A team of physicists and engineers at China’s Hefei National Laboratory has succeeded in conducting the first instance of precise absolute distance measurement over a path exceeding 100 km. The group has written a paper describing how they achieved such a feat and posted it on the arXiv preprint server.
As scientists develop ever more sophisticated technology, the need for more precise measurement grows. One such application is satellite formation flying. For it to be done as precisely as needed, new ways to very accurately measure long distances are needed—such as from a satellite to the ground, and back. In this new effort, the research team in China has found a way to measure such distances with unprecedented precision.
The work by the team involved adding an improvement to a measuring technique involving the use of an optical comb—a device that allows for averaging the time it takes for multiple wavelengths of light to travel to a target and bounce back—while also analyzing interference patterns that may have arisen during the trip.
Such technology has proven to be very precise when measuring relatively short distances. It has not worked very well over long distances, unfortunately, because the light is subject to noise and environmental factors such as humidity and temperature. In this new effort, the team overcame these problems to extend the use of optical combs to distances over 100 kilometers.
The solution, the team found, was to add a second comb at the other end of the space to be measured. Doing so caused the beams of light to interfere with one another, resulting in patterns that could be used to measure the distance between them. The technique works, the researchers note, because while the combs are indistinguishable, the interference patterns created at each end are not.
Because the beams are traveling in reverse directions relative to each other, comparison of the differences in interference patterns can be resolved, resulting in mitigating the impacts of environmental noise. The result is an extremely precise way to measure objects that are more than a kilometer apart.
The team proved the effectiveness of their approach by using it to measure objects 113 kilometers apart with a precision of 82 nm. They also note that their test is the first to achieve such precision over such a long distance.
More information: Yan-Wei Chen et al, 113 km absolute ranging with nanometer precision, arXiv (2024). DOI: 10.48550/arxiv.2412.05542
Plasmons are collective oscillations of electrons in a solid and are important for a wide range of applications, such as sensing, catalysis, and light harvesting. Plasmonic waves that travel along the surface of a metal, called surface plasmon polaritons, have been studied for their ability to enhance electromagnetic fields.
One of the most powerful tools for studying these waves is time-resolved electron microscopy, which uses ultrashort laser pulses to observe how these plasmonic waves behave. An international research team recently pushed the boundaries of this technique.
As reported in Advanced Photonics, the researchers used multiple time-delayed laser pulses of four different polarizations to capture the full electric field of these waves. This method allowed them to achieve a level of accuracy previously not possible.
To test their technique, the team investigated a specific spin texture known as a meron pair. A meron is a topological structure where the direction of the spin texture only covers half of a sphere, which distinguishes it from other similar structures, like skyrmions, whose spin covers the entire sphere.
To reconstruct the spin texture from the experiment, the researchers needed the electric and magnetic field vectors of the surface plasmon polaritons. While the electric field vectors could be directly measured, the magnetic field vectors had to be calculated based on the electric field’s behavior over time and space.
By using their precise method, the researchers were able to reconstruct the spin texture and determine its topological properties, such as the Chern number, which describes the number of times the spin texture maps onto a sphere. In this case, the Chern number was found to be one, indicating the presence of a meron pair.
The study also demonstrated that the spin texture remains stable throughout the duration of the plasmonic pulse, despite the fast rotation of the electric and magnetic field vectors. This new approach is not limited to meron pairs and can be applied to other complex surface plasmon polariton fields.
Understanding these fields and their topological properties is important, especially at the nanoscale, where topological protection can help maintain the stability of materials and devices.
This research shows that it is now possible to study complex spin textures with high precision on extremely short timescales. The ability to accurately reconstruct the full electric and magnetic fields of surface plasmon polaritons opens new possibilities for exploring the topological properties of electromagnetic near fields, which may have important implications for future technologies at the nanoscale.
More information: Pascal Dreher et al, Spatiotemporal topology of plasmonic spin meron pairs revealed by polarimetric photo-emission microscopy, Advanced Photonics (2024). DOI: 10.1117/1.AP.6.6.066007
Quantum walks are a powerful theoretical model using quantum effects such as superposition, interference and entanglement to achieve computing power beyond classical methods.
A research team at the National Innovation Institute of Defense Technology from the Academy of Military Sciences (China) recently published a review article that thoroughly summarizes the theories and characteristics, physical implementations, applications and challenges of quantum walks and quantum walk computing. The review was published Nov. 13 in Intelligent Computing in an article titled “Quantum Walk Computing: Theory, Implementation, and Application.”
As quantum mechanical equivalents of classical random walks, quantum walks use quantum phenomena to design advanced algorithms for applications such as database search, network analysis and navigation, and quantum simulations. Different types of quantum walks include discrete-time quantum walks, continuous-time quantum walks, discontinuous quantum walks, and nonunitary quantum walks. Each model presents unique features and computational advantages.
Discrete-time quantum walks involve step-by-step transitions without a time factor, using coin-based models like Hadamard and Grover walks or coinless models such as Szegedy and staggered quantum walks for graph-based movement. In contrast, continuous-time quantum walks operate on graphs using time-independent Hamiltonians, making them particularly useful for spatial searches and traversal problems.
Discontinuous quantum walks combine the properties of both discrete-time and continuous-time models, enabling universal computation through perfect state transfers. Meanwhile, nonunitary quantum walks, including stochastic quantum walks and open quantum walks, act as open quantum systems and find applications in simulating photosynthesis and quantum Markov processes.
The two original branches, discrete-time and continuous-time quantum walks, achieve faster diffusion than classical random walk models and exhibit similar probability distributions. To some extent, discrete-time and continuous-time models are interchangeable. In addition, various discrete models can be interchanged based on the graph structure, highlighting the versatility of quantum walk models.
According to the authors, quantum walks not only have evolutionary merits, but also improve sampling efficiency, solving problems previously considered computationally difficult for classical systems.
The wide variety of physical quantum systems used to implement quantum walks demonstrates the utility of discrete-time and continuous-time quantum walk models and quantum-walk-based algorithms. There are two different approaches to physically implementing quantum walks:
Analog physical simulation primarily uses solid-state, optical and photonic systems to directly implement specific Hamiltonians without translation into quantum logic. This approach enables scalability by increasing particle numbers and dimensions but lacks error correction and fault tolerance. It faces challenges in efficiently simulating large graphs.
Digital physical simulation constructs quantum circuits to simulate quantum walks, offering error correction and fault tolerance. Designing efficient circuits remains difficult, but digital implementations can achieve quantum speedup and simulate a variety of graphs.
Quantum walk applications are categorized into four main categories: quantum computing, quantum simulation, quantum information processing and graph-theoretic applications.
Quantum Computing: Quantum walks enable universal quantum computation and accelerate computations in algebraic and number-theoretic problems. They are also being explored for applications in machine learning and optimization.
Quantum Simulation: Quantum walks are an important tool for simulating the behavior of uncontrollable quantum systems, providing insight into complex quantum phenomena that are difficult or impossible to analyze classically. Applications include simulating multi-particle systems, solving complex physics problems, and modeling biochemical processes.
Quantum Information Processing: Quantum walks are used for the preparation, manipulation, characterization and transmission of quantum states, as well as in quantum cryptography and security applications.
Graph-Theoretic Applications: Quantum walks, associated with graph structures, provide promising solutions for graph-theoretic problems and various network applications. They are used to explore graph characteristics, rank vertex centrality and identify structural differences between graphs.
Despite rapid progress, practical quantum walk computing faces challenges, including devising effective algorithms, scaling up the physical implementations and implementing quantum walks with error correction or fault tolerance. These challenges, however, provide a roadmap for future innovations and advancements in the field.
More information: Xiaogang Qiang et al, Quantum Walk Computing: Theory, Implementation, and Application, Intelligent Computing (2024). DOI: 10.34133/icomputing.0097
Researchers at Rice University have made a meaningful advance in the simulation of molecular electron transfer—a fundamental process underpinning countless physical, chemical and biological processes. The study, published in Science Advances, details the use of a trapped-ion quantum simulator to model electron transfer dynamics with unprecedented tunability, unlocking new opportunities for scientific exploration in fields ranging from molecular electronics to photosynthesis.
Electron transfer, critical to processes such as cellular respiration and energy harvesting in plants, has long posed challenges to scientists due to the complex quantum interactions involved. Current computational techniques often fall short of capturing the full scope of these processes. The multidisciplinary team at Rice, including physicists, chemists and biologists, addressed these challenges by creating a programmable quantum system capable of independently controlling the key factors in electron transfer: donor-acceptor energy gaps, electronic and vibronic couplings and environmental dissipation.
Using an ion crystal trapped in a vacuum system and manipulated by laser light, the researchers demonstrated the ability to simulate real-time spin dynamics and measure transfer rates across a range of conditions. The findings not only validate key theories of quantum mechanics but also pave the way for novel insights into light-harvesting systems and molecular devices.
“This is the first time that this kind of model was simulated on a physical device while including the role of the environment and even tailoring it in a controlled way,” said lead researcher Guido Pagano, assistant professor of physics and astronomy. “It represents a significant leap forward in our ability to use quantum simulators to investigate models and regimes that are relevant for chemistry and biology. The hope is that by harnessing the power of quantum simulation, we will eventually be able to explore scenarios that are currently inaccessible to classical computational methods.”
The team achieved a significant milestone by successfully replicating a standard model of molecular electron transfer using a programmable quantum platform. Through the precise engineering of tunable dissipation, the researchers explored both adiabatic and nonadiabatic regimes of electron transfer, demonstrating how these quantum effects operate under varying conditions. Additionally, their simulations identified optimal conditions for electron transfer, which parallel the energy transport mechanisms observed in natural photosynthetic systems.
“Our work is driven by the question: Can quantum hardware be used to directly simulate chemical dynamics?” Pagano said. “Specifically, can we incorporate environmental effects into these simulations as they play a crucial role in processes essential to life such as photosynthesis and electron transfer in biomolecules? Addressing this question is significant as the ability to directly simulate electron transfer in biomolecules could provide valuable insights for designing new light-harvesting materials.”
The implications for practical applications are far-reaching. Understanding electron transfer processes at this level could lead to breakthroughs in renewable energy technologies, molecular electronics and even the development of new materials for quantum computing.
“This experiment is a promising first step to gain a deeper understanding of how quantum effects influence energy transport, particularly in biological systems like photosynthetic complexes,” said Jose N. Onuchic, study co-author, the Harry C. and Olga K. Wiess Chair of Physics and professor of physics and astronomy, chemistry and biosciences. “The insights we gain in this type of experiment could inspire the design of more efficient light-harvesting materials.”
Peter G. Wolynes, study co-author, the D.R. Bullard-Welch Foundation Professor of Science and professor of chemistry, biosciences and physics and astronomy, emphasized the broader significance of the findings: “This research bridges the gap between theoretical predictions and experimental verification, offering an exquisitely tunable framework for exploring quantum processes in complex systems.”
The team plans to extend its simulations to include more complex molecular systems such as those involved in photosynthesis and DNA charge transport. The researchers also hope to investigate the role of quantum coherence and delocalization in energy transfer, leveraging the unique capabilities of their quantum platform.
“This is just the beginning,” said Han Pu, co-lead author of the study and professor of physics and astronomy. “We are excited to explore how this technology can help unravel the quantum mysteries of life and beyond.”
The study’s other co-authors include graduate students Visal So, Midhuna Duraisamy Suganthi, Abhishek Menon, Mingjian Zhu and research scientist Roman Zhuravel.
More information: Visal So et al, Trapped-ion quantum simulation of electron transfer models with tunable dissipation, Science Advances (2024). DOI: 10.1126/sciadv.ads8011
A top-secret lab in the UK is developing the country’s first quantum clock to help the British military boost intelligence and reconnaissance operations, the defense ministry said Thursday.
The clock is so precise that it will lose less than one second over billions of years, “allowing scientists to measure time at an unprecedented scale,” the ministry said in a statement.
“The trialing of this emerging, groundbreaking technology could not only strengthen our operational capability, but also drive progress in industry, bolster our science sector and support high-skilled jobs,” Minister for Defense Procurement Maria Eagle said.
The groundbreaking technology by the Defense Science and Technology Laboratory will reduce reliance on GPS technology, which “can be disrupted and blocked by adversaries,” the ministry added.
It is not a world first, as the University of Colorado at Boulder developed a quantum clock 15 years ago with the US National Institute of Standards and Technology.
But it is “the first device of its kind to be built in the UK,” the statement said, adding it could be deployed by the military “in the next five years”.
A quantum clock uses quantum mechanics — the physics of matter and energy at the atomic and subatomic scale — to keep time with unprecedented accuracy by measuring energy fluctuations within atoms.
Accurate timekeeping is crucial for satellite navigation systems, mobile telephones and digital TV, among other applications, and may open new frontiers in research fields such as quantum science.
Companies and governments around the world are keen to cash in on the huge potential benefits quantum technology could bring.
Google last month unveiled a new quantum computing chip it said could do in minutes what it would take leading supercomputers 10 septillion years to complete.
The United States and China are investing heavily in quantum research, and the US administration has imposed tight restrictions on exporting such sensitive technology.
One expert, Olivier Ezratty, told AFP in October that private and public investment in such technology had reached $20 billion during the past five years.
The defense ministry said future research would “see the technology decrease in size to allow mass manufacturing and miniaturization, unlocking a wide range of applications, such as use by military vehicles and aircraft”.
Detecting infrared light is critical in an enormous range of technologies, from remote controls to autofocus systems to self-driving cars and virtual reality headsets. That means there would be major benefits from improving the efficiency of infrared sensors, such as photodiodes.
Researchers at Aalto University have developed a new type of infrared photodiode that is 35% more responsive at 1.55 µm, the key wavelength for telecommunications, compared to other germanium-based components. Importantly, this new device can be manufactured using current production techniques, making it highly practical for adoption.
“It took us eight years from the idea to proof-of-concept,” says Hele Savin, a professor at Aalto University.
The basic idea is to make the photodiodes using germanium instead of indium gallium arsenide. Germanium photodiodes are cheaper and already fully compatible with the semiconductor manufacturing process—but so far, germanium photodiodes have performed poorly in terms of capturing infrared light.
Savin’s team managed to make germanium photodiodes that capture nearly all the infrared light that hits them. The study was published on 1 Jan 2025 in the journal Light: Science & Applications.
“The high performance was made possible by combining several novel approaches: eliminating optical losses using surface nanostructures and minimizing electrical losses in two different ways,” explains Hanchen Liu, the doctoral researcher who built the proof-of-concept device.
The team’s tests showed that their proof-of-concept photodiode outperformed not only existing germanium photodiodes but also commercial indium gallium arsenide photodiodes in responsivity. The new technology captures infrared photons very efficiently and works well across a wide range of wavelengths. The new photodiodes can be readily fabricated by existing manufacturing facilities, and the researchers expect that they can be directly integrated into many technologies.
“The timing couldn’t be better. So many fields nowadays rely on sensing infrared radiation that the technology has become part of our everyday lives,” says Savin.
Savin and the rest of the team are keen to see how their technology will affect existing applications and to discover what new applications become possible with the improved sensitivity.
More information: Hanchen Liu et al, Near-infrared germanium PIN-photodiodes with >1A/W responsivity, Light: Science & Applications (2025). DOI: 10.1038/s41377-024-01670-4
In an era of medical care that is increasingly aiming at more targeted medication therapies, more individual therapies and more effective therapies, doctors and scientists want to be able to introduce molecules to the biological system to undertake specific actions.
Examples are gene therapy and drug delivery, which for widespread use need to be both effective and inexpensive. In service of this goal, a trio of researchers has used machine learning to design a way to remove molecules inside a molecular cage. Their study is published in Physical Review Letters.
The research, whose lead author is Ryan K. Krueger of Harvard University, but to which each co-author contributed equally, uses differentiable molecular dynamics to design complex reactions to direct the system to specific outcomes.
As an example, they undertook the controlled disassembly of colloidal structures—in particular, designing a molecule that could remove a particle surrounded and bound by a complete shell or “cage” of colloidal particles. (Colloids are mixtures of substances where nanoscopic or microscopic insoluble particles are dispersed throughout another substance. Examples are milk, smoke and gelatin.)
Machine learning was used to optimize the design of the shell’s “opener” molecule, which they call the “spider” due to its geometry. As they wrote, “disassembly is central to the dynamic functions of living systems, such as defect repair, self-replication, and catalysis.”
In particular, they designed for the controlled disassembly of icosahedral shells, collection of 12 particles with 30 outside edges connecting the shell particles. This configuration is much like protein capsids that house viruses.
The shell particles are considered “patchy”—their interactions with other shell particles, and the caged particle, have specific values of parameters that dictate the interaction’s directionality and relative strength. Introduced in soft material research 20 years ago, patchiness offers a versatile tunability in the designed interactions, achieving specific behaviors, assisted by the recent development of patchy particle simulations within a differentiable library.
Patchiness may even be varied over the surface of the patchy particles; here the 12 individual shell particles. The goal was to disassemble the shell, which carried an inherent tension between accomplishing the disassembly while maintaining the integrity of the substructure that remained.
The researchers assumed a Morse potential for the potential energy of the interacting shell particles, often used as a model of the interaction between the two atoms in a diatomic molecule, and with the caged molecule.
The Morse potential is simple and has three free parameters that can (and must) be selected for the desired situation. Removing the caged particle requires removing one of the shell particles.
For their analysis, the team assumed the object removing the shell particle was a rigid pyramid-type structure that would fit on top of the 12-sphere cluster. They called this object a “spider.” It consisted of a pentagon-shaped ring of particles that formed the base of the pyramid, with a single “head particle” on top of the pyramid assembly.
In their simulation, the icosahedral shell was given and fixed, with the spider free to land on any shell particle and interact with it.
The patch parameters were tuned so the spider as a whole was neither attracted or repelled by the cluster of shells, but the top-of-the-pyramid particle was attracted to patches on the shell particles by a force that could be varied by distance and strength. The dimensions of the spider and the radii of its head particle and base particles could also be adjusted.
Krueger and his collaborators used molecular dynamics, a standard technique which calculates the motion of each particle by the interaction forces it experiences with the other particles. They wanted to determine which particular parameters of the spider would pluck out the caged molecule from the shell.
Doing this on a computer by brute force—calculating for all possible parameters, particle by particle, until the desired outcome was reached—would take far too much computational power and time. So the group turned to machine learning to minimize a loss function that represented the tension between the disassembly and the remaining substructure integrity.
This process succeeded in producing a rigid spider that could accomplish the removal task. They then allowed the spider to flex, introducing a new free parameter that represented “configurable entropy.”
When it was optimized as well, the energy required to free the caged particle decreased. They found that a spider with asymmetrically flexible base legs required less energy to release the caged particle compared with a spider with the symmetrical, pentagonal base that was first assumed.
They noted their methodology can be broadly applied. “Since we optimize directly with respect to the numerically integrated dynamics, our method is general enough to study a wide range of systems,” they wrote.
“Foremost, it may enable experimental realizations of theoretical models that were otherwise limited by an inability to finely tune interaction energies.”
Students learning quantum mechanics are taught the Schrodinger equation and how to solve it to obtain a wave function. But a crucial step is skipped because it has puzzled scientists since the earliest days—how does the real, classical world emerge from, often, a large number of solutions for the wave functions?
Each of these wave functions has its individual shape and associated energy level, but how does the wave function “collapse” into what we see as the classical world—atoms, cats and the pool noodles floating in the tepid swimming pool of a seedy hotel in Las Vegas hosting a convention of hungover businessmen trying to sell the world a better mousetrap?
At a high level, this is handled by the “Born rule”—the postulate that the probability density for finding an object at a particular location is proportional to the square of the wave function at that position.
Erwin Schrödinger invented his famous feline as a way to amplify the consequences of the collapsing wave function—a simple event, such as a quantum event of the radioactive decay of an atomic nucleus, somehow translates into the macroscopic cat in the box being, either alive or dead. (This mysterious transition, perhaps theoretical only, is called the Heisenberg Cut.)
Traditional quantum mechanics says that at any time the cat becomes either alive or dead when the box is opened and the cat state is “measured.” Before that, the cat is, in a sense, both alive and dead—it exists in a quantum superposition of each state. It is only when the box is opened and its inside is viewed does the wave function of the cat collapse into a definite state of being either alive or dead.
In recent years, physicists have been looking at this process more deeply to understand what’s happening. Modifying the Schrödinger equation has had only limited success.
Other ideas than the Copenhagen interpretation described above, such as De Broglie-Bohm pilot wave theory and the many-worlds interpretation of quantum mechanics, are receiving more attention.
Now a team of quantum theorists from Spain have used numerical simulations to show that, on large scales, features of the classical world can emerge from a wide class of quantum systems. Their work is published in the journal Physical Review X.
“Quantum physics is at odds with our classical experience as far as the behavior of single electrons, atoms or photons is concerned,” lead author Philipp Strasberg of the Autonomous University of Barcelona told Phys.org.
“However, if one zooms out, and considers coarse quantities that we humans can perceive (for example, the temperature of our morning coffee or the position of a stone), our results indicate that quantum interference effects, which are responsible for weird quantum behavior, vanish.”
Their finding suggests that the classical world we see can emerge from the many-worlds picture of quantum mechanics, where many universes exist at the same point in spacetime and where almost a potentially huge number of worlds branch off from ours every time a measurement is made.
As a rough analogy, imagine a shower bag filled with water. Poke holes in the bag and water—which inside the bag is a large collection of frequently colliding molecules moving in random directions—will stream out in mostly smooth flows. This is akin to how the complicated jumble of a quantum system nonetheless appears in the classical world as something we recognize and feel familiar with.
But a technical problem remained with the many-worlds portrait: how do we reconcile the many-universes with the classical experience we have within our one universe? After all, we never see cats in a superposition of alive and dead. A priori, how can we speak of other universes or worlds or branches in any meaningful sense?
In their paper, Strasberg and co-authors write “Speaking of different worlds or histories becomes meaningful if we can reason about their past, present, and future in classical terms.”
The co-authors attempted to solve this problem in a new way. While previous work has brought in the idea of quantum decoherence—where the objects we see arise out of the many superpositions of a quantum system when it interacts with their environment. But this approach has a fine-tuning problem—it only works for specific types of interactions and types of initial wave functions.
By contrast, the group showed that a stable, self-consistent set of features emerges from the range of many possible evolutions of a wave function (with many energy levels) at observable, non-microscopic scales. This solution does not have a fine-tuning problem, works for a wide choice of initial conditions and the details of the interactions between energy levels.
“In particular,” Strasberg told Phys.org, “we provide clear evidence that this vanishing [of quantum interference effects] happens extremely fast—to be precise: exponentially fast—with growing system size. That is, even a few atoms or photons can behave classically. Furthermore, it is a ubiquitous and generic phenomenon that does not require any fine tuning: the emergence of a classical world is inevitable.”
The group numerically simulated quantum evolution for up to five time-steps and up to 50,000 energy levels for nontrivial quantum systems. Though that evolution is still small compared to what will be needed to simulate everyday classical phenomena, it’s much larger than any previous work.
They considered a broad range of choices of the initial wave function and of coupling strengths and found approximately the same large-scale structure of stable branches exist—the emergence of a stable and slowly evolving macroscopic structure.
“Remarkably, we also explicitly demonstrate that interesting classical worlds can emerge from a quantum system that is overall in thermodynamic equilibrium. Even though it is very unlikely that this is the case in our universe, it nevertheless demonstrates that order, structure and an arrow of time can emerge on single branches of a quantum Multiverse, which overall looks chaotic, unstructured and time-symmetric.”
Relating their work to statistical mechanics, where macroscopic features like temperature and pressure emerge from a melange of randomly moving particles, the group found that some branches lead to worlds where entropy increases and others to worlds where entropy decreases. Such worlds would have opposite entropic arrows of time.
More information: Philipp Strasberg et al, First Principles Numerical Demonstration of Emergent Decoherent Histories, Physical Review X (2024). DOI: 10.1103/PhysRevX.14.041027
In bird colonies, schools of fish and cycling pelotons, significant interactions occur between individuals through the surrounding fluid. These interactions are well understood in fluids such as air and water, but what happens when objects move through something like sand? It turns out that similar interactions occur in granular materials—things like soil or sand—and they play a crucial role in everyday contexts. Think of plows cutting through farmland, animals burrowing underground, roots pushing through soil, or even robots exploring the surface of Mars.
Recently, we came across a fascinating discovery: When two objects—what we call “intruders”—move side by side through granular materials, they can actually help each other by reducing the resistance they face. This cooperative effect was uncovered by a team of researchers from the School of Mechanical Engineering at the University of Campinas (UNICAMP) in Brazil, and the FAST laboratory, CNRS, Université Paris-Saclay in France.
To investigate this, we set up an experiment using spherical objects immersed in glass beads to replicate a granular medium. The goal was to pull these objects at a constant speed and measure the drag force they experienced as they moved through the grains. While previous studies had looked at the lateral forces between objects, our team wondered whether moving together might also reduce the drag force.
Some intriguing numerical simulations by two of our researchers at UNICAMP, D. D. Carvalho and E. M. Franklin, published in the Physics of Fluids in 2022, suggested that it could, but we wanted to test this in the real world.
What we found was exciting: When the two intruders were close together, the drag on each of them dropped significantly—by as much as 30% compared to when they were farther apart. And the deeper they were buried in the material, the more pronounced this effect became. The explanation? When two objects move side by side, the motion of one disrupts the force chains between the grains around the other. This break in the grain contact reduces the overall resistance each object encounters.
Beyond just observing this effect, we also developed a semi-empirical model to describe it. The model is based on the idea that interactions between closely spaced objects disrupt these granular force chains, making it easier for them to move. This study, now published in Physical Review Fluids, highlights a previously under-explored aspect of granular dynamics: the cooperative motion of multiple objects.
As research into these dynamics advances, it may lead to new technologies and techniques for navigating granular materials—on Earth and beyond—potentially enabling more efficient solutions for various industries and scientific endeavors.
This story is part of Science X Dialog, where researchers can report findings from their published research articles. Visit this page for information about Science X Dialog and how to participate.
More information: D. D. Carvalho et al, Drag reduction during the side-by-side motion of a pair of intruders in a granular medium, Physical Review Fluids (2024). DOI: 10.1103/PhysRevFluids.9.114303
Super-resolution (SR) technology plays a pivotal role in enhancing the quality of images. SR reconstruction aims to generate high-resolution images from low-resolution ones. Traditional methods often result in blurred or distorted images. Advanced techniques such as sparse representation and deep learning-based methods have shown promising results but still face limitations in terms of noise robustness and computational complexity.
In a recent study published in Sensors, researchers from the Changchun Institute of Optics, Fine Mechanics and Physics of the Chinese Academy of Sciences proposed innovative solutions that integrate chaotic mapping into SR image reconstruction process, significantly enhancing the image quality across various fields.
Researchers innovatively introduced circle chaotic mapping into the dictionary sequence solving process of the K-singular value decomposition (K-SVD) dictionary update algorithm. This integration facilitated balanced traversal and simplified the search for global optimal solutions, thereby enhancing the noise robustness of the SR reconstruction.
In addition, researchers adopted the orthogonal matching pursuit (OMP) greedy algorithm, which converges faster than the L1-norm convex optimization algorithm, to complement K-SVD, and constructed a high-resolution image using the mapping relationship generated by the algorithm.
They trained and learned high- and low-resolution dictionaries from a large number of images similar to the target. Through the joint dictionary training method, the high- and low-resolution image blocks under the dictionary had the same sparse representation, reducing the complexity of the SR reconstruction process.
The proposed method, named the Chaotic Mapping-based Sparse Representation (CMOSR), significantly improves the image quality and authenticity. It could effectively reconstruct high-resolution images with high spatial resolution, good clarity, and rich texture details. Compared to traditional SR algorithms, the CMOSR exhibits better noise robustness and computational efficiency. It does not generate unexpected details when processing images and is more inclusive of image sizes.
More information: Hailin Fang et al, Super-Resolution Reconstruction of Remote Sensing Images Using Chaotic Mapping to Optimize Sparse Representation, Sensors (2024). DOI: 10.3390/s24217030
