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Researchers at TMOS have developed a metasurface-enabled solenoid beam that can pull particles towards it, potentially revolutionizing non-invasive medical procedures like biopsies. This technology, which uses a thin layer of nanopatterned silicon, offers a lightweight, portable alternative to the bulky equipment previously required for such beams. Credit: University of Melbourne.

Researchers at TMOS, the ARC Centre of Excellence for Transformative Meta-Optical Systems, have made a significant initial advancement in creating tractor beams enabled by metasurfaces. These beams of light, capable of drawing particles towards them, are inspired by the fictional tractor beams seen in science fiction.

In research published in ACS Photonics, the University of Melbourne team describes their solenoid beam that is generated using a silicon metasurface. Previous solenoid beams have been created by bulky special light modulators (SLMs), however, the size and weight of these systems prevent the beams from being used in handheld devices. The metasurface is a layer of nanopatterned silicon only about 1/2000 of a millimeter thick. The team hopes that one day it could be used to take biopsies in a non-invasive manner, unlike current methods such as forceps that cause trauma to the surrounding tissues.

A newly discovered neutron star, found by an international team using the ASKAP radio telescope, spins every 54 minutes, making it the slowest of its kind.

This discovery could alter scientific theories about neutron stars and white dwarfs, emphasizing the need for more research to understand their emission properties and evolutionary paths.

Astronomers have detected what they believe to be a neutron star spinning at an unprecedentedly slow rate — slower than any of the more than 3,000 radio-emitting neutron stars measured to date.

A study has revealed that galaxies possess a regulatory mechanism similar to a heart and lungs, which controls their growth by limiting gas absorption.

This mechanism, involving a supermassive black hole and its jet emissions, prevents galaxies from expanding too rapidly, ensuring their longevity and preventing premature aging into “zombie” galaxies.

Continue reading “Black Holes: Not Destroyers but Protectors” »

Low-energy nuclear fusion reactions are influenced by the migration of neutrons and protons between fusing nuclei and their isospin compositions. Research conducted using high-performance computational models has shown the importance of isospin dynamics and nuclear shapes, particularly in asymmetric, neutron-rich systems, revealing significant implications for nuclear physics and potential energy applications.

Low-Energy Nuclear Fusion

Low-energy nuclear fusion reactions can potentially provide clean energy. In stars, low-energy fusion reactions during the stages of carbon and oxygen burning are critical to stellar evolution. These reactions also offer valuable insights into the exotic processes occurring in the inner crust of neutron stars as they accumulate matter. However, scientists do not fully understand the underlying dynamics governing these reactions.

Mindfulness training may cause altered states of consciousness, including disembodiment and unity, according to a University of Cambridge study. While often positive, these experiences can sometimes be unsettling. Awareness and open communication about these potential side effects are essential for both teachers and students.

A new study from the University of Cambridge suggests that participants in mindfulness training may undergo altered states of consciousness, experiencing sensations of disembodiment and unity.

The team says that while these experiences can be very positive, that is not always the case. Mindfulness teachers and students need to be aware that they can be a side-effect of training, and students should feel empowered to share their experiences with their teacher or doctor if they have any concerns.

Optical spectrometers are versatile instruments that can produce light and measure its properties over specific portions of the electromagnetic spectrum. These instruments can have various possible applications; for instance, aiding the diagnosis of medical conditions, the analysis of biological systems, and the characterization of materials.

Conventional spectrometer designs often integrate advanced optical components and complex underlying mechanisms. As a result, they are often bulky and expensive, which significantly limits their use outside of specialized facilities, such as hospitals, laboratories and research institutes.

In recent years, some electronics engineers have thus been trying to develop more compact and affordable optical spectrometers that could be easier to deploy on a large-scale. These devices are typically either developed following the same principle underpinning the functioning of conventional larger spectrometers or via the use of arrayed broadband photodetectors, in conjunction with computational algorithms.

Space storms could soon be forecast with greater accuracy than ever before thanks to a big leap forward in our understanding of exactly when a violent solar eruption may hit Earth.

Artificial intelligence (AI) has grown rapidly in the last few years, and with that increase, industries have been able to automate and improve their efficiency in operations.

A feature article published in AIChE Journal identifies the challenges and benefits of using Intelligence Augmentation (IA) in process safety systems.

Contributors to this work are Dr. Faisal Khan, professor and chemical engineering department head at Texas A&M University, Dr. Stratos Pistikopoulos, professor and director of the Energy Institute, Drs. Rajeevan Arunthavanathan, Tanjin Amin, and Zaman Sajid from the Mary Kay O’Connor Safety Center.

The concept of short-range order (SRO)—the arrangement of atoms over small distances—in metallic alloys has been underexplored in materials science and engineering. But the past decade has seen renewed interest in quantifying it, since decoding SRO is a crucial step toward developing tailored high-performing alloys, such as stronger or heat-resistant materials.

Understanding how atoms arrange themselves is no easy task and must be verified using intensive lab experiments or computer simulations based on imperfect models. These hurdles have made it difficult to fully explore SRO in metallic alloys.

But Killian Sheriff and Yifan Cao, graduate students in MIT’s Department of Materials Science and Engineering (DMSE), are using machine learning to quantify, atom by atom, the complex chemical arrangements that make up SRO. Under the supervision of Assistant Professor Rodrigo Freitas, and with the help of Assistant Professor Tess Smidt in the Department of Electrical Engineering and Computer Science, their work was recently published in Proceedings of the National Academy of Sciences.