Lifeboat Foundation

A detailed study of a reaction between a molecular ion and a neutral atom has implications for both atmospheric and interstellar chemistry.

Reactions between ions and neutral atoms or molecules occur in various settings, from planetary atmospheres to plasmas. They are also the driving force behind rich reaction chains at play in the interstellar medium (ISM)—the giant clouds of gas and dust occupying the space between stars. The ISM is cold, highly dilute, and abundant with ionizing radiation [1]. These conditions are usually unfavorable for chemistry. Yet, more than 300 molecular species have been detected in the ISM to date, of which about 80% contain carbon [2]. Now Florian Grussie at the Max Planck Institute for Nuclear Physics (MPIK) in Germany and collaborators report an experimental and theoretical study of an ion–neutral reaction: that between a neutral carbon atom and a molecular ion (HD⁺), made of a hydrogen and a deuterium (heavy hydrogen) atom [3, 4]. The study’s findings could improve our understanding of the chemistry of the ISM.

Ion–neutral reactions are fundamentally different from those involving only neutral species. Unlike typical neutral–neutral reactions, ion–neutral reactions often do not need to overcome an activation energy barrier and proceed efficiently even if the temperature approaches absolute zero. The reason for this difference is that, in ion–neutral reactions, the ion strongly polarizes the neutral atom or molecule, causing attractive long-range interactions that bring the reactants together.

A recent study has explored the influence on low-energy fusion processes of isospin composition. This is a key nuclear property that differentiates protons from neutrons. The researchers used computational techniques and theoretical modeling to investigate the fusion of different nuclei with varying isospin configurations. The results show that the isospin composition of the nuclei in a fusion reaction plays a crucial role in understanding the reaction. The paper is published in the journal Physical Review C.

In this study, researchers at Fisk University and Vanderbilt University used high-performance computational and theoretical modeling techniques to conduct a detailed many-body method study of how the dynamics of isospin influence nuclear fusion at low energies across a series of isotopes. The study also examined how the shape of the nuclei involved affect these dynamics. In systems where the nuclei are not symmetrical, the dynamics of isospin become particularly important, often leading to a lowered fusion barrier, especially in systems rich in neutrons. This phenomenon can be explored using facilities that specialize in the generation of beams composed of exotic, unstable nuclei.

The findings provide critical knowledge regarding the fundamental nuclear processes governing these reactions, which have broad implications for fields such as nuclear physics, astrophysics, and, perhaps someday, fusion-based energy.

Fusion energy is released when two light nuclei combine to form a single heavier one (nuclear fusion reaction). Fusion energy-based power generation (fusion power plant) uses the energy generated when deuterium and tritium combine to form helium.

Small modular nuclear reactors are too expensive, too slow, and too risky, and the focus should be on wind, solar, and battery storage for energy needs Questions to inspire discussion What did Tony Seba predict about nuclear power in 2014? —Tony Seba predicted in 2014 that nuclear power would be obsolete by 2030, and recent research has shown that his predictions about the cost blowouts and inefficiency of small modular nuclear reactors were accurate.

New studies show photon polarization is constant in varying environments, potentially improving plasma heating methods for fusion energy advancement.

Light, both literally and figuratively, pervades our world. It eliminates darkness, conveys telecommunications signals across continents, and reveals the unseen, from distant galaxies to microscopic bacteria. Light can also help heat the plasma within ring-shaped devices known as tokamaks as scientists work to leverage the fusion process to produce green electricity.

Recently, researchers from Princeton Plasma Physics Laboratory have discovered that one of the fundamental properties of photons—polarization—is topological, meaning it remains constant even as the photon transitions through various materials and environments. These findings, published in Physical Review D, could lead to more effective plasma heating techniques and advancements in fusion research.

WEST, a fusion reactor in France, broke a record for tungsten tokamaks with a 6-minute plasma. It’s an important step toward clean, limitless energy.

For the first time, scientists successfully track energetic ion flow through space and energy driven by electromagnetic waves in fusion plasmas.

Swiss company Transmutex will use particle-accelerator technology to make uranium that produces less nuclear waste when used in fission reactions.

Researchers at the Princeton Plasma Physics Laboratory are harnessing artificial intelligence and machine learning to enhance fusion energy production, tackling the challenge of controlling plasma reactions. Their innovations include optimizing the design and operation of containment vessels and using AI to predict and manage instabilities, significantly improving the safety and efficiency of fusion reactions. This technology has been successfully applied in tokamak reactors, advancing the field towards viable commercial fusion energy. Credit: SciTechDaily.com.

The intricate dance of atoms fusing and releasing energy has fascinated scientists for decades. Now, human ingenuity and artificial intelligence are coming together at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) to solve one of humankind’s most pressing issues: generating clean, reliable energy from fusing plasma.

Unlike traditional computer code, machine learning — a type of artificially intelligent software — isn’t simply a list of instructions. Machine learning is software that can analyze data, infer relationships between features, learn from this new knowledge, and adapt. PPPL researchers believe this ability to learn and adapt could improve their control over fusion reactions in various ways. This includes perfecting the design of vessels surrounding the super-hot plasma, optimizing heating methods, and maintaining stable control of the reaction for increasingly long periods.