Some 275 million light-years from the Milky Way lies a true cosmic mystery.
There, in the heart of a galaxy named 1ES 1927+654, squats a supermassive black hole whose monkeyshines and hijinks have baffled astronomers for years.
Now, we might finally have an explanation for at least some of its wild misbehavior: an orbiting white dwarf star veering precariously close to the brink of the black hole’s event horizon, the point beyond which no matter can ever return.
What lies beyond the beginning of time? Physicists are exploring groundbreaking ideas that could reveal a hidden universe behind the Big Bang.
This mind-bending theory challenges everything we know about existence and the mysteries of our cosmic origins.
Imagine rewinding the story of our universe —back through billions of years of expansion, past the formation of galaxies, stars, and planets, to the very beginning. What if, instead of a single moment of creation, there was a cosmic reflection—a mirror image of everything we know, moving backward in time?
The Perfect Cosmic Fireball
Astronomers have unveiled the extraordinary details of a nearly perfect spherical explosion—a kilonova—caused by the collision of two neutron stars. This dramatic event unfolded in 2017 in the galaxy NGC 4,993, located 140–150 million light years from Earth in the Hydra constellation. With a combined mass of 2.7 times that of the sun, the neutron stars had orbited each other for billions of years before their explosive merger.
Lead researcher Albert Sneppen of the Cosmic Dawn Center described the event as “a perfect explosion” due to its symmetry and scientific implications. The kilonova’s luminous fireball emitted a light equivalent to a billion suns for several days, dwarfing any earthly nuclear explosion in intensity.
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Marburg Virus Outbreak Sparks Global Concern: Travel Warnings Issued Amid Deadly ‘Bleeding Eye’ Symptoms and Spread Risks.
For more than 5,000 years, humans have adorned themselves with tattoos.
In a new study, researchers used lasers to uncover highly intricate designs of ancient tattoos on mummies from Peru.
The preserved skin of the mummies and the black tattoo ink used show a stark contrast—revealing fine details in tattoos dating to around 1,250 A.D. that aren’t visible to the naked eye, said study co-author Michael Pittman, an archaeologist at the Chinese University of Hong Kong.
Posted in chemistry, economics, energy, engineering, nanotechnology, sustainability | Leave a Comment on Fully recyclable carbon nanotube fibers have far-reaching implications for manufacturing across sectors
In a significant step toward creating a sustainable and circular economy, Rice University researchers have published a study in the journal Carbon demonstrating that carbon nanotube (CNT) fibers can be fully recycled without any loss in their structure or properties. This discovery positions CNT fibers as a sustainable alternative to traditional materials like metals, polymers and the much larger carbon fibers, which are notoriously difficult to recycle.
“Recycling has long been a challenge in the materials industry—metals recycling is often inefficient and energy-intensive, polymers tend to lose their properties after reprocessing and carbon fibers cannot be recycled at all, only downcycled by chopping them up into short pieces,” said corresponding author Matteo Pasquali, director of Rice’s Carbon Hub and the A.J. Hartsook Professor of Chemical and Biomolecular Engineering, Materials Science and NanoEngineering and Chemistry.
“As CNT fibers are being scaled up, we asked whether and how these new materials could be recycled in the future so as to proactively avoid waste management problems that emerged as other engineered materials reached large-scale use. We expected that recycling would be difficult and would lead to significant loss of properties. Surprisingly, we found that carbon nanotube fibers far exceed the recyclability potential of existing engineered materials, offering a solution to a major environmental issue.”
A study by Michael Gerlich at SBS Swiss Business School has found that increased reliance on artificial intelligence (AI) tools is linked to diminished critical thinking abilities. It points to cognitive offloading as a primary driver of the decline.
AI’s influence is growing fast. A quick search of AI-related science stories reveals how fundamental a tool it has become. Thousands of AI-assisted, AI-supported and AI-driven analyses and decision-making tools help scientists improve their research.
AI has also become more integrated into daily activities, from virtual assistants to complex information and decision support. Increased usage is beginning to influence how people think, especially impactful among younger people, who are avid users of the technology in their personal lives.
Collapsed dead stars, known as neutron stars, are a trillion times denser than lead, and their surface features are largely unknown. Nuclear theorists have explored mountain building mechanisms active on the moons and planets in our solar system. Some of these mechanisms suggest that neutron stars are likely to have mountains.
Neutron star “mountains” would be much more massive than any on Earth—so massive that gravity just from these mountains could produce small oscillations, or ripples, in the fabric of space and time.
Mountains, or non-axisymmetric deformations of rotating neutron stars, efficiently radiate gravitational waves. In a study published in the journal Physical Review D, nuclear theorists at Indiana University consider analogies between neutron star mountains and surface features of solar system bodies.
The mechanisms resulting in particle acceleration to relativistic energies in space plasmas are an open question. Here, the authors show a reinforced shock acceleration model which enables electrons to efficiently achieve relativistic energies and reveal a low electron injection threshold.
AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our brains. They are trained with vast quantities of data on high-performance computers, gobbling up massive amounts of energy in the process.
Spiking neurons, which are much less energy-intensive, could be one solution to this problem. In the past, however, the normal techniques used to train them only worked with significant limitations.
A recent study by the University of Bonn has now presented a possible new answer to this dilemma, potentially paving the way for new AI methods that are much more energy-efficient. The findings have been published in Physical Review Letters.
