semiconductors, which are only a single atom thick, have emerged as promising candidates due to their unique electronic and optical properties. However, despite intense research interest, controlling the growth of high-quality 2D semiconductor crystals has remained a major scientific and technological challenge.
A research team led by Research Associate Professor Hiroo Suzuki from the Department of Electrical and Communication Engineering at Okayama University, Japan, together with Dr. Kaoru Hisama from Shinshu University and Dr. Shun Fujii from Keio University, has now overcome a key barrier by directly observing how these materials grow at the atomic scale. Using an advanced in situ observation system, the researchers captured real-time images of monolayer transition metal dichalcogenides (TMDCs) forming inside a micro-confined reaction space. The study was published on December 12, 2025, in the journal Advanced Science.
The work builds on earlier success by the team in synthesizing large-area monolayer TMDC single crystals using a substrate-stacked microreactor. While that method consistently produced high-quality materials, the mechanisms governing crystal growth inside the confined space were poorly understood.
Machine learning accelerates plasma mirror design for high-power lasers
Plasma mirrors capable of withstanding the intensity of powerful lasers are being designed through an emerging machine learning framework. Researchers in Physics and Computer Science at the University of Strathclyde have pooled their knowledge of lasers and artificial intelligence to produce a technology that can dramatically reduce the time it takes to design advanced optical components for lasers—and could pave the way for new discoveries in science.
High-power lasers can be used to develop tools for health care, manufacturing and nuclear fusion. However, these are becoming large and expensive due to the size of their optical components, which is currently necessary to keep the laser beam intensity low enough not to damage them. As the peak power of lasers increases, the diameters of mirrors and other optical components will need to rise from approximately one meter to more than 10 meters. These would weigh several tons, making them difficult and expensive to manufacture.
Two-step approach creates more sustainable protein nanostructures for advanced sensing and therapeutics
Gas vesicles are among the largest known protein nanostructures produced and assembled inside microbial cells. These hollow, air-filled cylindrical nanostructures found in certain aquatic microbes have drawn increasing interest from scientists due to their potential for practical applications, including as part of novel diagnostic and therapeutic tools. However, producing gas vesicles is a difficult task for cells in the lab, hindering the development of applications.
In a study recently published in Nature Communications, a team of researchers led by Rice University bioengineer George Lu reports the development of a new genetic regulatory system to improve cell viability during the production of gas vesicles.
“In the past few years, studies have shown that gas vesicles’ ability to reflect sound makes them useful as unique and versatile acoustic reporter systems for biomedical research and clinical applications,” said Lu, an assistant professor in the Department of Bioengineering at Rice’s George R. Brown School of Engineering and Computing.
Long-period Jupiter-like exoplanet discovered with TESS
Using NASA’s Transiting Exoplanet Survey Satellite (TESS), an international team of astronomers has discovered a new extrasolar planet transiting a distant star. The newfound alien world, designated TOI-6692 b, is the size of Jupiter and has an orbital period of about 130 days. The discovery was presented in a paper published January 22 on the arXiv pre-print server.
TESS is conducting a survey of about 200,000 bright stars near the sun with the aim of searching for transiting exoplanets. To date, more than 7,800 potential planets (known as TESS Objects of Interest) have been cataloged using this satellite, with 733 of those discoveries officially verified.
A more realistic picture of platinum electrodes
Current electrochemical theory does not adequately describe realistic platinum electrodes. Scientists at Leiden University have now, for the first time, mapped the influence of imperfect platinum surfaces. This provides a more accurate picture of these electrodes, with applications in hydrogen production and sensors.
Platinum electrodes play a crucial role in electrochemical applications. They are used in sensors, catalysis and fuel cells, for example in the production of green hydrogen. These developments call for a better and more realistic understanding of the underlying fundamental electrochemistry. Current theory falls short.
The surface of a platinum electrode appears smooth. But if you zoom in to the atomic level, you see an irregular landscape with so-called defects. These turn out to influence the electrochemical reactions that take place there. Ph.D. candidates Nicci Lauren Fröhlich and Jinwen Liu investigated this influence under the supervision of Professor Marc Koper and Assistant Professor Katharina Doblhoff-Dier at the Leiden Institute of Chemistry. Their results are published in Nature Chemistry.
New AI system fixes 3D printing defects in real time
Additive manufacturing has revolutionized manufacturing by enabling customized, cost-effective products with minimal waste. However, with the majority of 3D printers operating on open-loop systems, they are notoriously prone to failure. Minor changes, like adjustments to nozzle size or print speed, can lead to print errors that mechanically weaken the part under production.
Traditionally, manufacturers fix these issues on a case-by-case basis, ultimately “babysitting” the printer to manually adjust parameters and test samples in an effort to figure out what went wrong.