National Science Review最新文献

Helical organization in soft materials is omnipresent in systems ranging from DNA and peptides to liquid crystal displays. Dynamic transformation and reconfiguration of helicity triggered non-invasively by light are highly desirable, yet challenging to control in soft-condensed matter. Herein, we report the photo-transformation of helicity in soft matter with robust manipulation of the helical pitch and inversion of chirality. The key molecular design is based on the introduction of a multi-branched dendron-like chiral photoswitch, along with balancing long-range order and short-range disorder states, featuring ultra-large helical twisting power (HTP) and initiating an extremely broad dynamic spectral range (400-3000 nm). The resonance coupling between helixes and inherent luminescence of the chiral photoswitch enables stimulated circularly polarized luminescence (CPL), with a dissymmetric factor of 1.97 approaching the theoretical limit. The precise dynamic control allows for photo-tailorable infrared beams and high dimensional coding, offering a robust approach to dynamic soft matter, chiro-optics and information processing.

Twisted homobilayer transition metal dichalcogenides-specifically twisted bilayer MoTe[Formula: see text] and twisted bilayer WSe[Formula: see text]-have recently emerged as a versatile platform for strongly correlated and topological phases of matter. These two-dimensional systems host tunable flat Chern bands in which Coulomb interactions can dominate over kinetic energy, giving rise to a variety of interaction-driven phenomena. A series of groundbreaking experiments have revealed a rich landscape of quantum phases, including integer and fractional quantum anomalous Hall states, quantum spin Hall states, anomalous Hall metals, zero-field composite Fermi liquids and unconventional superconductors, along with more conventional topologically trivial correlated states, including antiferromagnets. This review surveys recent experimental discoveries and theoretical progress in understanding these phases, with a focus on the key underlying mechanisms-band topology, electron interactions, symmetry breaking and charge fractionalization. We emphasize the unique physics of twisted transition metal dichalcogenide homobilayers in comparison to other related systems, discuss open questions and outline promising directions for future research.

Energy-dense sodium-ion batteries (SIBs) offer lithium-free, cost-effective solutions for grid-scale energy storage. However, the structural complexity of hard carbon (HC) anodes hinders the establishment of a clear structure-performance relationship, leading to the insufficient performance of current HC when paired with advanced cathodes. In this study, we precisely adjusted the content and size of closed pores in HC using an economical, extensible rosin-assisted pore-promoting strategy, and quantified the effective pore volume for sodium storage through small-angle X-ray scattering experiments. We show that optimizing the closed pore size to increase the effective pore volume is key to enhancing the electrochemical performance of HC. By controlling the size (∼2 nm) of closed pores, we enhance the Na-cluster filled volume fraction of HC, resulting in an extended low-potential plateau (<0.1 V vs. Na⁺/Na) and higher sodium storage capacity. Additionally, we established a positive correlation between the plateau capacity of HC and the effective pore volume. Consequently, the 4.5 Ah pouch-type SIBs assembled with optimized HC here (areal capacity, 2.8 mAh cm^-2) achieved a high energy density of 202 Wh kg^-1, with over 80% capacity retention after 500 cycles at 0.5 C. This research provides a solution for realizing low-cost, advanced SIBs.

A lack of quantitative rainfall reconstruction has hindered understanding of the role of hydrological disturbances at ∼4.2 kyr BP (1000 years before present) in the collapse of the Shijiahe culture-an advanced Neolithic society in the Middle Yangtze Valley (MYV). We provide a quantitative paleohydrology reconstruction for the period 4.6-3.5 kyr BP by using calcium isotopes, trace elements and δ¹³C from an annually laminated stalagmite from the MYV. Our reconstructed rainfall shows three drier intervals with rainfall of <700 mm/yr (4.36-4.33 kyr BP, 4.23-4.10 kyr BP, 3.57-3.55 kyr BP) and two wetter intervals with rainfall of >1000 mm/yr (3.95-3.84 kyr BP, 3.70-3.59 kyr BP), with suggestions of tripole/dipole rainfall patterns. Combined with archaeological and paleoflood evidence, these data suggest that the Shijiahe culture underwent transformation during drier periods, but abandoned the region when the rainfall was >1000 mm/yr. This robust, multiproxy record demonstrates that water excess could be as problematic as water shortage, even for advanced civilizations, and contributes to understanding hydrological perturbations at ∼4.2 kyr BP.

Marginally twisted bilayer graphene having small twist angles is predicted to exhibit unique structural and electronic properties, though experimental characterization remains limited. Using scanning tunneling microscopy, we investigate such systems with twist angles of 0.06°-0.35°. AA-stacked regions reveal a pronounced tunneling spectral peak signifying highly localized electronic states. Conversely, AB domains display uniform multiple spectral peaks, indicative of strong lattice reconstruction and enhanced electronic homogeneity. We identify two distinct strain-induced domain walls: one exhibits a sharp -120 meV spectral peak (shear type), while the other shows distinct spectral characteristics (mixed shear-tensile type). Tight-binding calculations verify strain-driven transformations of both domain wall types and confirm direct observation of strain-mediated domain wall transitions. These results elucidate the electronic structure of marginally twisted bilayer graphene and establish strain as a control parameter for domain wall states.

The core mission of COP30 was to turn existing climate promises into concrete action.

The success of data-driven deep learning in computational imaging is often constrained by the need for extensive labeled datasets. Recent progress in physics-informed neural networks has mitigated this issue by integrating analytical physical models, allowing data-free training. However, for challenging imaging tasks, such as to simultaneously acquire complex amplitude light-field information, the weak physical constraints of conventional imaging hardware largely limit the spatiotemporal imaging resolution. Here, we propose an extremely simple yet powerful monocular camera for complex amplitude imaging based on a liquid-crystal (LC)-lens-informed Fourier neural network. Combining a polarization-multiplexed bifocal LC lens with a polarization image sensor, the camera acts as a polarization phase-shifting radial shearing interferometer. Without any labeled data, the LC-lens-informed Fourier neural network can reconstruct the complex amplitude of a variety of scenes from captured polarization images in a single shot with high fidelity. We experimentally demonstrate the reconstruction of wavefront aberrations involving 136 Zernike modes with a phase accuracy of λ/35 as well as static hologram retrieval and dynamic monitoring of air flow and flame fields. This complementary hardware-algorithm framework offers a promising pathway for developing compact, versatile and high-performance complex amplitude imaging systems for adaptive optics, hologram reconstruction and material diagnosis applications.

While many nations committed to the Paris Agreement have completed the second-round updates to their nationally determined contributions (NDCs), especially China's dual carbon commitment, the specific climate outcomes of these latest NDCs remain uncertain. Here, we quantify the potential climate mitigation outcomes from these latest NDCs through ensemble simulations of an Earth System Model under a newly developed global emission scenario aligned with China's carbon neutrality pathway. We project a global temperature rise of 2.05°C during 2081-2100 through the implementation of the latest NDCs, demonstrating a likely achievable 2.0°C target without early extensive carbon removal technologies. Moreover, our results demonstrate that the latest NDCs will yield significant long-term climate benefits while incurring adverse near-term impacts, revealing temporally asymmetric climate outcomes when compared to fixing anthropogenic emissions at 2023 levels. We believe this work is valuable for understanding more plausible future climate change, with particular relevance to the ongoing seventh assessment report of the Intergovernmental Panel on Climate Change (IPCC).

Physical non-linearities near the Mott transition exhibit substantial potential for neuromorphic computing. The complex computational behaviour stems from their intrinsic local active characteristics. Most studies focus on decay dynamics or regular oscillations, treating Mott devices primarily as simple threshold elements. Challenges remain in connecting measurable material properties to more complex device dynamics and their control methods through a unified theoretical model. Here, we develop a thermodynamic compact model for vanadium oxide devices based on electrical measurements and the local active principle. Utilizing the non-linearities near the Mott transition, we propose an injection-based control method to regulate behaviours of non-linear oscillators, such as frequency division, stochastic oscillations and frequency locking. Finally, a single device operating at the edge of chaos demonstrates exceptional capability in extracting information in the frequency domain within a physical computing framework, achieving performance equivalent to a two-layer convolutional neural network on the same task. This work facilitates a paradigm shift from traditional local passive devices to local active devices, bridging the physical non-linearities, circuit dynamics and computational theory to advance dynamic neuromorphic computing.

Accurate prediction of protein-ligand binding free energies is critical yet computationally demanding in drug discovery. Alchemical free energy methods (AFEMs) offer high accuracy but suffer from significant computational costs and complex modeling setup, such as tuning the λ-schedule of alchemical transformation. While conventional deep learning (DL) models may instantly predict binding affinity, they often require a large training set and exhibit limited generalizability across chemical space. To address these challenges, we introduce LamNet, an alchemical-path-aware graph neural network. LamNet integrates endpoint molecular states and the bridging alchemical path (parametrized by λ) into a physics-informed representation learning framework, explicitly modeling free energy changes along a chosen thermodynamic transformation pathway. Trained on molecular-dynamics-simulated data along alchemical pathways and incorporating data reliability metrics, LamNet accurately predicts relative binding free energies and absolute binding free energies, and optimizes λ-schedules to improve traditional AFEM convergence. Evaluations on diverse datasets (463 ligands, 16 proteins) demonstrate that LamNet achieves superior or comparable performance to state-of-the-art methods, including traditional AFEM, but with up to 1000-fold acceleration. These findings establish LamNet as a generalizable, physics-grounded, and cost-effective tool that not only accelerates computations but also provides a novel framework for integrating rigorous computational physics into modern DL-driven drug discovery workflows.