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Astronomers detect oldest black hole ever observed

Wed, 17/01/2024 - 15:59

The international team, led by the University of Cambridge, used the NASA/ESA/CSA James Webb Space Telescope (JWST) to detect the black hole, which dates from 400 million years after the big bang, more than 13 billion years ago. The results, which lead author Professor Roberto Maiolino says are “a giant leap forward”, are reported in the journal Nature.

That this surprisingly massive black hole – a few million times the mass of our Sun – even exists so early in the universe challenges our assumptions about how black holes form and grow. Astronomers believe that the supermassive black holes found at the centre of galaxies like the Milky Way grew to their current size over billions of years. But the size of this newly-discovered black hole suggests that they might form in other ways: they might be ‘born big’ or they can eat matter at a rate that’s five times higher than had been thought possible.

According to standard models, supermassive black holes form from the remnants of dead stars, which collapse and may form a black hole about a hundred times the mass of the Sun. If it grew in an expected way, this newly-detected black hole would take about a billion years to grow to its observed size. However, the universe was not yet a billion years old when this black hole was detected.

“It’s very early in the universe to see a black hole this massive, so we’ve got to consider other ways they might form,” said Maiolino, from Cambridge’s Cavendish Laboratory and Kavli Institute for Cosmology. “Very early galaxies were extremely gas-rich, so they would have been like a buffet for black holes.”

Like all black holes, this young black hole is devouring material from its host galaxy to fuel its growth. Yet, this ancient black hole is found to gobble matter much more vigorously than its siblings at later epochs.

The young host galaxy, called GN-z11, glows from such an energetic black hole at its centre. Black holes cannot be directly observed, but instead they are detected by the tell-tale glow of a swirling accretion disc, which forms near the edges of a black hole. The gas in the accretion disc becomes extremely hot and starts to glow and radiate energy in the ultraviolet range. This strong glow is how astronomers are able to detect black holes.

GN-z11 is a compact galaxy, about one hundred times smaller than the Milky Way, but the black hole is likely harming its development. When black holes consume too much gas, it pushes the gas away like an ultra-fast wind. This ‘wind’ could stop the process of star formation, slowly killing the galaxy, but it will also kill the black hole itself, as it would also cut off the black hole’s source of ‘food’.

Maiolino says that the gigantic leap forward provided by JWST makes this the most exciting time in his career. “It’s a new era: the giant leap in sensitivity, especially in the infrared, is like upgrading from Galileo’s telescope to a modern telescope overnight,” he said. “Before Webb came online, I thought maybe the universe isn’t so interesting when you go beyond what we could see with the Hubble Space Telescope. But that hasn’t been the case at all: the universe has been quite generous in what it’s showing us, and this is just the beginning.”

Maiolino says that the sensitivity of JWST means that even older black holes may be found in the coming months and years. Maiolino and his team are hoping to use future observations from JWST to try to find smaller ‘seeds’ of black holes, which may help them untangle the different ways that black holes might form: whether they start out large or they grow fast.

The research was supported in part by the European Research Council, the Royal Society, and the Science and Technology Facilities Council (STFC), part of UK Research and Innovation (UKRI).

 

Reference:
Roberto Maiolino et al. ‘A small and vigorous black hole in the early Universe.’ Nature (2024). DOI: 10.1038/s41586-024-07052-5

Researchers have discovered the oldest black hole ever observed, dating from the dawn of the universe, and found that it is ‘eating’ its host galaxy to death.

It’s a new era: the giant leap in sensitivity, especially in the infrared, is like upgrading from Galileo’s telescope to a modern telescope overnightRoberto MaiolinoNASA, ESA, and P. Oesch (Yale University)The GN-z11 galaxy, taken by the Hubble Space Telescope


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Accelerating how new drugs are made with machine learning

Mon, 15/01/2024 - 10:05

Predicting how molecules will react is vital for the discovery and manufacture of new pharmaceuticals, but historically this has been a trial-and-error process, and the reactions often fail. To predict how molecules will react, chemists usually simulate electrons and atoms in simplified models, a process that is computationally expensive and often inaccurate.

Now, researchers from the University of Cambridge have developed a data-driven approach, inspired by genomics, where automated experiments are combined with machine learning to understand chemical reactivity, greatly speeding up the process. They’ve called their approach, which was validated on a dataset of more than 39,000 pharmaceutically relevant reactions, the chemical ‘reactome’.

Their results, reported in the journal Nature Chemistry, are the product of a collaboration between Cambridge and Pfizer.

“The reactome could change the way we think about organic chemistry,” said Dr Emma King-Smith from Cambridge’s Cavendish Laboratory, the paper’s first author. “A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster. But more fundamentally, the understanding we hope to generate will be beneficial to anyone who works with molecules.”

The reactome approach picks out relevant correlations between reactants, reagents, and performance of the reaction from the data, and points out gaps in the data itself. The data is generated from very fast, or high throughput, automated experiments.

“High throughput chemistry has been a game-changer, but we believed there was a way to uncover a deeper understanding of chemical reactions than what can be observed from the initial results of a high throughput experiment,” said King-Smith.

“Our approach uncovers the hidden relationships between reaction components and outcomes,” said Dr Alpha Lee, who led the research. “The dataset we trained the model on is massive – it will help bring the process of chemical discovery from trial-and-error to the age of big data.”

In a related paper, published in Nature Communications, the team developed a machine learning approach that enables chemists to introduce precise transformations to pre-specified regions of a molecule, enabling faster drug design.

The approach allows chemists to tweak complex molecules – like a last-minute design change – without having to make them from scratch. Making a molecule in the lab is typically a multi-step process, like building a house. If chemists want to vary the core of a molecule, the conventional way is to rebuild the molecule, like knocking the house down and rebuilding from scratch. However, core variations are important to medicine design.

A class of reactions, known as late-stage functionalisation reactions, attempts to directly introduce chemical transformations to the core, avoiding the need to start from scratch. However, it is challenging to make late-stage functionalisation selective and controlled – there are typically many regions of the molecules that can react, and it is difficult to predict the outcome.

“Late-stage functionalisations can yield unpredictable results and current methods of modelling, including our own expert intuition, isn't perfect,” said King-Smith. “A more predictive model would give us the opportunity for better screening.”

The researchers developed a machine learning model that predicts where a molecule would react, and how the site of reaction vary as a function of different reaction conditions. This enables chemists to find ways to precisely tweak the core of a molecule.

“We trained the model on a large body of spectroscopic data – effectively teaching the model general chemistry – before fine-tuning it to predict these intricate transformations,” said King-Smith. This approach allowed the team to overcome the limitation of low data: there are relatively few late-stage functionalisation reactions reported in the scientific literature. The team experimentally validated the model on a diverse set of drug-like molecules and was able to accurately predict the sites of reactivity under different conditions.

“The application of machine learning to chemistry is often throttled by the problem that the amount of data is small compared to the vastness of chemical space,” said Lee. “Our approach – designing models that learn from large datasets that are similar but not the same as the problem we are trying to solve – resolves this fundamental low-data challenge and could unlock advances beyond late-stage functionalisation.”  

The research was supported in part by Pfizer and the Royal Society.

References:
Emma King-Smith et al. ‘Predictive Minisci Late Stage Functionalization with Transfer Learning.’ Nature Communications (2023). DOI: 10.1038/s41467-023-42145-1

Emma King-Smith et al. ‘Probing the Chemical "Reactome" with High Throughput Experimentation Data.’ Nature Chemistry (2023). DOI: 10.1038/s41557-023-01393-w

Researchers have developed a platform that combines automated experiments with AI to predict how chemicals will react with one another, which could accelerate the design process for new drugs.

A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster. Emma King-SmithBlackJack3D via Getty ImagesDigital Molecular Structure Concept


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Mysterious missing component in the clouds of Venus revealed

Tue, 09/01/2024 - 10:05

What are the clouds of Venus made of? Scientists know they are mainly made of sulfuric acid droplets, with some water, chlorine, and iron. Their concentrations vary with height in the thick and hostile Venusian atmosphere. But until now they have been unable to identify the missing component that would explain the clouds’ patches and streaks, only visible in the UV range.

In a study published in Science Advances, researchers from the University of Cambridge synthesised iron-bearing sulfate minerals that are stable under the harsh chemical conditions in the Venusian clouds. Spectroscopic analysis revealed that a combination of two minerals, rhomboclase and acid ferric sulfate, can explain the mysterious UV absorption feature on our neighbouring planet.

“The only available data for the composition of the clouds were collected by probes and revealed strange properties of the clouds that so far we have been unable to fully explain,” said Paul Rimmer from the Cavendish Laboratory and co-author of the study. “In particular, when examined under UV light, the Venusian clouds featured a specific UV absorption pattern. What elements, compounds, or minerals are responsible for such observation?”

Formulated on the basis of Venusian atmospheric chemistry, the team synthesised several iron-bearing sulfate minerals in an aqueous geochemistry laboratory in the Department of Earth Sciences. By suspending the synthesised materials in varying concentrations of sulfuric acid and monitor the chemical and mineralogical changes, the team narrowed down the candidate minerals to rhomboclase and acid ferric sulfate, of which the spectroscopic features were examined under light sources specifically designed to mimic the spectrum of solar flares (Rimmer’s FlareLab; Cavendish Laboratory).

Researchers from Harvard University provided measurements of the UV absorbance patterns of ferric iron under extreme acidic conditions, in an attempt to mimic the even more extreme Venusian clouds. The scientists are part of the newly-established Origins Federation, which promotes such collaborative projects.

“The patterns and level of absorption shown by the combination of these two mineral phases are consistent with the dark UV-patches observed in Venusian clouds,” said co-author Clancy Zhijian Jiang, from the Department of Earth Sciences, Cambridge. “These targeted experiments revealed the intricate chemical network within the atmosphere, and shed light on the elemental cycling on the Venusian surface.”

“Venus is our nearest neighbour, but it remains a mystery,” said Rimmer. “We will have a chance to learn much more about this planet in the coming years with future NASA and ESA missions set to explore its atmosphere, clouds and surface. This study prepares the grounds for these future explorations.”

The research was supported by the Simons Foundation, and the Origins Federation.

Reference:
Clancy Zhijian Jiang et al., ‘Iron-sulfur chemistry can explain the ultraviolet absorber in the clouds of Venus.’ Science Advances (2024). DOI:10.1126/sciadv.adg8826

Researchers may have identified the missing component in the chemistry of the Venusian clouds that would explain their colour and 'splotchiness' in the UV range, solving a longstanding mystery.

FreelanceImages/Universal Images Group/Science Photo Library via Getty ImagesSunrise over Venus


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Diamonds and rust help unveil ‘impossible’ quasi-particles

Tue, 05/12/2023 - 10:02

Researchers led by the University of Cambridge used a technique known as diamond quantum sensing to observe swirling textures and faint magnetic signals on the surface of hematite, a type of iron oxide.

The researchers observed that magnetic monopoles in hematite emerge through the collective behaviour of many spins (the angular momentum of a particle). These monopoles glide across the swirling textures on the surface of the hematite, like tiny hockey pucks of magnetic charge. This is the first time that naturally occurring emergent monopoles have been observed experimentally.

The research has also shown the direct connection between the previously hidden swirling textures and the magnetic charges of materials like hematite, as if there is a secret code linking them together. The results, which could be useful in enabling next-generation logic and memory applications, are reported in the journal Nature Materials.

According to the equations of James Clerk Maxwell, a giant of Cambridge physics, magnetic objects, whether a fridge magnet or the Earth itself, must always exist as a pair of magnetic poles that cannot be isolated.

“The magnets we use every day have two poles: north and south,” said Professor Mete Atatüre, who led the research. “In the 19th century, it was hypothesised that monopoles could exist. But in one of his foundational equations for the study of electromagnetism, James Clerk Maxwell disagreed.”

Atatüre is Head of Cambridge’s Cavendish Laboratory, a position once held by Maxwell himself. “If monopoles did exist, and we were able to isolate them, it would be like finding a missing puzzle piece that was assumed to be lost,” he said.

About 15 years ago, scientists suggested how monopoles could exist in a magnetic material. This theoretical result relied on the extreme separation of north and south poles so that locally each pole appeared isolated in an exotic material called spin ice.

However, there is an alternative strategy to find monopoles, involving the concept of emergence. The idea of emergence is the combination of many physical entities can give rise to properties that are either more than or different to the sum of their parts.

Working with colleagues from the University of Oxford and the National University of Singapore, the Cambridge researchers used emergence to uncover monopoles spread over two-dimensional space, gliding across the swirling textures on the surface of a magnetic material.

The swirling topological textures are found in two main types of materials: ferromagnets and antiferromagnets. Of the two, antiferromagnets are more stable than ferromagnets, but they are more difficult to study, as they don’t have a strong magnetic signature.

To study the behaviour of antiferromagnets, Atatüre and his colleagues use an imaging technique known as diamond quantum magnetometry. This technique uses a single spin – the inherent angular momentum of an electron – in a diamond needle to precisely measure the magnetic field on the surface of a material, without affecting its behaviour.

For the current study, the researchers used the technique to look at hematite, an antiferromagnetic iron oxide material. To their surprise, they found hidden patterns of magnetic charges within hematite, including monopoles, dipoles and quadrupoles.

“Monopoles had been predicted theoretically, but this is the first time we’ve actually seen a two-dimensional monopole in a naturally occurring magnet,” said co-author Professor Paolo Radaelli, from the University of Oxford.

“These monopoles are a collective state of many spins that twirl around a singularity rather than a single fixed particle, so they emerge through many-body interactions. The result is a tiny, localised stable particle with diverging magnetic field coming out of it,” said co-first author Dr Hariom Jani, from the University of Oxford.

“We’ve shown how diamond quantum magnetometry could be used to unravel the mysterious behaviour of magnetism in two-dimensional quantum materials, which could open up new fields of study in this area,” said co-first author Dr Anthony Tan, from the Cavendish Laboratory. “The challenge has always been direct imaging of these textures in antiferromagnets due to their weaker magnetic pull, but now we’re able to do so, with a nice combination of diamonds and rust.”

The study not only highlights the potential of diamond quantum magnetometry but also underscores its capacity to uncover and investigate hidden magnetic phenomena in quantum materials. If controlled, these swirling textures dressed in magnetic charges could power super-fast and energy-efficient computer memory logic.

The research was supported in part by the Royal Society, the Sir Henry Royce Institute, the European Union, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
K. C. Tan, Hariom Jani, Michael Högen et al. ‘Revealing Emergent Magnetic Charge in an Antiferromagnet with Diamond Quantum Magnetometry.’ Nature Materials (2023). DOI: 10.1038/s41563-023-01737-4.

Researchers have discovered magnetic monopoles – isolated magnetic charges – in a material closely related to rust, a result that could be used to power greener and faster computing technologies.

If monopoles did exist, and we were able to isolate them, it would be like finding a missing puzzle piece that was assumed to be lostMete AtatüreAnthony Tan and Michael HoegenMagnetic monopoles in hematite


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