Physics

Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate lensless ptychography that overcomes this barrier through the co-design of optics, sensing scheme, and computation: a coded surface encodes the dynamic object, time-sequential sensing spreads its information across frames, and a space-time neural-field framework recovers the dynamics by exploiting spatiotemporal correlations. Our approach transforms SBP scaling from sequential measurements to efficient correlation extraction. We demonstrate video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths, achieving an SBP throughput of 20.8 gigapixels per second. Experimental validations span from monitoring mesoscale dynamics of snowflakes, bacteria, stem cells, microneedles, to characterizing time-varying dynamics in extreme-ultraviolet (EUV) experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we enable single-sensor gigapixel video that extends naturally to short-wavelength and electron regimes where radiation sensitivity has traditionally precluded high-resolution dynamic imaging.

Accurate descriptions of interactions between atoms are essential for molecular simulations used to study biology and support drug discovery. Existing force fields often face a trade-off between physical reliability, computational efficiency, and accuracy across unfamiliar molecules. Here we show that Residual Learning Force Field, a hybrid machine learning force field, can reduce this trade-off by combining simple physics-based descriptions of bonded interactions with learned corrections for remaining energetic effects. The two components are trained together through a three-step strategy so that each contributes complementary information. In tests covering drug-like molecules, molecular dimers, torsional energy profiles, energy-minimum structures, and biomolecular simulations, Residual Learning Force Field gives accurate and stable predictions across diverse systems. These results suggest that combining physical constraints with data-driven corrections can provide a practical route toward more reliable and efficient molecular simulation for biological research and drug discovery.

Ceramic-matrix composites face a persistent challenge: the trade-off between strength and toughness. Inspired by the mineral bridge architecture of nacre, we propose a reverse interphase design that contrasts with conventional dense-laminar pyrolytic carbon given the active incorporation of nanopores. Multiscale characterization and simulations reveal a dual reinforcement mechanism: nanopores reduce the interfacial debonding strength and induce crack deflection that protects fibers from brittle fracture. Meanwhile, the resulting rough fracture paths enhance interfacial frictional stress and load transfer, thereby improving the matrix bearing capacity and energy dissipation. This asymmetric modulation of interfacial properties simultaneously preserves fiber integrity and maximizes energy dissipation. The resulting single-tow Cf/SiC composites exhibit 903 MPa tensile strength, which is 38% higher than that of conventional designs, and a 1.8-fold increase in fracture energy. The interphase-enabled mechanisms identified here are intrinsically scalable, with their effectiveness further demonstrated in architectured ceramic-matrix composites. This work demonstrates a shift from empirical optimization toward theory-driven interface design and establishes a viable route to overcome the classical strength–toughness dilemma in structural composites. Ceramic composites are prone to breaking. Here, authors propose a bioinspired porous interphase that guides crack deflection and growth, coupling weak debonding with high friction to enhance load transfer, energy dissipation, strength and toughness.

Abstract Two-dimensional (2D) materials have been demonstrated as promising candidates for ultracompact entangled photon sources and on-chip integrated quantum photonic devices. Ferroelectric van der Waals materials have recently emerged as promising ultrathin sources of entangled photons via nonlinear optical processes. Here, the authors compare spontaneous parametric down conversion in NbOBr 2 and NbOI 2 , demonstrating near infrared polarization entangled photon generation and linking performance to crystal structure and optical properties. Our results extend the wavelength range of NbOX 2 -based SPDC sources and demonstrate NbOI 2 is especially suitable for near-infrared SPDC. Polarization entangled state (concurrence of 0.90 ± 0.05) can also be directly generated from NbOI 2 . Besides adding new candidates to the SPDC material library, our work provides new insight for the future generation of integrated quantum materials.

Solar flares are the largest energy releasing events in the solar system, where the open magnetic field lines reconnect and form the closed flare loops. During this process, rapid magnetic reconnection, the associated shock waves, and chromospheric evaporation are expected but not yet well understood. These processes are crucial for understanding similar features in stellar flares and other astrophysical jets. Here, we report the characteristics of propagating slow-mode shocks in the flare loop system, by combining a 3D high-resolution magnetohydrodynamics modeling and spectral analysis of Extreme Ultra-Violet observations. It is found that normal slow shocks are recurrently formed after the collision between the post-reconnection downflows and evaporation flows in the flare loops, which subsequently propagate toward the chromosphere at speeds comparable to the evaporation flows. In particular, the Doppler analysis of the Fe XXI 1354 Å line normally shows a sharp change in blueshifted velocity and an asymmetrical line broadening once the line-of-sight passes through the shock front. This study highlights that propagation of slow shocks can facilitate energy release in flares and affect energy transport, suggesting an advancement in the standard flare model framework. Slow shocks have been invoked to explain flare observations, but simulations suggest that they result in relatively small energy release in the post-loops. Here, the authors show evidence for the existence of propagating slow shocks, due to the downflow-evaporation collision, which contributes to efficient energy dissipation in the shrinking post-flare loops.

The emergence of hydrodynamics is one of the deepest phenomena in many-body systems. Arguably, the hydrodynamic equations are also the most important tools for predicting large-scale behaviour. Understanding how such equations emerge from microscopic deterministic dynamics is a century-old problem, despite recent progress in fine-tuned integrable systems. Due to the universality of hydrodynamics, the specific microscopic implementation should not matter. Here, we show that classical deterministic circuits provide a minimal, exact, and efficient platform that admits non-trivial hydrodynamic behaviour for deterministic but chaotic systems. By developing new techniques and focusing on 1D circuits as a proof of concept, we obtain the characteristic dynamics, including relaxation to Gibbs states, exact Euler equations, shocks, diffusion, and exact KPZ super-diffusion. Our methods can be easily generalised to higher dimensions or quantum circuits.

Real bipartite networks combine degree-constrained random mixing with structured connectivity balancing short and long range connections, effectively accounted for by geometric network models. We introduce a statistical filter that benchmarks node-level bipartite clustering against degree-preserving randomizations to classify nodes as geometric (signal) or degree constrained noise. In synthetic mixtures with known ground truth, the filter achieves high classification accuracy and sharpens inference of latent geometric parameters. Applied to four empirical systems –metabolism, online group membership, plant-pollinator interactions, and languages– the filter isolates recurrent neighborhoods while removing ubiquitous or weakly co-occurring entities. Filtering exposes a compact geometric backbone that disproportionately sustains connectivity under percolation and preserves downstream classifier accuracy in node-feature tasks, offering a simple, scalable way to disentangle structure from noise in bipartite networks. Real bipartite networks combine structured relations with non-specific connectivity. Here, authors introduce a clustering-based node-level filtering method that extracts the transitivity backbone, identifying meaningful structure that sustains connectivity and preserves predictive performance.

Ultra-wide bandgap gallium oxides offer tremendous possibilities to develop short-wave optoelectronic devices. However, it is formidably challenging to produce single-crystal gallium oxide wafer and develop high-performance high-dimensional optoelectronics. Here we show a liquid-metal-assisted strategy to directly synthesize and transfer single-crystal, large-area and ultrathin β-Ga2O3. Benefiting from the UV-exposure oxidation of liquid gallium and strong interaction with gallium, our β-Ga2O3 film shows a 4-inch wafer-scale size, a 7.5-nm thickness and a flexible transfer operation. The solar-blind β-Ga2O3 detector achieves high responsivity (16.3 A W−1), fast response (<150 μs) and wide linear dynamic range (120 dB). By employing metasurface design, the anisotropy ratio reaches a record high value of 28.8 for Ga2O3-based detectors. Moreover, we develop a sundial-inspired metasystem to simultaneously detect the incident direction, polarization, and intensity of solar-blind irradiation. These findings illustrate the potential of high-quality Ga2O3 wafer for high-dimensional photodetection, paving the way for next-generation solar-blind communications. Researchers show a liquid-metal-assisted strategy to synthesize and transfer 4-inch ultrathin β-Ga2O3 single crystal. The sundial-inspired metasystem can simultaneously detect the direction, polarization and intensity of solar-blind irradiation.

Liquid water exists on the Earth and several other planetary bodies in our solar system. The chemical character of these aqueous reservoirs is central to evaluating their habitability. Here, we synthesize the chemical features of water reservoirs and their biological implications on the modern Earth. We then outline constraints on the evolutionary history of Earth's ocean chemistry and discuss its interplay with the biosphere. Furthermore, we examine the inferred chemical environments of water bodies on early Mars, dwarf planet Ceres, Jupiter's moon Europa and Saturn's moons Enceladus and Titan. We conclude by outlining priority questions for future planetary habitability studies.

The interplay between displacement defects governs the evolution of irradiation damage in materials and is of great fundamental interests with important practical implications, from microelectronics industry to advanced nuclear system. Hydrogen, a ubiquitous impurity, is known to segregate to vacancies, but its role in altering vacancy-interstitial recombination-the key process underlying defect annihilation-has not been established. Here, using tungsten as a model system, we show that hydrogen adsorption on the inner surfaces of vacancy clusters significantly suppresses recombination with self-interstitial atoms, thereby inhibiting defect annihilation. We identify a stress-mediated mechanism in which hydrogen adsorption transforms the local stress field of vacancy clusters, weakening their long-range attraction to self-interstitial atoms. Based on this mechanism, we develop a predictive model that quantitatively relates the relative reduction of recombination radius to the hydrogen inner surface density, independent of cluster size. By integrating atomistic parametrization with multiscale simulations, we investigate the co-evolution of hydrogen and displacement defects, which show quantitative agreement with recent experiments, including the hydrogen isotope retention, distribution and desorption. Our results establish a direct link between impurity-defect interactions and defect-defect recombination, providing a physically grounded framework for understanding and controlling irradiation damage in structural materials.

Neutrons provide exceptional insight into materials, owing to their sensitivity to light elements, isotopic composition, magnetic moments, and high-penetration. However, neutron sources are polychromatic and of low brightness. Neutron optics provides a route to address these limitations by focusing, and to date, various types of neutron optics have been developed based on reflection, refraction, diffraction, and magnetism. Notably, compound refractive lenses and Fresnel zone plates have been demonstrated for imaging, yet their severe chromatic aberration under polychromatic beams has prevented their widespread use and limits progress towards true high-resolution neutron microscopy. Here, we demonstrate an achromatic neutron lens for full-field neutron microscopy. This development overcomes the intrinsic sample-detector distance constraint in pinhole-based radiography. The lens magnification enables the use of efficient detection systems without loss of spatial resolution and establishes a pathway towards high-resolution neutron microscopy. We anticipate the neutron achromat will advance a broad range of neutron methods.

Recent advances in computational microscopy enable highspeed high-resolution intravital 3D imaging with low phototoxicity. However, inevitable sample vibration and tissue deformation in multi-cellular organisms make it extremely challenging to maintain samples stably in focus over long-term even with an extended effective depth of field. Here, we propose a real-time robust autofocus method based on scanning light-field microscopy (AFsLF), enabling sustained high-speed 3D imaging of diverse samples across several days by continuously tracking the sample focal plane without hardware modifications. Based on the intrinsic disparity of light-field angular measurements, AFsLF estimates the focal plane with less than 2 µm error over a 500 µm depth range, completing within 0.1 s, 300-time faster than previous methods. We validate AFsLF across diverse tissues and challenging conditions, including low excitation power, multichannel illumination, and large axial displacements, enabling stable, long-term, multichannel subcellular imaging of neural activities and immune responses in mouse brain and liver. Continuous focus maintenance during intravital 3D imaging remains challenging. Here, Wang et al. proposed a real-time autofocus method for scanning light-field microscopy that enables stable, long-term, multichannel subcellular imaging of neural activities and immune responses in mouse brain and liver.

Martian rocks are known to contain sulfur-bearing species, including sulfates and sulfides. These compounds record a sulfur cycle that operated over Mars' geological evolution. We used the Curiosity rover to investigate a deposit of light-toned stones in Gediz Vallis, within Gale crater on Mars. We find that the stones are composed of native sulfur. The sulfur deposit appears to have formed in place, within a sinuous entrenched canyon cut into the floor of Gediz Vallis. The presence of native sulfur implies that a sulfur enrichment pathway involving buoyant subsurface fluids operated on ancient Mars. We propose that the primary source of this sulfur was magmatic vapor, which cooled in the near subsurface cryosphere and was released by decompression during the erosion of Gediz Vallis.