Schematic of the antineutrino detector and reactors in the proposed area it is theoretically able to detect. Credit: Stephen Wilson
Nuclear fission reactors act as a key power source for many parts of the world and worldwide power capacity is expected to nearly double by 2050. One issue, however, is the difficulty of discerning whether a nuclear reactor is being used to also create material for nuclear weapons.
Capturing and analyzing antimatter particles has shown promise for monitoring what specific reactor operations are occurring, even from hundreds of miles away.
In AIP Advances, researchers from the University of Sheffield and the University of Hawaii developed a detector that senses and analyzes antineutrinos emitted by nuclear reactors. The detector, designed by Stephen Wilson and colleagues, senses antineutrinos and can characterize their energy profiles from miles away as a way of monitoring activity at nuclear reactors.
“In this paper, we test a detector design that could be used to measure the energy of particle emission of nuclear fission reactors at large distances,” said author Wilson. “This information could tell us not only whether a reactor exists and about its operational cycle, but also how far away the reactor is.”
Neutrinos are chargeless elementary particles that have a mass of nearly zero, and antineutrinos are their antimatter counterpart, most often created during nuclear reactions. Capturing these antiparticles and analyzing their energy levels provides information on anything from operational cycle to specific isotopes in spent fuel.
The group’s detector design exploits Cherenkov radiation, a phenomenon in which radiation is emitted when charged particles moving faster than light pass through a particular medium, akin to sonic booms when crossing the sound barrier. This is also responsible for nuclear reactors’ eerie blue glow and has been used to detect neutrinos in astrophysics laboratories.
The researchers proposed to assemble their device in northeast England and detect antineutrinos from reactors from all over the U.K. as well as in northern France.
One issue, however, is that antineutrinos from the upper atmosphere and space can muddle the signal, especially as very distant reactors yield exceedingly small signals—sometimes on the order of a single antineutrino per day.
To account for this, the group proposed to place their detector in a mine more than 1 kilometer underground.
“Discriminating between these particles is also a significant analysis challenge, and being able to measure an energy spectrum can take an impractically long time,” Wilson said. “In many ways, what surprised me most is that this is not actually impossible.”
Wilson hopes the detector stimulates more discussion in how to use antineutrinos to monitor reactors, including measuring the antineutrino spectrum of spent nuclear fuel or developing smaller detectors for use closer to reactors.
More information:
Remote reactor ranging via antineutrino oscillations, AIP Advances (2024). DOI: 10.1063/5.0220877
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Using antimatter to detect nuclear radiation: Byproducts of fission reactors provide insight into nuclear reactor use (2024, October 1)
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“To get rich is glorious.” In the 1980s, this was one of the most famous sayings—unofficially, at least—to describe the ethos at the dawn of the opening-up period in post-Maoist China.
China’s paramount leader of the time, Deng Xiaoping, apparently justified this unorthodox situation for an ostensibly egalitarian Communist country by saying: “First you let some make money, then more will follow.”
And make money they did. One of the earliest examples was Nian Guangjiu, the founder of Chinese snack company Fool’s Melon Seeds, who went from being a poor farmer to a wealthy entrepreneur within a few years. But Nian’s story is something of a morality tale for those who followed.
He spent time in prison accused of embezzlement and other crimes in 1989, and his company was taken from him. To get rich in China was indeed possible, but it was a route that often led to jail and perdition.
Despite this, the scale and speed of growth in China—and the importance of business people, often in the private sector, in generating this—means the country now has a shared phenomenon with the developed world: a group of fabulously wealthy people.
When the first Chinese “rich list” was produced by Forbes in 1999, there was only one US dollar billionaire—a Hong Kong magnate based on the mainland called Rong Yiren.
By 2010, this number had risen, even by a conservative estimate, to over 60. And over the next decade, it soared to 389—a stunning example of how far China had come since the almost universal poverty of the Maoist years half a century earlier.
The Hurun Report’s wealth tracker, which uses a different methodology in calculating and valuing assets, has gone even further, suggesting China currently has the most billionaires in the world at 814, outpacing the US on 800.
Billionaires may have been a good indicator of China’s dynamic economy—but they are also an all-too-obvious symbol of its sharpening inequality. The Gini coefficient, an international standard of disparities between the richest and poorest in societies, had China in 2021 as significantly more unequal than the US or UK.
Since China’s current president, Xi Jinping, came to power in 2012, his stated ethos has been to “serve the people” and deliver “common prosperity.” That means more wealth, but more equitably shared out. “Common prosperity” was an almost ubiquitous slogan plastered across walls in Beijing and Shanghai when I visited both cities in late August 2024.
However, China’s economy is currently undergoing turbulence as a result of the residual effects of the pandemic and ongoing tensions with the US. The downturn has been severe enough to prompt the country’s central bank into announcing a major stimulus package which has, at least, sparked a rally on China’s stock market.
So, these days, ostentatious wealth and billionaires appearing like they are above the law are unwelcome. According to the Hurun Report data, China lost about 155 members of this elite group between 2023 and 2024, down to its current estimate of 814 billionaires.
Wealth comes at a cost
Beyond the fact that China’s economy has been slowing over the past two years, and the situation in terms of cost of living generally has grown tougher for everyone, there are some other factors that lie behind this chilling of the atmosphere for China’s super-rich.
Some of China’s best-known billionaires, including Alibaba founder Jack Ma Yun, have reportedly left the country after experiencing a political backlash for making statements regarded as disloyal and too critical of the Chinese government and official regulators. And other super-rich individuals may have taken such high-profile cases as good reason to preempt possible trouble by moving out of China.
While not in the class of the absolute richest of the rich, 13,800 millionaires departed China in 2023 according to one report, mostly to the US, Canada and Singapore. The people in this group that I know of seem to be motivated by a mixture of worries about the economics and politics of their home country. The fact it is increasingly difficult to get assets and cash out of China underlines how badly these people want to be based somewhere else.
We shouldn’t overstate the issue, though. It is still OK to be rich in China, if a bit less so than in the past. But it’s probably more advisable to be low profile and very loyal in public to the Communist party—and to get rich working in high-tech sectors that the government favors.
For example, Wang Chuanfu, founder of Chinese electronic vehicle manufacturer BYD, has doubled his wealth in the space of a few years. According to Forbes, he is now worth US$20 billion (£14.9 billion). The environmental and strategic value of his company, and what it makes, means he is relatively safe to continue living in Shenzhen, where he is currently based.
Ironically, though, the richest person in China since 2021 is Zhong Shanshan, who produces very non-technical bottled mineral water under the Nongfu brand. Ultimately, a key factor in keeping out of trouble if you are super-rich in China is either to be in super-high or very low-technology businesses.
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Why it’s becoming harder to get super-rich in China (2024, October 1)
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The rise of artificial intelligence (AI) has triggered concern about potentially detrimental effects on humans. However, the technology also has the potential to harm animals.
An important policy reform now underway in Australia offers an opportunity to address this. The federal government has committed A$5 million to renewing the lapsed Australian Animal Welfare Strategy. Consultation has begun, and the final strategy is expected in 2027.
While AI is not an explicit focus of the review, it should be.
Australians care about animals. The strategy could help ensure decision-makers protect animals from AI’s harm in our homes, on farms and in the wild.
Will AI harms to animals go unchecked?
Computers are now so developed they can perform some complex tasks as well as, or better than, humans. In other words, they have developed a degree of “artificial intelligence“.
So far, documents released as part of the review suggest AI is not being considered under the strategy. That is a serious omission, for reasons we outline below.
Powerful and pervasive technology in use
Much AI use benefits animals, such as in veterinary medicine. For example, it may soon help your vet read X-rays of your animal companion.
AI is being developed to detect pain in cats and dogs. This might help if the technology is accurate, but could cause harm if it’s inaccurate by either over-reporting pain or failing to detect discomfort.
AI may also allow humans to decipher animal communication and better understand animals’ point of view, such as interpreting whale song.
It has also been used to discover which trees and artificial structures are best for birds.
But when it comes to animals, research suggests AI may also be used to harm them.
There are plans to use AI to distinguish cats from native species and then kill the cats. Yet, AI image recognition tools have not been sufficiently trained to accurately identify many wild species. They are biased towards North American species, because that is where the bulk of the data and training comes from.
Algorithms using AI tend to promote more salacious content, so they are likely to also recommend animal cruelty videos on various platforms. For example, YouTube contains content involving horrific animal abuse.
Some AI technologies are used in harmful animal experiments. Elon Musk’s brain implant company Neuralink, for instance, was accused of rushing experiments that harmed and killed monkeys.
Researchers warn AI could estrange humans from animals and cause us to care less about them. Imagine AI farms almost entirely run by smart systems that “look after” the animals. This would reduce opportunities for humans to notice and respond to animal needs.
Existing regulatory frameworks are inadequate
Australia’s animal welfare laws are already flawed and fail to address existing harms. They allow some animals to be confined to very small spaces, such as chickens in battery cages or pigs in sow stalls and farrowing crates. Painful procedures (such as mulesing, tail docking and beak trimming) can be legally performed without pain relief.
Only widespread community outrage forces governments to end the most controversial practices, such as the export of live sheep by sea.
This has implications for the development and use of artificial intelligence. Reform is needed to ensure AI does not amplify these existing animal harms, or contribute to new ones.
Internationally, some governments are responding to the need for reform.
The United Kingdom’s online safety laws now requiresocial media platforms to proactively monitor and remove illegal animal cruelty content from their platforms. In Brazil, Meta (the owner of Facebook and WhatsApp) was recently fined for not taking down posts that had been tagged as illegal wildlife trading.
The EU’s new AI Act also takes a small step towards recognizing how the technology affects the environment we share with other animals.
Among other aims, the law encourages the AI industry to track and minimize the carbon and other environmental impact of AI systems. This would benefit animal as well as human health.
The current refresh of the Australian Animal Welfare Strategy, jointly led by federal, state and territory governments, gives us a chance to respond to the AI threat. It should be updated to consider how AI affects animal interests.
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Animals in the machine: Why the law needs to protect animals from AI (2024, October 1)
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Kevin Wood, a Chamberlain Postdoctoral Fellow at Berkeley Lab and run coordinator for the 2×2 prototype and Brooke Russell, now the Neil and Jane Pappalardo Special Fellow in Physics at MIT and the 2×2 prototype’s charge readout expert, examine the 2×2 prototype detector. Credit: Dan Svoboda/Fermilab
Researchers at the U.S. Department of Energy’s SLAC National Accelerator Laboratory have joined collaborators from around the world to build a prototype neutrino detector that has now captured its first neutrino interactions at Fermi National Accelerator Laboratory (Fermilab).
The prototype detector will help fine-tune a full-size version of the DUNE Near Detector Liquid Argon (ND-LAr) detector in the coming years for the international Deep Underground Neutrino Experiment (DUNE), led by Fermilab, and in the meantime help illuminate some specific neutrino properties.
Researchers will also use the detector to test advanced machine learning techniques, developed at SLAC, that are expected to play a key role in processing the vast amount of data generated by DUNE.
Scientists will also use data from the prototype to study electron neutrinos, which are one of three known neutrino types. Nearly all of the neutrinos that come out of the neutrino beam at Fermilab will be muon neutrinos, but one in about 1,000 will be electron neutrinos.
“DUNE needs to measure the oscillation of muon neutrinos to electron neutrinos by counting both interactions,” Sinclair said. “We know that the interaction probability of electron neutrinos is different from muon neutrinos. The 2×2 will allow us to study and verify the new detector’s capability to identify and study electron neutrino interactions.”
Four sturdy boxes make a novel detector
Although the module system might seem simple, it faces a practical challenge, Tanaka said. It puts a lot more stuff in the way of detecting neutrinos. It fell to longtime SLAC mechanical engineer Knut Skarpaas VIII and his colleagues to design a system that was light, sturdy, and could withstand the very cold temperatures of liquid argon.
Skarpaas worked on many of the components for the TPC modules with collaborators at the University of Bern and Berkeley Lab. When Skarpaas first heard about the prototype detector, he walked up to a chalkboard and sketched a possible design of it. Many years later, the detector looked nearly identical to those initial drawings.
After completing the design, Skarpaas and the team focused on building the prototype’s electrostatic field cages, the boxes that contained all of the detector’s electronic components and the liquid argon. This cage defines the volume of the prototype, and everything has to fit inside of that volume.
Additionally, the team had to squeeze a high-voltage cathode, which guides those ionization electrons toward an anode, into the cage without touching any other metal parts. If metal touched the cathode, this could create an electrical arc, jeopardizing the detector equipment.
Perhaps the most difficult part of the building process was selecting the right power cable. The cable feeds electricity to the high-voltage cathode and makes the whole detector work, and it needs to be straight, cannot touch any other parts and must be able to shrink up to two inches due to the cold temperature inside of the detector. If the cable bends under these cold temperatures, it could shatter.
After many long days inside a machine shop at SLAC, Skarpaas and the team finished assembly and shipped the modules to the University of Bern for testing.
“Putting all of a detector’s pieces together is like being the conductor of an orchestra,” Skarpaas said. “You have to understand what everyone needs for their science goals and then meld these needs together to build the detector.”
Advanced machine learning techniques
DUNE’s primary goal is to explore some of the deepest questions about the composition of the universe by studying neutrino properties. To do this, researchers need to not only capture neutrino interactions, but also make sense of the data generated by these interactions.
In the case of the prototype detector, the data generated by up to thousands of neutrino interactions per day would be impossible for scientists to study manually image by image. Researchers therefore invented new machine learning techniques for this amount of data. Machine learning is a type of artificial intelligence that detects patterns in large datasets, then uses those patterns to make predictions and improve future rounds of analysis.
“By eye, it might be easy to find the information you need in a single image generated by the detector,” SLAC researcher Francois Drielsma said. “But it is difficult to teach a machine to perform this task. Sometimes there is the thought that if something is simple for a human being, it should be simple for a machine. But that is not necessarily true.”
Still, humans aren’t up to scanning millions of images at a time. They’ve also struggled to use traditional programming techniques to help identify objects in detector data, so Drielsma’s group started working on a machine learning technique called neural networks, a type of algorithm loosely modeled after the human brain.
Once a neural network is trained on a large set of data—whether from particle interactions or astronomical images—it can automatically analyze other complex datasets, almost instantaneously and with great precision.
The program is working better each day, and researchers will continue to fine-tune its performance over the coming years while the prototype detector is collecting data.
“It’s going to be a difficult task to train the program to do everything we want accurately,” Drielsma said. “But when things are difficult, they can be really entertaining.”
“The prototype is going to be very important because it’s the only source of neutrino beam data at energies comparable to the DUNE beam that will be available before DUNE is running,” said James Sinclair, a SLAC scientist working on the project. “We are excited to be completing this critical step in the experiment and are now ready to study the data that’s coming in.”
A modular design for an unusual problem
Neutrinos are fundamental particles unlike any other. They can pass through almost all matter largely unseen and can change forms along the way—a phenomenon called neutrino oscillation. Scientists think a better understanding of their unusual properties could help answer some of the most challenging questions about the origin of matter in the universe and the pattern of neutrino masses.
To detect neutrinos, physicists use what’s called a time projection chamber (TPC)—a vast tank of liquified noble gases such as argon. When a particle enters the chamber from outside, two things happen.
First, interactions between the particle and argon atoms create flashes of light called scintillation. Second, the particle can knock electrons free from argon atoms, ionizing them. TPCs typically include photosensitive equipment to detect scintillation and an electric field that guides free electrons to one end of the detector, where—traditionally—a wire mesh picks them up as an electrical current.
By comparing details of the flash with the time it takes electrons to arrive at the mesh, researchers can identify key details including what kinds of particles they’re picking up and how fast those particles are moving.
The idea is to capture as many neutrino interactions as possible with a large volume of argon and a relatively small amount of detector equipment, almost all of which stays on the periphery of that volume.
But something more is needed for DUNE, said SLAC scientist Hiro Tanaka, the technical director for the DUNE near detector and head of SLAC’s efforts on the DUNE project.
Unlike many other neutrino experiments, DUNE will produce a very large number of neutrinos and beam them in bunches toward DUNE’s near detector outside Chicago.
Over the course of just a few microseconds, scientists expect to see multiple neutrino interactions in the near detector. The trouble is, all those interactions make it hard to tell which flash of light belongs to which neutrino, in part because large tanks of liquid argon scatter and diffuse each individual flash.
It also makes it hard to tell which electron comes from which ionization event, since any one electron takes milliseconds to reach the edge of a TPC, during which time many interactions may have occurred.
It was out of these concerns that the newly minted prototype, called the 2×2 detector, was born. On one level, the idea is simple: rather than use one giant TPC, break the device into a set of four TPC modules arranged in a two-by-two grid—hence the name.
Each module actually contains two separate volumes of argon with an opaque wall in the center. That wall effectively creates eight optically separate TPC tanks, so that it’s less likely to mistake one neutrino flash for another. It also serves as the source of the electric field that draws ionization electrons to the sides of the detector module.
In addition, each module contains a new system for detecting ionization electrons developed at DOE’s Lawrence Berkeley National Laboratory that picks up not just when the electrons arrive, but also precisely where, in contrast to the traditional wire-based designs, where the information provided by each plane of wires can be difficult to reconcile in the high interaction-rate environment of the DUNE near detector.
Combined with the light flashes, this will help researchers determine where neutrino interactions occurred for the first time without ambiguity in three dimensions.
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A modular neutrino detector years in the making (2024, October 1)
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Project development with back-ended and front-ended risk. Credit: Nature Communications (2024). DOI: 10.1038/s41467-024-51661-7
Australia risks losing its world-leading advantage in critical and rare minerals used for clean energy, electric vehicles and batteries for solar energy, unless it embraces artificial intelligence in the mining sector, according to research from Monash University and the University of Tasmania.
Australia is in a prime position to benefit with the world’s largest proven reserves of nickel and zinc, the second largest proven reserves of cobalt and copper and the world’s third largest proven reserves of bauxite. It is also the world’s largest producer of bauxite and lithium and is the third largest producer of cobalt.
Co-researcher Deputy Dean, Research, Professor Russell Smyth, from the Department of Economics at Monash University said to take advantage of these resources, Australia must embrace AI through all stages of the mining process.
“With the right policies and technological advancements, AI has the potential to transform the mining industry, making it more efficient, cost effective, less risky, and environmentally friendly,” said Professor Smyth.
Critical and rare minerals are a crucial part of achieving net zero emissions by 2050. But the International Energy Agency (IEA) has identified it takes 12.5 years from exploration to production, meaning investors see it as too risky.
In order to achieve global net zero by 2050, the IEA estimates investment of US $360-450 billion will be necessary by 2030, leading to an anticipated supply between US $180-220 billion. This implies an investment shortfall of up to US $230 billion.
Such a shortfall could lead to insufficient supply in the future, making decarbonization efforts more costly and potentially slowing them down. Professor Smyth said their research could help address a number of these issues.
“AI could improve processes such as mineral mapping by using drone-based photogrammetry and remote sensing; more accurately calculate the life of the mine and improve mining productivity including drilling and blasting performance,” said Professor Smyth.
“AI can also be used to reduce the required rate of return on investment by forecasting the risk of cost blow-outs, as well as equipment planning and predictive maintenance and management of equipment to minimize repairs.”
Co-researcher Associate Professor Joaquin Vespignani, from the Tasmanian School of Business and Economics at the University of Tasmania, said their theory suggests that back-ended critical mineral projects that have unaddressed technical and non-technical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors, which they term the back-ended risk premium.
“We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We proposed that the back-ended risk premium may also reduce the gains in productivity expected from AI technologies in the mining sector,” Associate Professor Vespignani said.
“Progress in AI may, however, lessen the back-ended risk premium itself by shortening the duration of mining projects and the required rate of investment by reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research.
“Without significant investment by governments around the world in AI within the mining industry to increase productivity and improve environmental practices, there is a high risk that the clean energy transition will become costly for communities, potentially slowing down decarbonization efforts.”
More information:
Joaquin Vespignani et al, Artificial intelligence investments reduce risks to critical mineral supply, Nature Communications (2024). DOI: 10.1038/s41467-024-51661-7
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Using artificial intelligence to reduce risks to critical mineral supply (2024, October 1)
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