In Texas, US, electricity demand is expected to grow because of rapid population growth, increased adoption of electric vehicles, expansion of AI data centres, and other societal and industrial developments. Most of Texas’ electricity system is managed by the Electric Reliability Council of Texas (ERCOT), which operates a grid that is largely independent from the rest of the United States. Combined with the state’s rapidly changing demand patterns, this makes Texas an important test case for exploring future energy scenarios.

In this work, the researchers developed a framework to model how different sources of electricity demand growth could affect the Texas grid through to 2050. They separately examined the impacts of population growth, electrified heating, electric vehicles, large industrial loads such as data centres, and electrification of oil and gas operations.

The study found that electric vehicles have the greatest impact on peak electricity demand because charging tends to occur at similar times of day, creating sharp increases in demand. In contrast, large industrial loads, including data centres, cryptocurrency mining, hydrogen production and manufacturing facilities, have the greatest impact on overall electricity consumption because they often operate continuously. Electrification of heating had a smaller net effect because heat pumps increase winter electricity demand while simultaneously reducing summer electricity demand through more efficient cooling.

The findings suggest that different types of demand growth require different grid solutions. Battery storage is particularly effective for managing the sharp demand peaks associated with electric vehicle charging, whereas growth from data centres and other large industrial loads is most economically met through additional natural gas, wind and solar generation. The study also showed that uncertainty in future demand could lead to dramatically different grid expansion requirements by 2050.

A key impact of this work is demonstrating that the source of future electricity demand can be just as important as its size, since different demand drivers require different investments in generation, storage and grid infrastructure. The framework developed in this study could help planners, investors and policymakers make more informed decisions about future electricity systems.

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How different power plant types contribute to electric grid reliability, resilience, and vulnerability: a comparative analytical framework by K Ramirez-Meyers et al. (2021)

The post Forecasting future demand on the Texas grid appeared first on Physics World.

N = 4 super Yang-Mills is a highly symmetric quantum field theory that physicists use as a model system for exploring ideas in quantum gravity and string theory. The researchers study interactions between small and large excitations of the theory. The small, or light, excitations are super gravitons, which can be thought of as particle-like ripples of spacetime. The large, or heavy, excitations are giant gravitons: extended brane-like objects that arise when a graviton carries a very large amount of angular momentum. The goal is to understand how the light excitations behave in the presence of these heavy objects.

To study this, the authors analyse a four-point correlation function containing two light (L) and two heavy (H) operators, known as an LLHH correlator. These correlators are notoriously difficult to calculate directly because doing so requires detailed knowledge of how the supergravity fields couple to the giant graviton and its fluctuations. The key idea of the paper is to treat the pair of heavy operators as a zero-dimensional defect. This reformulates the four-point function as a two-point function of light probes in the presence of the defect, allowing bootstrap techniques to be applied.

Using this defect framework together with bootstrap techniques, the authors compute all strong-coupling four-point functions involving two maximal giant gravitons and two supergravitons of arbitrary dimension. They also uncover a partially broken higher-dimensional hidden conformal symmetry that organizes these correlators. Finally, they determine the leading interaction-induced energy shifts for a complete class of double-particle states associated with the defect and argue that the defect picture provides the natural description of heavy-light correlators more generally. This establishes a powerful new framework for studying giant gravitons, non-planar effects, and aspects of quantum gravity and strongly coupled quantum field theories.

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Bootstrap and amplitudes: a hike in the landscape of quantum field theory by Henriette Elvang (2021)

The post A new way to study giant gravitons appeared first on Physics World.

Using solar and wind power for clean energy generation relies on the weather, which can be unpredictable. In addition to seasonal changes and daily variability, energy planners must also account for rare and extreme weather events. Traditional planning methods often fail to capture these events accurately, resulting in systems that are either overly expensive or insufficiently reliable.

In this work, the researchers aimed to develop a more effective way of planning energy systems that can cope with real climate variability. They built an artificial intelligence model trained on 30 years of wind and solar data from Pingtan in China. The model generated thousands of realistic weather scenarios, including both typical conditions and extreme events.

The researchers then used these scenarios to determine the most cost-effective and reliable combination of energy technologies. Their Integrated Energy System included solar panels, wind turbines, batteries, hydrogen production and storage, and a connection to the national electricity grid. The optimal system configuration was identified by testing its performance across all of the AI-generated scenarios.

The optimisation showed that the most effective renewable energy system combines several technologies working together. Wind and solar provide the main source of electricity, batteries manage short-term fluctuations in supply and demand, hydrogen storage provides backup during extended periods of low renewable generation, and the grid acts as an additional safety net.

The key finding is that batteries and hydrogen have complementary roles: batteries are best for balancing daily variations, while hydrogen is better suited to storing energy over weeks or months. Together, they help create a renewable energy system that is both reliable and cost-effective.

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Energy policy and public opinion: patterns, trends and future directions by Parrish Bergquist, David M Konisky and John Kotcher (2020)

The post Using AI to plan for climate variability in renewable energy systems appeared first on Physics World.

When light is absorbed by a semiconductor, it first creates an electron-hole pair before free charges can be generated. The electron and hole can remain bound together in a quasiparticle known as an exciton. This intermediate stage is important because excitons determine how light is absorbed and emitted, and how efficiently a material can convert light into electrical current.

Monolayer tungsten diselenide (WSe₂) is a material that researchers are investigating for use in photodetectors, optical computing and quantum technologies. It is an atom-thick semiconductor that interacts very strongly with light. The excitons formed in WSe₂ are unusually stable, allowing quantum effects to be observed particularly strongly. WSe₂ therefore provides a model system for learning how to control light-generated excitons, knowledge that could eventually lead to more efficient solar cells, photodetectors and low-energy photonic technologies.

A key question in this area of research is what causes the WSe₂ exciton energy to shift to a higher energy (blue shift), a lower energy (red shift) or split into two levels. Two leading explanations are the Optical Stark Effect, in which the laser’s electric field directly modifies the exciton energy, and exciton-exciton interactions, in which excitons alter one another’s energies through many-body interactions.

In this work, the researchers used helicity-resolved transient absorption spectroscopy to show that, while the laser pulse is present, the Optical Stark Effect dominates, causing a blue shift, splitting or a red shift depending on the laser detuning. After the pulse ends, exciton-exciton interactions become dominant, producing a blue shift. By varying the laser detuning and tracking the exciton response on femtosecond timescales, the researchers were able to separate the coherent Optical Stark Effect from the later incoherent exciton-exciton interaction. The work provides a clearer picture of how light and excitons interact on ultrafast timescales, which is important for the design of next-generation optoelectronic devices.

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Intercalation in two-dimensional transition metal chalcogenides: interlayer engineering and applications by Dibyendu Ghosh, Pooja Devi and Praveen Kumar (2022)

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In an antiferromagnet, neighbouring spins prefer to align in opposite directions to minimise their energy. In a geometrically frustrated magnet, this is not always possible; for example, three spins on a triangle cannot all simultaneously be antiparallel to one another. The magnet PdCrO₂ contains chromium (Cr) atoms on a triangular lattice, so that the magnetic moments on the Cr atoms cannot point antiparallel to each other. This compound also contains sheets of palladium (Pd) atoms that have very high electrical conductivity. PdCrO₂ is therefore a system in which the interaction between magnetism and electronic structure can be studied in a precise way.

Previous studies have shown that the Cr-Cr exchange interaction is highly sensitive to inter-atomic spacing. In consequence, the magnetic ordering wavevector shifts rapidly as uniaxial stress is applied to PdCrO₂, distorting the triangular lattice. In this work, the researchers have taken a step further by applying enough stress to qualitatively change the magnetic structure. They use a combination of X-ray diffraction and measurement of the stress-strain relationship to probe the effects on the elastic properties, and elastic neutron scattering to see how the magnetism changes. As the magnetic structure changes, the Young’s modulus changes by around 100 GPa, showing that magnetic interactions have a large effect on the crystal’s mechanical stiffness.

As stress is initially applied to PdCrO₂, the magnetic wavevector changes rapidly, reflecting the sensitivity of the Cr-Cr exchange interaction to strain. The main finding of this work is that, under a uniaxial stress of about 0.6 GPa, the magnetism undergoes a first-order transition into a state where the magnetic wavevector is rigid, that is, no longer responds to continued change in lattice strain. The rigidity of this stress-induced magnetic structure is reflected in the elastic properties: the Young’s modulus almost doubles, and the Poisson ratio falls from about 0.7, an exceptionally high value, to about 0.3, which is more typical. This rigidity may be a consequence of nesting: the wavevector of this stress-induced magnetic phase nests the Fermi surface of the Pd sheets. PdCrO₂ may therefore provide a system in which the thermodynamics of nesting can be studied with quantitative precision. More broadly, the work demonstrates that uniaxial stress is a powerful tool for tuning frustrated magnetism and accessing new magnetic states.

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Unusual ordered phases of highly frustrated magnets: a review by Oleg A Starykh (2015)

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A critical point is where a material changes phase. For example, water turns to ice below 0°C (273 K) at standard pressure. A quantum critical point occurs at absolute zero (0 K) and, instead of being driven by temperature, is caused by changing another property such as a magnetic field, pressure, or interaction strength. One example is the transition between an ordered magnetic state and a disordered quantum state.

Entanglement entropy is a measure of how much quantum information two parts of a system share. If a system is split into two regions, a low entanglement entropy means that knowing about one half tells you little about the other half, while a high entanglement entropy means that the two halves are strongly connected through quantum mechanics. Entanglement entropy provides important information about quantum phases of matter, quantum critical points, and universal properties of quantum systems. However, calculating entanglement entropy accurately at quantum critical points in two-dimensional quantum systems is very difficult. These systems are commonly described as (2+1)-dimensional because their critical theories involve two spatial dimensions and one time dimension.

In this work, the researchers developed a novel Quantum Monte Carlo algorithm to calculate entanglement entropy more accurately. Quantum Monte Carlo methods use random sampling to study quantum systems that are too complex to solve exactly. The researchers started with a standard quantum magnet model, the transverse-field Ising model, and added extra interactions that allowed them to study different types of phase transitions, including ordinary Ising critical points and a tricritical point that, in (2+1) dimensions, is described by a Gaussian free theory.

The researchers compared the second Rényi entanglement entropies of two specially chosen regions with the same boundary length. This directly cancelled the dominant area-law contribution, allowing the much smaller universal corner term to become the leading signal. They then used the same approach to obtain a precise value for the Ising critical point. The results showed that the Ising and tricritical/Gaussian critical points have different universal entanglement fingerprints, demonstrating that entanglement can distinguish between different types of quantum critical behaviour. More broadly, the work provides a powerful new method for studying entanglement in strongly interacting quantum systems and for testing theoretical predictions in two-dimensional quantum materials.

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Dynamical quantum phase transitions: a review by Markus Heyl (2018)

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Quantum dot LEDs (QLEDs) are of interest because they emit bright light with highly pure colours, making them attractive for display and lighting applications. They also have the potential to be manufactured at lower cost using solution-processing techniques. However, producing high-performance QLEDs remains challenging because the quantum dots must be uniformly distributed within a densely packed film. Poor film uniformity can lead to lower efficiency and faster device degradation.

In this study, machine learning was used to identify solvent conditions that produce the most uniform quantum dot films. Several solvents were characterised using five key solvent parameters that influence how quantum dots arrange themselves as the solvent evaporates. Film uniformity was assessed using atomic force microscopy, which provides detailed information about surface morphology and roughness.

Three machine learning models were trained to predict film uniformity from the solvent properties. Support Vector Regression was found to provide the most accurate predictions and was subsequently used to identify an optimal mixed-solvent formulation. Grazing-Incidence Small-Angle X-ray Scattering confirmed that the resulting films exhibited more homogeneous quantum dot packing.

The optimised solvent formulation produced QLEDs with higher efficiency and longer operational lifetimes than devices fabricated using single solvents. Overall, the study demonstrates that film homogeneity is a critical factor in QLED performance and highlights the potential of machine learning as a powerful tool for optimising solution-processed optoelectronic devices.

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Materials, photophysics and device engineering of perovskite light-emitting diodes by Ziming Chen et al. (2021)

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Wireless power transfer systems are used to transfer electricity without a physical cable, for example in wireless electric vehicle charging, medical implants and consumer electronics such as smartwatches. In real systems, poor alignment and variable distances can reduce efficiency, causing some of the transmitted energy to be reflected back towards its source rather than being absorbed by the receiver.

Wireless power transfer research tends to focus on engineering solutions such as improved coil designs, impedance matching and frequency tuning. However, in this work, the researchers investigate a more fundamental question: how can the physics of time-domain wave interference be used to control energy flow within resonant systems?

A phenomenon known as Coherent Perfect Absorption occurs when a system completely absorbs incoming energy with negligible reflection. However, this requires specific conditions and a system that is perfectly matched, operating at a point of equilibrium known as the steady state. Here, the researchers report a new effect called Transient Coherent Perfect Absorption. Rather than relying on a steady state, TCPA operates during the brief transition period immediately after a system is switched on.

During this transient period, a steady wave produced by the source coexists with a temporary, decaying wave created by energy stored within the system. These waves interfere with one another and both contribute to the reflected signal. When they are exactly out of phase, the reflected signals cancel out. As a result, a system that would normally reflect a significant amount of incoming energy can briefly behave as though it were perfectly matched, absorbing almost all of the incoming energy.

While this is fundamentally a physics study of wave behaviour and interference, it introduces a new way to suppress energy reflections in resonant systems. In the future, the approach could help improve the efficiency of wireless power transfer and other energy-delivery technologies by reducing losses under imperfect operating conditions.

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The rise of electric vehicles—2020 status and future expectations by Matteo Muratori et al. (2021)

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A system will generally move towards equilibrium when a change is made to it in a process called relaxation. To predict the state of the system after some period of time, we only need to know its current state and the external conditions. However, some polymers have memory behaviours where the evolution of the system is not only dependent on the current state, but also on previous states. This is known as the Kovacs effect.

In this work, the researchers investigated the Kovacs effect in a system consisting of suspensions of rod-like nanoparticles. By applying an electric field to these nanoparticles they align, therefore the particles change the liquid’s optical anisotropy, which can be measured as birefringence, which is how the liquid changes light polarization. More birefringence means the particles are more aligned. Importantly, there are faster- and slower-relaxing nanoparticles because of their polydispersity and different sizes, meaning they align at different rates. By changing the strength of the electric field, the faster and slower nanoparticles become out of sync with each other. The future behaviour will then depend on the system’s previous history that is overall hidden by the global birefringence measurement; this is the Kovacs effect.

The researchers modelled the orientational dynamics using the Smoluchowski equation, which describes rotational diffusion of particles. They found that relaxation is not determined by a single timescale or relaxation mode. When some of the modes are fast and some are slow, memory effects will occur. The Kovacs effect is important because it shows that a simple strategy of applying a strong electric field before switching to the target field does not necessarily speed up relaxation. Due to memory effects arising from particles relaxing at different rates, the system overshoots the target state, reducing the expected gain in speed.

To overcome this, the researchers designed improved two- and three-step protocols that suppress the slowest relaxation modes rather than simply matching the average birefringence. These protocols reduce the memory effect, produce a smoother approach to equilibrium, and achieve faster relaxation, although the benefit of additional switching steps becomes progressively smaller. This research improves our understanding of memory effects in complex systems and provides a practical strategy for controlling nanoparticle dynamics more quickly and efficiently.

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Scientific developments of liquid crystal-based optical memory: a review by Jai Prakash, Achu Chandran and Ashok M Biradar (2016)

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Na₂Mn₃O₇ is a magnetic material that behaves unusually and therefore can provide insight into how magnetism emerges in complex materials. Magnetic materials contain entities known as spins, which can be thought of as tiny magnets with north and south poles. These spins interact with neighbouring spins, and as a material is cooled, they often align into an ordered pattern that extends across the entire material, known as long-range magnetic order.

However, Na₂Mn₃O₇, which contains magnetic Mn⁴⁺ ions, behaves differently. It has strong magnetic interactions, very little disorder, and a two-dimensional arrangement of magnetic ions. Under these conditions, a material with relatively large spins would normally be expected to develop long-range magnetic order at low temperatures. Surprisingly, no magnetic ordering is observed, even at very low temperatures.

The material also exhibits two distinct magnetic crossovers. Around 110-120 K, the magnetic susceptibility reaches a broad maximum, indicating that strong magnetic correlations are developing. Around 60-70 K, a feature appears in the specific heat, showing that the system is undergoing a further internal reorganisation. The presence of two separate temperature scales suggests that the magnetism develops in two stages.

Using theoretical and computational techniques, the researchers showed that this unusual behaviour originates from the crystal structure of Na₂Mn₃O₇. The crystal structure divides the magnetic lattice into strongly connected hexagonal clusters of six Mn ions. These clusters behave like magnetic ‘molecules’, with the spins inside each hexagon becoming strongly correlated. Interactions between different hexagons are much weaker and frustrated, preventing the formation of long-range magnetic order across the material.

This research is important because it demonstrates that even large-spin magnetic materials can exhibit quantum-disordered behaviour. More broadly, it shows that crystal structure can be used as a tool to engineer new magnetic states by organising spins into strongly correlated clusters that suppress conventional magnetic ordering.

“What is striking in Na₂Mn₃O₇ is that the crystal structure does more than slightly distort the magnetic lattice. It reorganises the spins into strongly correlated hexagons that behave as emergent magnetic molecules, while frustration and quantum fluctuations prevent these units from ordering collectively.“ – Yasir Iqbal, Indian Institute of Technology Madras

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Frustrated magnets in high magnetic fields—selected examples by J Wosnitza, S A Zvyagin and S Zherlitsyn (2016)

The post Magnetic molecules explain a puzzling material appeared first on Physics World.

Graphene, an atomically thin material of a single layer of carbon atoms, is a fascinating material for high-speed electronics, spintronic devices, and neuromorphic components due to its exceptional tunable electrical conductivity. One area of research focuses on understanding how electrons flow through graphene to enable the design of advanced devices. For applications, ultrathin metal-oxide layers (e.g., aluminum oxide and titanium oxide) are routinely deposited on graphene, which deliberately alter electron flow and provide greater control over its properties, in devices ranging from transistors to spin valves. However, although the effects of these coatings are known, researchers have not previously been able to directly observe what is happening inside the device. Instead, their understanding has relied primarily on theoretical models, numerical simulations, and indirect electrical measurements.

In this work, the researchers used X-ray Photoelectron Spectroscopy (XPS) to map electrical potentials across devices while they were operating. They examined the electrical landscape, identifying regions with steep slopes corresponding to strong electric fields and flatter regions corresponding to weaker electric fields, to determine how oxide coatings modify this landscape. They found that metal oxides flatten the electrical landscape through a process known as p-type charge-transfer doping. This occurs when the oxide removes some electrons from the graphene, altering its local electrostatic environment and reducing the voltage gradient. As a result, local electric fields become weaker. The oxide layers suppress the electric fields, reducing them by more than 50%, a significant effect for device operation. A key advance of this research is the visualisation of this effect at very high spatial resolution, whereas previously it could only be inferred from electrical resistance measurements. In addition, by making a direct comparison with electrical measurements, the researchers confirmed the observed effects are due solely to electrostatics and charge redistribution.

Modern electronic devices increasingly depend on controlling electricity at very small scales. By locally adjusting electric fields using oxide coatings, researchers gain an additional design tool that enables different regions of a graphene circuit to be tuned independently. This is particularly important for spintronic devices, which are highly sensitive to local electric fields, and neuromorphic computing hardware, which requires precise control of local electrical properties.

This work represents a breakthrough in the ability to directly map charge distributions and electric fields inside operating 2D-material devices, enabling improved device design.

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Phonons and thermal transport in graphene and graphene-based materials byDenis L Nika and Alexander A Balandin (2017)

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Many catalysts are made using core-shell nanoparticles, in which the core is a structurally important or inexpensive material and the thin shell surrounding it is an expensive metal such as platinum. Since catalytic reactions occur on the surface, this helps to reduce the amount of platinum required and therefore lowers costs. Crystal structures are described based on the smallest repeating unit of the crystal (the unit cell) using the Bravais lattice system. Three important crystal structures are face-centred cubic (fcc, atoms at the corners and faces of a cube), body-centred cubic (bcc, atoms at the corners and centre of a cube), and hexagonal close-packed (hcp, a hexagonal arrangement of atoms).

Different materials have different crystal structures; for example, platinum is fcc, many alloys are bcc, and magnesium and zinc are hcp. In core–shell nanoparticles, differences between the crystal structures of the core and shell mean that the atoms do not line up perfectly. This mismatch creates strain, which can significantly affect catalytic performance. In this work, the researchers explored how a platinum shell grows on a different crystal structure in a process known as heteroepitaxy.

They studied platinum shells (Pt, fcc) grown on cores made of ruthenium (Ru, hcp), palladium–copper (PdCu, bcc), and specially synthesised ruthenium with an fcc structure. It was found that each system accommodates the atomic mismatch differently. In hcp/fcc particles, some areas lined up coherently while others contained defects called dislocations, which formed networks in particles smaller than 10 nm. In bcc/fcc particles, both the core and shell stretched or compressed to fit together. In fcc/fcc particles, the matching crystal structures aligned more readily, but twin defects formed in which one region mirrored another.

Overall, this study shows that the way a Pt shell grows on a nanoparticle core is determined by a balance between the energies of the interface, shell, and core, with the system naturally adopting the lowest-energy configuration. These findings could help scientists achieve atomic-precision interfacial engineering, controlling the catalytic, mechanical, and electrical properties of core-shell nanoparticles.

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PLP-Logo-2.png, find out more. Single metal nanoparticles: optical detection, spectroscopy and applications by P Zijlstra and M Orrit (2011)

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Intrinsically disordered proteins (IDPs) do not form stable 3D structures. Instead, they remain flexible, adopt many conformations, and can interact with multiple molecules. Although proteins were once thought to require a fixed structure to function, many IDPs play essential cellular roles. Some IDPs can assemble into biomolecular condensates, membrane-less compartments that help organise processes such as gene expression and stress responses.

Many IDPs contain prion-like low complexity domains (PLCDs), which have defined sequence features known as molecular grammas. These IDPs are multivalent and can form hierarchies of interactions with one another. These interactions promote clustering and, under the right conditions, phase separation into a protein-rich condensate and a surrounding dilute phase. Dysregulation of condensates has been linked to disease.

In this study, researchers used computer simulations to investigate the PLCD known as A1-LCD. Their aims were to accurately identify the critical point where phase separation ceases, map the full phase diagram, and assess methods used to estimate the theta temperature (Tθ), a measure related to solvent quality and protein interactions.

The simulations revealed three distinct phase-separation regimes: a dilute phase containing mostly isolated proteins, an intermediate regime where clusters form before large condensates appear, and a regime near the critical point characterised by system-spanning networks. The study also showed that condensates behave as percolated networks of interconnected proteins. Importantly, the authors found that commonly used methods for estimating theta temperature may be inaccurate for these systems.

Overall, this work provides new insights into the phase behaviour of condensate-forming proteins and highlights the need for more reliable approaches to assessing solvent quality. These findings could improve future studies of biomolecular condensates and their roles in health and disease.

“Comparative assessments of driving forces for phase separation requires knowledge of IDP-specific critical points. Our application of rigorous finite size scaling methods pioneered by Kurt Binder helped us delineate the critical and mean field regimes and demonstrate how the critical point can be converged upon for realistic approximations of IDPs. This revealed several surprises regarding the complex nature of the coexisting dilute phase. Additionally, we found that calculation of the two-body interaction coefficient provides a computationally tractable approach to compute comparative, sequence-encoded driving forces for phase separation of IDPs. This is directly relevant to inferring how changes to molecular grammars influence phase behaviors driven by homotypic associations.” – Rohit V. Pappu, Washington University in St. Louis

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Biomolecular dynamics: order–disorder transitions and energy landscapes by Paul C Whitford, Karissa Y Sanbonmatsu and José N Onuchic (2012)

The post The physics of protein condensates appeared first on Physics World.

According to band theory, materials with partially filled energy bands and unpaired electrons should conduct electricity, but in Mott insulators this rule is broken. In these materials, the repulsion between electrons is so strong that they cannot move freely and become localized. When sufficient energy is supplied (for example by light), electrons can form a doublon (two electrons on one site) and a hole (an empty site). These excitations can move, enabling electrical conductivity.

A key question for Mott insulators is how these charge carriers are created and what controls their production. Traditionally, two regimes were known: multiphoton absorption, where several photons combine their energy, and tunnelling, where a strong electric field drives electrons across the gap.

In this work, the authors show that there are actually four regimes: multiphoton, tunnelling, a cooperative regime where the two processes work together, and an incoherent regime where weak fields and scattering dominate. They also show that the energy gap (the cost to create a doubloon-hole pair) is not fixed, but changes during the process because the generated carriers modify the system. This creates a feedback loop, making the dynamics nonlinear and time-dependent. As a result, carrier production can either slow down or suddenly speed up, even making the system more efficient without changing the driving field.

The behaviour depends on field strength, frequency, dissipation, and time, and each mechanism can be identified by its distinct momentum pattern. By tuning laser parameters, the system’s properties can be controlled in real time. Overall, this work shows that light can be used to dynamically reshape and control strongly interacting materials, opening possibilities for ultrafast, light-driven technologies.

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PLP-Logo-2.png, find out more. Quantum frustration in organic Mott insulators: from spin liquids to unconventional superconductors by B J Powell and Ross H McKenzie (2011)

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Quantum mechanics has two seemingly competing rules. Firstly, a system evolving without measurement follows a continuous, deterministic evolution governed by the Schrödinger equation, with dynamics determined by a Hamiltonian. Secondly, when a measurement occurs, the wavefunction collapses, producing a sudden, discontinuous change that is not derived from a Hamiltonian. Several approaches attempt to reconcile these behaviours, including the Copenhagen interpretation (which does not explain the mechanism of collapse), decoherence theory (which does not provide a single definite outcome), stochastic collapse models, and continuous measurement theory.

In this work, measurement is not treated as fundamentally different. Instead, it is described using stochastic (random) Hamiltonians that generate continuous evolution of the quantum state. In this picture, collapse emerges from noisy dynamics. The authors show that these dynamics can be understood as double-bracket gradient flows, where the system is driven to align with a measured observable, steadily reducing uncertainty until it reaches a definite outcome. Thus, wavefunction collapse can be viewed as coarse-grained continuous dynamics that minimise the variance of the observable. By interpreting this as a gradient flow, the same mechanism can be exploited using feedback to drive a system into desired states, including entangled ones.

This approach provides a continuous and physically interpretable picture of wavefunction collapse. Compared to decoherence theory, it explains the emergence of a single outcome but does not specify when measurement dynamics begin. More broadly, it replaces the notion of collapse with a dynamical process, making the theory more internally consistent, while also offering practical tools for controlling quantum systems, which is important for quantum computing and experiments.

“These geometric connections between Hamiltonian dynamics and quantum measurements open the door to exciting new approaches to quantum algorithm design.” – Aaron Villanueva, Radboud University

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Genuine quantum correlations in quantum many-body systems: a review of recent progress by Gabriele De Chiara and Anna Sanpera (2018)

The post Making sense of quantum wavefunction collapse appeared first on Physics World.

Percolation studies how small connections can group together to form a larger connected system, known as a cluster. In a grid where sites can be turned on or off, turning on more sites leads to the formation of clusters, and at a certain point a giant cluster spanning the system emerges. This behaviour is relevant to many real-world systems where local interactions lead to global effects, such as power grids, disease spreading, forest fires, and brain activity. While percolation is often studied using theoretical models or simulations, real systems are more complex. Nonlinear effects, noise, imperfect connections, and time-dependent dynamics can all shift the tipping point and change how clusters form.

In this work, the researchers built a physical system using a 2D array of 100 coupled lasers to study percolation experimentally. Each laser acts as a site that can be turned on and interacts with its neighbours, making the system a controllable realisation of a percolation grid. They found that when a large connected cluster forms, the lasers also begin to phase-lock, meaning that connectivity and synchronisation emerge together. Normally, these effects are studied separately.

At high pump power (the energy supplied to the lasers), the system behaves like an ideal percolation model, with a clear critical threshold and a smooth (second-order) transition, consistent with theory. However, at low pump power, nonlinear effects become important. The lasers compete more strongly, meaning more active sites are needed to form a spanning cluster, and weaker clusters are suppressed. This shifts the percolation threshold and alters the cluster size distribution, contrary to the usual expectation that nonlinear effects are stronger at higher power.

To explain this, the researchers introduced a simple model where a site only remains active if it has enough active neighbours, effectively modifying the connectivity rules. This reproduces the observed behaviour and shows how nonlinear effects can reshape percolation. Overall, the work demonstrates that real physical systems can significantly modify ideal percolation behaviour, and provides a controllable platform for studying how connectivity, competition, and synchronisation interact in complex networks.

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Percolation theory by J W Essam (2018)

The post Experimental realisation of percolation with coupled lasers appeared first on Physics World.

When electrons are placed in a strong magnetic field and cooled to very low temperatures, they stop behaving independently and instead act as a collective fluid. This is known as a fractional quantum Hall (FQH) system. In this regime, the electrons lose their individual behaviour and act as one correlated system. The strong interactions between them produce quasiparticles called anyons. These are not real particles but effective ones that emerge from the collective behaviour, and they have unusual properties that do not occur in ordinary systems.

Anyons exhibit braiding behaviour: when they are moved around each other, the system remembers the path taken. In Abelian braiding, this only adds a simple phase, so the quantum state is effectively unchanged. In contrast, non-Abelian braiding transforms the quantum state into a different one, which can be measured and used to encode information.

To study these systems, physicists must compute measurable properties from complicated wave functions, which is computationally challenging, especially for large systems. The traditional method, Metropolis Monte Carlo, is slow and struggles with large numbers of electrons, limiting system size, accuracy, and the ability to test advanced theories.

In this work, the researchers developed a new simulation method, Hybrid Monte Carlo (HMC), for FQH wave functions. It includes global updates (updating many electrons at once) and double stereographic projection to more accurately sample particle positions, making it much faster and more efficient than the Metropolis method.

Using this improved method, they accurately computed edge properties that reveal the system’s topology, as well as high-quality braiding matrices of non-Abelian quasiholes, which are crucial because such braiding underpins topological quantum computing. Overall, this work enables fast, large-scale simulations, allowing more accurate study of exotic quantum states and their anyon behaviour, advancing both fundamental physics and topological quantum computing.

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A review of the quantum Hall effects in MgZnO/ZnO heterostructures by Joseph Falson and Masashi Kawasaki (2018)

The post Advanced simulations of exotic quantum matter appeared first on Physics World.

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Physical properties and atomic arrangements in crystals by W A Wooster (1953)

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Electricity grids need to be carefully balanced, with too low levels of electricity leading to blackouts and too high levels causing damage to grids. Due to the intermittent nature of renewable energy sources such as solar and wind, it is possible for specific regions (e.g. the north of Germany) to have an excess of electricity, while the power lines do not have enough capacity to transport it to higher demand regions such as cities and industrial areas. This is known as a grid bottleneck. To keep the grid running properly, operators redispatch electricity by reducing renewable feed-in in oversupplied areas and increasing generation elsewhere. However, this leads to renewable energy being wasted, and because producers are still compensated and generation increase on demand is costly, it is financially inefficient.

In 2024, Germany created regional redispatch markets so that renewable electricity that would otherwise be curtailed can instead be sold locally. This is particularly useful for hydrogen producers because they do not require a continuous supply of electricity. Because they can operate flexibly, turning on and off depending on electricity availability, they can take advantage of very low-priced electricity. In this work, the researchers ask whether using this electricity source makes producing green hydrogen cheaper and how market prices for otherwise curtailed renewable electricity affect this.

They found that using only redispatch electricity is not cost-effective because of its unreliability. However, combining it with different renewable energy sources does reduce costs. Depending on hydrogen storage costs, using redispatch electricity can reduce hydrogen production costs by €0.9 – €1.96 per kg, if the redispatch electricity is available for 0 €/MWh, compared to typical costs of around €6-8 per kg without redispatch electricity.

Increased redispatch prices reduce the potential for production cost reductions, which, combined with the unreliability of redispatch availability, lowers the incentive for
system beneficial electrolyser siting within the regional market areas. Therefore, the success of these markets depends heavily on keeping prices low, which might necessitate effective price capping but could increase green hydrogen competitiveness and simultaneously decrease renewable energy curtailment.

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Metrics for assessing the economic impacts of power sector climate and clean electricity policies John Bistline (2021)

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Top quarks are the heaviest known elementary particles. They are produced in pairs with their antiparticle equivalent, the antitop quark. Researchers at the Large Hadron Collider (LHC) smash protons together at extremely high energies in the hope of producing these top-quark pairs. The minimum energy required to create a pair is known as the production threshold (approximately twice the top mass). Near this threshold, the particles move relatively slowly and, because of the strong nuclear force, they may briefly interact strongly enough to form a quasi-bound state before each top quark decays individually.

In this work, researchers used a huge dataset collected with the ATLAS detector at the LHC. Top quark pairs decay far too quickly to be observed directly, so the ATLAS team instead studied their decay products. A top quark usually decays into a W boson and a b-quark, while the antitop quark decays into the corresponding antiparticles. The W bosons then decay further, either through leptonic decay (producing an electron, muon or tau plus a neutrino) or through hadronic decay, which produces jets of particles.

The ATLAS analysis focuses on the case where both W bosons decay leptonically into electrons and muons and reconstructs the invariant mass of the system using the energy and momentum of the decay products.

If non-relativistic QCD effects are included, the event rate near threshold is expected to be higher than that predicted by standard perturbative QCD alone, due to contributions such as the formation of quasi-bound states that are not accounted for in the perturbative framework. The analysis also employs spin-correlation observables to enhance the sensitivity to the unique spin structure of the phenomena. If non-relativitic QCD effects occur near the production threshold, including the formation of quasi-bound states, physicists expect to see an excess of events concentrated at masses close to the minimum energy needed to produce the pair.

The researchers observed significantly more events near the production threshold (∼345 GeV) than standard QCD calculations predicted, confirming a previous similar measurement by the CMS collaboration. The excess matches predictions from non-relativistic QCD that the top and antitop quarks briefly perform non-relativistic QCD interaction, including the formation of quasi-bound states, before separating and decaying. This is important because it provides strong evidence for a subtle quantum effect of the strong force and improves our understanding of top-quark behaviour in an extreme regime. This measurement underscores the capacity of LHC experiments at the precision frontier, reinforcing the vital interplay between theory and experiment in these precision studies.

“This is a very exciting finding that pushes our current understanding of top quark physics and its modelling to the extreme. It has only been made possible by recent efforts to connect quantum information theory and collider physics.” – Yoav Afik, an ATLAS physicist affiliated with the Enrico Fermi Institute at the University of Chicago

“With the newly collected Run 3 dataset, which is more than twice as large as the Run 2 dataset used in this first analysis, we will be able to scrutinize this excess in much greater detail and determine whether it can be described solely by non-relativistic QCD effects, or whether there is something more to it.” – Baptiste Ravina, a Senior Research Fellow at CERN

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Towards the identification of new physics through quark flavour violating processes by Andrzej J Buras and Jennifer Girrbach (2014)

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