High-entropy alloys (HEAs), which are near-equimolar alloys of four or more metal elements, have long been used to achieve the desired properties of catalytic materials. However, a novel alloying approach that includes multiple principal elements at high concentrations to generate HEAs as novel catalytic materials has been reported. The fabrication of well-defined ultrastable supported HEAs, which provide superior performance and stability of catalysts owing to their augmented entropy and lower Gibbs free energy, remains a critical challenge. Supported HEA catalysts are sophisticated because of the variety of their morphologies and large sizes at the nanoscale. To address these challenges, PtPdInGaP@TiO₂, comprising five different metals, is prepared via ultrasonic-assisted coincident electro-oxidation–reduction precipitation (U-SEO-P). The electronic structure and catalytic performance of HEA nanoparticles (NPs) are studied using hard scanning transmission electron microscopy (STEM), which is the first direct observation of the electronic structure of HEA NPs. This research takes an important step forward in fully describing individual HEA NPs. Combining STEM with deep learning with convolutional neural network (CNN) of selected individual HEA NPs reveals significant aspects of shape and size for widespread and commercially important PtPdInGaP@TiO₂ NPs. The proposed method facilitates the detection and segmentation of HEA NPs, which has the potential for the development of high-performance catalysts for the reduction of organic compounds.

The realization of intelligent tactile perception in robotic systems requires multifunctional sensors capable of mimicking the dual-mode sensing mechanisms of human skin. Herein, we present a biomimetic hydrogel-based sensor capable of dynamic tactile detection through triboelectric sensing and static pressure monitoring via ionic-supercapacitive sensing. The triboelectric unit achieves a peak voltage of 14.64 V, with <5% signal decay over 5000 s of cycling, enabling robust detection of transient interactions (e.g., tapping or sliding). Additionally, the ionic-supercapacitive unit exhibits a high sensitivity of 2.69 kPa^-1 between 0.8–28 kPa, a rapid response time of 0.5 s, and minimal signal drift of <5% during 7-day continuous operation, providing stable monitoring of static interactions (e.g., touching or pressing). By leveraging a multilayer perceptron neural network, a robotic hand equipped with a biomimetic hydrogel-based bimodal sensor demonstrates intelligent recognition of material types and hardness levels with a high accuracy of 98.5%. This study establishes a paradigm for high-performance electronic skins, which advances human-machine interfaces and artificial intelligence-driven robotics through biomimetic tactile perception.

Different from traditional software encryption, hardware encryption shows obvious advantages in AI information encryption application scenarios with high reliability and high security requirements. With the development of memristors, memristor-based hardware encryption attracted the interests of researchers in secure communication. Hafnium-based memristors have received widespread attention due to fast speed, low power consumption, and compatibility with CMOS technology. In this study, a HfAlO_x-based memristor with an ON/OFF ratio of >10⁴, an endurance characteristic of 10⁵ cycles, and a low operating voltage of 0.56 V/-0.135 V was proposed. Eight-level states were achieved and used to design a hardware encryption scheme through a neural network. Parallel information encryption operations of “S” “D” “U” were realized in a memristor array. By constructing an artificial neural network, the recognition rate of encrypted letters without/with memristor is 62.3% and 98.1%, respectively. The memristor-based encryption scheme further expands the choices and application prospects of hardware encryption.

The precise construction of dual active sites has been uncovered for the electroreduction of C- and N-based precursors to synthesize urea. However, these strategies often face adsorption scaling constraints and spatial restrictions that hinder C–N coupling, resulting in suboptimal activity and selectivity. Here, we showcase a dynamically reversible evolution between vicinal Fe/Cu diatoms and alloy-like Fe–Cu sites, enabling cascade protonation and efficient C–N coupling. This approach markedly enhances urea electrosynthesis from CO₂ and NO₃^-, achieving an ultrahigh urea yield of 2421.2 μg h^-1 mg^-1, Faraday efficiency (FE) of 70.4%, and C-selectivity of 96.7%, surpassing state-of-the-art dual-site electrocatalysts. Operando spectroscopy and theoretical calculations reveal that neighboring Fe/Cu diatoms facilitate the selective adsorption and hydrogenation of NO₃^- and CO₂ into the key intermediates (*NO and *CO). Furthermore, alloy-like Fe–Cu sites, formed in situ due to declined metal surface free energy driven by electron transfer, facilitate C–N coupling and subsequent protonation to selectively produce urea, while dynamically reverting to vicinal Fe/Cu diatoms. This work provides new insights into the relay catalytic strategy for urea electrosynthesis by modulating the dynamic atomic-scale evolution of active sites.

Layered vanadium-based oxides have emerged as promising cathode materials for aqueous zinc-ion batteries (AZIBs) owing to their high theoretical capacity, multivalent vanadium species, and low cost. However, their practical development has been hindered by limitations such as narrow interlayer spacing and structural instability. To address these challenges, we successfully generated oxygen vacancies by a one-step hydrothermal method, and simultaneously inserted benzyltrimethylammonium organic cations (TMBA⁺) into the interlayers of V₂O₅ to obtain a VOH-TMBA⁺ composite electrode material, realizing the dual-strategy modification of V₂O₅. In the resulting VOH-TMBA⁺, oxygen vacancies and TMBA⁺ synergistically expand the interlayer spacing from 6.84 to 13.8 Å, stabilize the layered framework, and modulate the local atomic coordination and electronic structure. This “structural-electronic” dual regulation endows VOH-TMBA⁺ with a high specific capacity of 417.2 mAh g^-1 at 0.2 A g^-1 and exceptional cycling stability (90.7% capacity retention after 7000 cycles at 10.0 A g^-1). In-situ XRD/Raman and ex-situ XPS/SEM characterizations clarify that the VOH-TMBA⁺ electrode is an energy storage mechanism based on H⁺/Zn²⁺ co-insertion/extraction. Furthermore, density functional theory calculations demonstrated that the conductivity of VOH-TMBA⁺ is further enhanced, while the reduction of electrostatic interactions facilitates the transfer of Zn²⁺. This work provides a generalizable strategy for engineering layered metal oxides through collaborative structural and electronic modulation, offering perspectives for designing high-performance cathode materials in AZIBs.

Heterostructure fillers are crucial for enhancing the electromagnetic interference (EMI) shielding performance of composites, and the core lies in the regulation of their morphology. Inspired by the radial structures on marimo surfaces during growth, we propose a bioinspired heterostructure assembly strategy to fabricate novel marimo-like hollow spherical reduced graphene oxide (hs-rGO)@nickel-catalyzed nitrogen-doped carbon nanotubes (Ni-NCNTs) and their corresponding polyimide aerogels. Benefiting from the synergistic design of multilevel porous architectures formed by the hollow microspheres in combination with the aerogel matrix, as well as radially aligned Ni-NCNTs epitaxially grown on hs-rGO surfaces, the resulting aerogels exhibit exceptional EMI shielding effectiveness, reaching up to 68 dB. Finite element simulations further elucidate the shielding mechanisms. Additionally, these aerogels exhibit rapid, durable pressure-sensing performance due to their excellent resilience and conductivity. The multifunctional combination of high-efficiency EMI shielding and mechanical sensing highlights their promising potential in next-generation intelligent electronics, aerospace systems, and advanced communication technologies.

Materials with circularly polarized (CP) lasing, featuring optical rotatory power, are attractive for various advanced optical, sensing, biological, and medical applications. In this context, chiral perovskites attract great attention owing to their superior chiral opto-electronic-magnetic properties. However, the trade-off between coherence and circularity hinders lasing in low-dimensional perovskites. Herein, we invent a novel strategy to realize three-dimensional perovskites with high chirality featuring CP laser. The strong intramolecular interaction between chiral acid and inorganic framework via ionic bonding leads to intense electronic interaction and orbital hybridization, resulting in higher asymmetric and efficient CP emission than most low-dimensional perovskites and an amplified asymmetric factor of up to 0.054 at up-threshold excitation. This pioneering advancement heralds a new era for intramolecular interaction-based stable perovskite, as well as other low-dimensional materials, opening avenues for unprecedented applications in stable-perovskite/chiral optoelectronics and beyond.

Reservoir computing (RC) presents a computationally efficient alternative to conventional recurrent neural networks (RNNs) for temporal-data processing. Traditional bio-inspired auditory systems often face constraints due to limited computational power and high energy consumption, which impede speech-recognition accuracy. In this work, we demonstrate high-performance ferroelectric neuromorphic devices based on TiN/WO_x/Hf_0.5Zr_0.5O₂ (HZO, 4 nm)/TiN heterostructures for constructing an artificial auditory nervous system for efficient voice recognition. The device exhibited a high remanent polarization (P_r) of approximately 20.58 μC cm^–2 at 1.8 V and endurance exceeding 10¹⁰ cycles. Density functional theory calculations and experiments confirm that the WO_x interlayer regulates oxygen vacancy formation and migration within the HZO layer. By emulating essential biological synaptic plasticity functions, such as paired-pulse facilitation and long-term potentiation/inhibition, the ferroelectric tunnel junction-based devices can perform signal processing and neural computation within the RC framework, achieving an accuracy beyond 99% across 12 categories of everyday vocabulary voice words. These findings provide a promising pathway for developing highly reliable and energy-efficient neuromorphic artificial auditory systems.

High-entropy oxides (HEOs) are complex oxides with a single-phase crystal structure that contains five or more principal metal cations in their lattices. The multiple elements doping and configurational entropy stabilization could bring many beneficial effects, such as improved high-temperature phase stability, ionic conductivity, and surface reactivity. Consequently, HEOs have novel prospects for the systematic design of functional oxides for diverse applications with enhanced performance. Conducting oxides, which are conductive for electrons or certain kinds of ion(s), are of particular interest among the various oxide materials. They are key materials in many electrochemical energy conversion and storage devices, such as electrodes for lithium-ion batteries, electrolytes for solid-state batteries (SSBs), air electrodes, and electrolytes for solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs). The conductivity, stability, electrocatalytic activity, and ion storage capability of these conducting oxides determine the practical use of the corresponding devices. During the past, considerable research has been conducted towards the application of HEOs. Thus, this review seeks to provide an intensive, critical, and accessible summary of HEOs and their influence over a wide temperature range, highlighting the role of entropy-driven phase stabilization and multiple elements doping that support their distinctive characteristics. This review also rigorously delves into the core mechanisms that affect their functionality and hinder their broader implementation. It connects essential insights with practical aspects, detailing innovative strategies for conducting HEOs design and exploitability, and establishing a roadmap to expedite their shift from laboratory research to industrial applications in sustainable energy systems.

Lithium–sulfur (Li–S) batteries are promising candidates for next-generation energy storage systems, but practical use is limited by polysulfide (PS) shuttling and Li metal anode instability. Lithium nitrate (LiNO₃) is widely used to mitigate these issues; however, its interfacial effects across the anode, electrolyte, and cathode during operation are not fully understood. Here, operando optical microscopy with a custom side-by-side cell enables simultaneous monitoring of the Li anode, liquid electrolyte, and sulfur cathode in a single field of view under conditions with and without LiNO₃. In the absence of LiNO₃, the Li surface undergoes rough stripping and fragmented, non-coalescent deposition, accompanied by PS-induced corrosion and accumulation of parasitic byproducts at the anode-electrolyte interface. Redness Intensity (RI), introduced to quantify electrolyte-phase PS dynamics, indicates sustained transport toward the anode and delayed conversion to elemental sulfur. By contrast, LiNO₃ induces uniform Li stripping and the growth of aggregated, interconnected deposits, while mitigating PS crossover and promoting efficient sulfur crystallization at the cathode. Complementary SEM-EDS, UV–vis, XPS, TXM, and CT analyses corroborate these observations. By elucidating the multifunctional role of LiNO₃, this study clarifies the interfacial dynamics that govern Li–S battery performance.