Solar-driven interfacial evaporation is a promising route to sustainable seawater desalination. However, challenges remain in achieving the dual regulation of photothermal conversion and hydration network for efficient evaporation. Inspired by natural photosynthesis, this study introduces a porphyrin metal-organic framework (MOF) wood evaporator, which enables simultaneous water evaporation, photocatalysis, and thermoelectric conversion under solar irradiation. The porphyrin MOF, serving as a dual platform for photothermal and photocatalytic applications, is anchored onto wood via stable chemical bonds. Meanwhile, the active groups on the porphyrin MOF improve the hydration network by binding to water molecules. Structural reconstruction rearranges loose cellulose fibers, forming an interwoven micro/nanocellulose network within the wood. This provides additional coordination sites for MOF anchoring. The prepared evaporator achieves an evaporation rate of 2.91 kg m-2 h-1, a H2O2 generation rate of 14.2 mmol m-2 h-1, and an output voltage of 50.2 mV under 1 sun irradiation. Density functional theory (DFT) calculations confirm that MOF incorporation substantially enhances evaporator-water interactions, reducing the evaporation enthalpy. Life cycle assessment (LCA) indicates that the prepared evaporator exhibits lower environmental impacts across multiple categories compared with three conventional desalination technologies. This work offers a sustainable route for solar-driven water evaporation.

Doping remains one of the most difficult yet essential processes for single wall carbon-nanotubes (SWCNTs) electronics. The highly stable sp2 carbon network makes doping extremely difficult, rendering the development of SWCNTs-compatible doping strategies and reliable doping-control methods particularly challenging. Herein, we establish a controllable doping strategy by combining Pd encapsulation with electron-beam(e-beam) irradiation, enabling precise doping modulation. With this strategy, strong p-type doping introduced by Pd encapsulation can be effectively modulated by acceleration voltage of e-beam, enabling tunable, reversible doping control from lightly doped to heavily doped states. This approach allows the construction of SWCNTs transistors with heavily doped contacts and a lightly doped channel. Compared with pristine SWCNTs device, achieving a contact resistance reduced by half to 23 kΩ µm-1, an on-current enhanced by about four times to 24 µA µm-1, and an on/off ratio exceeding 107, providing an effective approach to overcome the challenges of efficient doping in carbon-based electronics.

Lightweight and highly conductive carbon nanotube fibers (CNTFs) are attractive for flexible electronics, yet their performance remains constrained by inefficient charge transport. Here we report a synergistic doping strategy that integrates in-plane nitrogen doping with endohedral molybdenum pentachloride (MoCl5) incorporation to produce CNTFs with exceptional electrical properties and environmental durability. Nitrogen doping creates sidewall defect sites that promote MoCl5 encapsulation, yielding a strong charge-transfer effect and markedly increased carrier density. The resulting fibers achieve a high specific electrical conductivity of 14166 S m2 kg-1 and a current carrying capacity of 1241 A mm-2, surpassing copper by 115% and 28%, respectively. The CNTFs also exhibit high flexibility and environmental stability, retaining performance under thermal, mechanical, and solvent stresses. When woven into textiles, they deliver an electromagnetic shielding effectiveness of 92.7 dB (8.2-12.4 GHz). This work establishes a scalable doping approach for fabricating ultrahigh-conductivity CNTFs for advanced flexible electronics.

As circuit integration continues to advance, power consumption has become a critical bottleneck limiting further development. Multi-valued logic (MVL) has garnered extensive attention due to its potential to reduce interconnect complexity and switching losses. Single-walled carbon nanotubes (SWCNTs), with their superior electrical properties, ultra-small dimensions, and controllable aligned array growth, offer unique advantages for the large-scale fabrication of high-density MVL circuits. However, progress in this field using SWCNTs remains relatively lagging compared to two-dimensional materials, primarily due to device stability issues arising from challenges in precise doping control. Here, we demonstrate a system consisting of acetylacetonate metal molecules encapsulated within SWCNTs (M(acac)x@s-SWCNT), in which carrier concentration can be dynamically modulated under an applied electric field. Transistors based on this platform validate that this electric-field-controlled modulation yields three well-defined logic states: 0, 1, and 2. These transistors demonstrate good uniformity and stable operation, showing a static power consumption of 8.2 pW and dynamic power consumption of 0.31 nJ (state 0 to 1) and 0.35 µJ (state 1 to 2). The ternary inverter based on this heterostructure exhibits rail-to-rail output capability, enabling the accurate execution of MVL operations. Ternary weight networks (TWNs) built with these transistors reduce computational complexity and storage, enabling efficient neuromorphic computing.

Solar interfacial evaporation and photocatalysis exhibit intrinsic complementarity in energy utilization pathways and reaction mechanisms. Therefore, integrating photocatalysis into interfacial evaporation systems enables a synergistic platform for efficient evaporation and pollutant removal. In this study, a strategy of defect-engineering is developed for UiO-66 by covalently anchoring five carboxyl-containing organic dyes into its framework, in which steric hindrance and ligand substitution synergistically induce abundant structural defects. This approach produces a series of defect-rich UiO-66 materials by regulating of dyes. Among, the dye-sensitized UiO-66@dye-2 system features optimal light absorption capacity and vacancy defects. On the one hand, the dyes, which act as sensitizers, broaden the light absorption range and accelerate water evaporation. On the other hand, the defect-inducing dyes introduce abundant trap sites, thereby enabling rapid charge transfer and efficient spatial charge separation. Under 1-sun irradiation, it exhibits an outstanding water evaporation rate with a high solar-to-vapor conversion efficiency of 97.8

Understanding the plastic behavior at crack tips is critical for enhancing the fracture toughness of nanometals. Although extensive research has been conducted, most previous studies have focused on pure metals, and how the crack tips accommodate plastic deformation in highly concentrated solid-solution alloys remain unclear due to limited atomic-scale evidence. In this study, the atomic-scale plastic behavior of crack tips in face-centered cubic (FCC) AuCu nanocrystals is investigated in situ. The results provide direct evidence that plastic deformation is governed by sequential activation of different deformation mechanisms, i.e., full dislocation activities first, then followed by random twinning/detwinning, and finally dislocation-twin interactions, which are rarely observed in pure metals. These deformation processes collectively enhance the fracture toughness of the nanocrystals, representing a previously unrecognized mechanism for fracture toughness improvement in metals. This work not only offers atomic-scale insights into the deformation behavior of nano-alloy materials but also provides new perspectives for the design of high-performance alloys with superior fracture resistance.

Twist-polaritonics provides precise control of light-matter states through the stacking of atomically smooth, anisotropic layers, but has been restricted to the van der Waals (vdW) crystals. Non-vdW crystals, despite their symmetry-broken dielectric responses ideal for exotic polaritons, are challenging to prepare as suitable flakes due to their rigid 3D bonding networks, thus limiting the implementation of deep-subwavelength twist-polaritonics. We established a non-vdW polaritonic platform using ultrathin, single-crystalline β-Ga2O3 nanoflakes synthesized by exploiting its anisotropic bonding hierarchy on the quasi-layered (100)B plane. These flakes exhibited deep-subwavelength polariton confinement beyond λ/20. Moreover, their atomic-scale flatness enabled the assembly of twisted bilayers, in which we observed a topological transition of the polariton dispersion from hyperbolic to elliptical, directly controlled by the twist angle. This work positions β-Ga2O3 as a high-performance nanophotonic platform beyond the vdW family, while proposing that anisotropic bonding hierarchy provides a general strategy to unlock non-vdW twist-polaritonic functionality in a wide range of bulk crystals.

The low-power ionic-type memristor and brain-inspired neuromorphic device offer significant potential in breaking the power consumption wall. However, the precise control of uniform metallic conductive filament (CF) at both intra- and inter-molecular levels rather than random migration raises a pressing challenge. Here, we first report a symmetrical dual-core naphthalene diimide (bis-NDI) molecular material featuring multi-active and lamellarly ordered redox sites, which actuates reconfigurable analog-to-digital (A-t-D) memristive operations via the controllable manipulation of CF growth at the molecular scale. The bis-NDI-based memristor exhibits highly efficient analog synaptic behaviors, demonstrating an ultralow-power consumption of 90 aJ µm-2. By effectively re-organizing lamellar redox sites, the device dynamically implements A-t-D transition with an operating voltage of 0.5 V (lower than most reported organic memristors) and ultrahigh yield of 98%. Relying on the bis-NDI induced A-t-D dynamic plasticity, a novel feedback mechanism of pruning algorithm is subtly devised for granular error analysis and voltage adjustment validation in spiking neural networks (SNNs) computing. The co-design of material-algorithm can effectively reduce the number of connected neurons (max reduced proportion = 92%), thereby achieving ultralow systemic energy consumption while maintaining exalted recognition rates (>90%). This work paves the material-algorithm cooperation way to realize ultralow-power neuromorphic devices and highly-efficient spiking computing.

The rapid growth of artificial intelligence and the Internet of Things calls for compact hardware platforms that integrate sensing, computing, and nonlinear processing within a unified architecture. However, most existing neuromorphic systems implement only partial functionalities and rely on heterogeneous device integration, limiting scalability and efficiency. Here, we show a high-speed, reconfigurable multi-modal split-floating-gate memory that monolithically integrates in-sensor computing, in-memory computing, and multiple nonlinear activation functions within a single device structure. By programming charges in spatially separated floating gates, the device enables non-volatile analog control of photoresponsivity and conductance, as well as electrically reconfigurable rectification to emulate ReLU and Sigmoid activations. We further demonstrate a fully hardware-implemented sensor-processor system based on the multi-modal split-floating-gate memory arrays that performs complete unsupervised and supervised learning tasks. This work establishes a compact, energy-efficient, and reconfigurable hardware foundation for scalable intelligent systems beyond conventional silicon architectures.

Solar-driven water evaporation and photocatalysis are promising and sustainable strategies for alleviating global freshwater shortages and energy crises. Wood-based solar evaporators attract considerable interest for their ecofriendliness, low cost, and inherent porous structure. However, their practical application is often hindered by limited light absorption, poor salt resistance, and inefficient water transport. Herein, a new interfacial array-structured wood evaporator modified with metal-organic frameworks (MOFs) is designed. Hydrophilic MOFs are anchored within the lamellar pores of wood via an in-situ growth strategy, forming a MOF-wood hybrid network that significantly reduces the energy required for water evaporation while increasing the water transport rate. The interfacial array structure enhances light absorption and facilitates salt solubilization backflow. Furthermore, covalently grafted ferrocene molecules serve as core functional units with highly efficient photo-thermal conversion and rapid electron transfer capabilities, constructing an efficient bifunctional platform for photothermal evaporation-catalysis. The prepared evaporator delivers an exceptional evaporation rate of 2.62 kg m-2 h-1 under one sun while simultaneously achieving a 99.65% yield of cis-stilbene in photothermal catalytic reactions. The evaporator also exhibits outstanding cyclic stability, superior salt resistance, and broad applicability across diverse aqueous environments. The present study provides new insights into the construction of a synergistically enhanced solar-driven energy-resource integration system.

Edge artificial intelligence (AI) and embodied vision call for compact, fast, and energy-efficient hardware that integrates sensing, linear analog computation, and nonlinear activation, while flexibly reallocating these functions as workloads change. However, in-sensor computing (ISC) and in-memory computing (IMC) platforms still implement activation with external peripherals and use fixed functional partitions, which break the analog signal path and restrict system reconfigurability. Here, we report a reconfigurable ferroelectric transistor (Fe-FET) array in which polarization-programmed local fields enable junction-barrier engineering in ambipolar tungsten diselenide (WSe2) channel. This junction-barrier engineering mechanism co-programs photoresponsivity, multilevel conductance, and tunable nonlinear transport within the same device, allowing each Fe-FET cell to be reassigned among weighted sensing (ISC), linear accumulation (IMC), and hardware-native activation. The array therefore functions as a uniform pool of physical units whose roles and spatial partitions can be dynamically allocated to match task demands without changing the hardware platform. Using this role-reconfigurable platform, we implement an end-to-end analog neuromorphic vision system in which broadband sensing, linear computation, and nonlinear activation are executed natively on the same Fe-FET platform. These results establish a task-adaptive and energy-efficient route toward scalable neuromorphic vision hardware for edge intelligence.

One-dimensional (1D) atomic wires represent the ultimate dimensional form of periodic materials, featuring distinctive structural characteristics in which their cross-sectional dimensions approach the atomic scale (∼1 nm), and they exhibit intrinsically self-passivated edges and pronounced directional anisotropy. The confinement of electronic states to a single spatial dimension induces intense quantum confinement effects and gives rise to novel physical phenomena, rendering them ideal model systems for exploring low-dimensional quantum behaviors. This review focuses on the core physical effects and functional properties of 1D atomic wires, including strong electron-electron correlation, electron-phonon coupling, anisotropic electronic and thermal transport, unique magnetic exchange interaction and magnetoelectric coupling, prominent excitonic effects, and chirality-related physical phenomena. These properties exhibit distinct manifestations across different structural configurations, such as isolated single wires, van der Waals-coupled multi-wires systems, and quasi-1D hybrid structures. Overall, 1D atomic wires not only provide an exceptional platform for the study of low-dimensional many-body physics, but also demonstrate great potential in realizing ultimate-scale interconnects for nanoelectronic devices, bridging the gap between fundamental quantum research and advanced functional applications.

This comprehensive review presents heterogeneous integration strategies for neuromorphic electronics, aiming to eliminate the von Neumann bottleneck, with a focus on enabling in‐sensor computation. It provides the evolution trajectory of memristor devices and arrays—from theoretical grounding and physical implementation to promising integration schemes. The device‐level discussion encompasses internal integration approaches that leverage multidimensional materials to modulate switching dynamics and enable artificial synaptic behaviors. At the array level, diverse paradigms of heterogeneous integration strategies are investigated, including architectures of 1D, resistive randomaccess memory, transistor‐rram, memtransistor, and 3D crossbar arrays. Each architecture is evaluated based on its functional advantages, physical significance, and neuromorphic applicability in systems that include convolutional neural networks, spiking neural networks, and reservoir computing. Emphasizing the syncretic design of materials, devices, and circuits, the review illustrates how these strategies facilitate the fusion of sensing, memory, and computation, thereby overcoming limitations of conventional computing. Representative implementations, ranging from planar wafer‐scale arrays to vertical 3D‐integrated systems, demonstrate significant innovations in energy efficiency and adaptive control for complex computational parallelism. The review concludes by identifying key challenges in material compatibility, fabrication complexity, and energy management, while highlighting effective solutions and promising pathways toward scalable, high‐performance neuromorphic hardware for artificial general intelligence.

Diamond holds significant promise for a wide range of applications due to its exceptional physicochemical properties. Investigating the controlled diamond preparation from nanocarbon precursors with varying dimensionalities is crucial to optimize the transition conditions and even elucidate the daunting transformation mechanism, however, this remains outstanding challenge despite considerable effort. Herein, the imperative dimensionality effect of nanocarbon precursors on diamond synthesis and the physical mechanism under high temperature and high pressure is reported, by comparing the distinct transition processes of 0D carbon nanocages (CNCs) and 1D carbon nanotubes (CNTs) from conventional graphite. The optical and structural characterizations evidently demonstrate that both 0D CNCs and 1D CNTs first undergo collapse and graphitization, followed by the formation of mixed amorphous carbon with embedded diamond clusters, eventually leading to cubic diamond. The plotted pressure-temperature diagram exhibits the unique dimensionality effect of carbon nanomaterials to diamond transformation. These results provide valuable insights into the phase transition mechanisms of diamond synthesis and its derivatives under extreme conditions.

The unstable configurations and uncontrollable stoichiometric ratios of atomically-thick one-dimensional (1D) magnets pose challenges for practical applications. Here, we employ a spatially confined domain strategy to obtain 1D vanadium tellurides (VxTey) with distinctive stoichiometry within single-walled carbon nanotubes (SWCNTs). Confined by SWCNTs with different inner diameters, three unconventional air-stable VxTey can be generated: 1D 1H-VTe2, V6Te6, and VTe3. Atomically resolved electron microscopy systematically unveils the conformational distributions of these three phases inside SWCNTs. Density functional theory (DFT) calculations indicate that these diverse VxTey phases exhibit different intrinsic electronic structures, which correspond to ferromagnetic, antiferromagnetic, and non-magnetic properties. Furthermore, the magnetic response and magnetic anisotropy of the 1D VxTey@SWCNTs assembly are experimentally confirmed. This work highlights the preparation of air-stable atomic 1D magnets, offering promising solutions for the design of next-generation spintronic devices.

All‐optical artificial synaptic devices offer promising potential for neuromorphic computing, yet their development is hindered by limited spectral tunability and poor plasticity linearity. Here, a broadband all‐optical synaptic memtransistor based on organic charge transfer cocrystals (DTT‐TCNQ) is reported, which enables fully light‐driven and reversible modulation of synaptic weights across a wide wavelength range (395–808 nm). The device exhibits bidirectional excitatory and inhibitory photoresponses, and achieves highly linear long‐term potentiation and depression (LTP/LTD) characteristics with ultralow nonlinearity (α p = 0.00191, α d = 0.00305) and asymmetry ratio (AR = 0.00114), attributed to a synergistic strategy combining frequency modulation and photoelectric coupling. When integrated into a convolutional neural long short‐term memory network (CNN‐LSTM) hybrid network, the device enables rapid convergence (98.77% accuracy in 6 training epochs) and robust recognition performance under spatiotemporal noise, outperforming conventional light‐write/electric‐erasing schemes. This work bridges material‐level innovation and system‐level functionality, offering a scalable approach toward energy‐efficient, noise‐resilient neuromorphic vision systems.