An interstellar mission to a black hole? Astrophysicist thinks it’s possible.
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Updates every hour. Last Updated: 29-Aug-2025 04:11 ET (29-Aug-2025 08:11 GMT/UTC)
It sounds like science fiction: a spacecraft, no heavier than a paperclip, propelled by a laser beam and hurtling through space at the speed of light toward a black hole, on a mission to probe the very fabric of space and time and test the laws of physics. But to astrophysicist and black hole expert Cosimo Bambi, the idea is not so far-fetched.
Reporting in the Cell Press journal iScience, Bambi outlines the blueprint for turning this interstellar voyage to a black hole into a reality. If successful, this century-long mission could return data from nearby black holes that completely alter our understanding of general relativity and the rules of physics.
Not all poisonous gases have a smell or a color. But a tiny grid of pastel- and candy-colored squares that effectively “sniffs” out hazardous chemicals in the air such as chlorosarin — a highly toxic nerve agent — could help detect them. Researchers report in ACS Sensors that the colorful patterns in their inexpensive and durable paper-based sensor array changed in the presence of poisonous gases, allowing for quick and accurate measurements within minutes.
Quantum Key Distribution (QKD) enables information-theoretic secure communication based on quantum physics. A new study by Danish, Austrian, and Canadian researchers has demonstrated composable secure key generation against collective attacks over 20 km fiber using discrete-modulated Continuous-Variable QKD and modern numerical security proof methods. This marks the first practical implementation of a long-theorized protocol, combining high key rates, standard telecom compatibility, and rigorous security guarantees - an important step toward real-world quantum-secure communication in metropolitan networks.
An astonishing world record has been set at ETH Zurich with support from TU Wien: glass particles reveal their quantum properties – without having to be cooled to extremely low temperatures, as was previously the case.
A machine learning method developed by researchers from Institute of Science Tokyo, the Institute of Statistical Mathematics, and other institutions accurately predicts liquid crystallinity of polymers with 96% accuracy. They screened over 115,000 polyimides and selected six candidates with a high probability of exhibiting liquid crystallinity. Upon successful synthesis and experimental analyses, these liquid crystalline polyimides demonstrated thermal conductivities up to 1.26 W m⁻1 K⁻1, accelerating the discovery of efficient thermal materials for next-generation electronics.