Hengzhong Fan

As an emerging class of multifunctional materials, high-entropy ceramics (HECs) exhibit a unique combination of properties that have not yet been fully elucidated, enabled by their distinctive high-entropy design concept. These materials provide valuable scientific insights and significant research potential in the field of tribology. This review critically assesses the tribological performance of HECs across six major categories: carbides, borides, silicides, oxides, nitrides, and MAX phases. Comparative studies among different systems reveal that carbides and nitrides have received far more investigation than silicides and borides, reflecting an unbalanced research landscape. While HECs generally exhibit enhanced wear resistance and adaptive lubrication compared with conventional ceramics, the field currently suffers from a lack of standardized tribological data, insufficient mechanistic understanding of the high-entropy effect, and high synthesis costs. Future efforts should prioritize data-driven rational design, multiscale mechanistic modeling, and cost-effective manufacturing strategies to accelerate the transition of HECs from laboratory to industrial applications.

The rational design of metamaterials enables anomalous physical properties, yet achieving target performance remains challenging in ultra‐large optimization spaces. Herein, we propose an intelligent design framework for programmable mechanical metamaterials based on graph neural networks (GNNs). A dataset of programmable mechanical metamaterials was first generated by finite element analysis to provide a diverse design space associating structural topology with mechanical performance. To overcome the limited global receptive field of conventional GNNs, a multi‐scale structure‐aware attention module was developed. Its effectiveness and generalizability were validated across multiple public datasets and benchmark models. By incorporating geometric constraints, the enhanced GNN model achieved high‐fidelity prediction of nonlinear stress‐strain curves with an accuracy of 97.65%. GNNExplainer‐based interpretability analysis further identified critical subgraph structures and node features driving model predictions, thereby improving the transparency and reliability of the proposed framework. In addition, a graph generation strategy based on edge‐feature was introduced to enable the inverse design of metamaterials with user‐defined relative densities and mechanical parameters. The designed architectures were jointly verified by numerical simulations and experimental validation, confirming the practical feasibility of the proposed framework. This work provides a robust and generalizable intelligent pathway for both property prediction and programmable generation of mechanical metamaterials.

Fiber‐based textiles capable of responding to external stimuli are crucial for flexible smart sensors and thermal management, yet they often lack the diverse functionalities required to simultaneously perceive electro‐thermo‐mechanical signals. In this study, polar‐bear‐hair bionic hollow graphene aerogel fibers (GAFs) were controllably fabricated via a scalable coaxial‐extrusion‐spinning process combined with a core‐component‐sacrificial method. Under promotion of shear stress in the outer channel, graphene oxide (GO) nanoplates self‐assemble into a hierarchically porous arch‐like microstructure. Precise regulation of the interfacial status and functional groups on reduced GO endow GAFs and the resulting smart textiles with tunable mechanical, electrical, and thermal properties. The low energy barrier and intense phonon scattering within the multiscale porous network result in the largest electrical conductivity (1457.09 S/m), record‐low thermal conductivity (1.28 mW/(m·K)) and remarkable elastic recovery (90% strain recovery) rather than those of most reported lightweight architectures. The flexible GAFs‐based textiles demonstrate multifunctional capabilities as sensitive wearable sensors based on either piezoresistive or thermoelectric effect, thermal barriers, and infrared stealth skins. This study provides a novel pathway for functional textiles of graphene aerogels in intelligent perception, self‐powered systems, fire alarming sensor, thermal management, and camouflage protection for both civilian and military applications.

The lightweight ceramic aerogels are plagued by thermal instability and mechanical degeneration at extreme conditions. In this study, a high‐entropy oxide ceramic of (Gd _1/2 Lu _1/2 ) ₂ (Ti _1/3 Zr _1/3 Hf _1/3 ) ₂ O ₇ (GLTZH) is prepared through a molecular synthesis route of pyrolytic solid‐solution reactions. The atomic resolution observations visualize the phase transition of polyacetylacetonato metal complexes into a defect‐fluorite structured high‐entropy oxide after thermal treatment at 200 to 1100 °C. The GLTZH oxide demonstrates exceptional crystallographic stability without severe grain growth, and element segregation appeared under prolonged exposure to extremely high temperature (≈1500 °C). This originates from the intricate coupling mechanism among entropy‐driven lattice distortion, high‐entropy stabilization, and orbital hybridization effects. Furthermore, GLTZH‐based lightweight nanofiber aerogel is constructed through electrospinning and followed by thermal annealing at 1000 °C. This architectured high‐entropy ceramic aerogel manifests unprecedented thermomechanical properties, including superelastic compressibility of 98% from −196 to 1500 °C, and thermal superinsulation capacity (24.14 mW·m ⁻¹ K ⁻¹ at room temperature, 81.21 mW·m ⁻¹ K ⁻¹ at 1000 °C). Due to superior performances beyond most conventional ceramic counterparts, the high‐entropy GLTZH paves a new pathway for advanced ceramic aerogel design in thermal insulation across a wide temperature range, such as thermal protection of hypersonic aircraft.

High-entropy boride ceramics demonstrate outstanding high-temperature stability, positioning them as promising candidates for reliable performance in extreme environments. However, their inherent limitation lies in relatively low fracture toughness, coupled with the unclear elucidation of high-temperature tribological behaviors. To address these challenges, high-entropy (Ti_0.2Zr_0.2Hf_0.2Nb_0.2Ta_0.2)B₂ (HEB) ceramics are utlized as the matrix material in the current investigation, while hexagonal boron nitride (h-BN) is introduced as a type of toughening and lubricating phase to develop HEB-hBN composite ceramics. The toughening and high-temperature self-lubrication of the composites are achieved by leveraging the high aspect ratio, lamellar microstructure, and interlayer slip characteristics of h-BN. Results indicate that h-BN enhances the fracture toughness of the composite ceramics by nearly 70%, attributed to the optimization of the crack growth path through its lamellar microstructure and facilitating crack deflection and bridging mechanisms due to its high aspect ratio. Moreover, through interlayer slip effects, h-BN collaborates with B₂O₃ and metal oxides generated from high-temperature oxidation, forming a gradient tribofilm in conjunction with other synergistic lubrication mechanisms. This synergistic interaction results in a nearly 40% reduction in the friction coefficient of the composite ceramics, accompanied by an approximately 60% decrease in wear rate under high-temperature friction conditions at 1000 °C. Under extreme friction environments ranging from 1000 °C to 1200 °C, the composite ceramic maintains a friction coefficient consistently below 0.30, with the wear rate stably sustained at an order of magnitude of 10^-5 mm³/(N·m).

Wearable pressure sensors show significant potential in areas such as health monitoring, electronic skin and human–computer interaction. However, achieving a balance between a wide sensing range and high sensitivity while simultaneously reducing manufacturing costs and simplifying fabrication processes remains a significant challenge for wearable pressure sensors. In this study, pressure sensors based on an MXene/Ag NFs coated cotton fabric (MACF) were prepared by a simple dip-coating technique. The hydroxyl groups in the cotton fabric interact with the hydrophilic groups on the MXene nanosheets, promoting strong adhesion between the two materials and forming a practical conductive pathway. The MACF sensor adopts a sandwich structure, demonstrating excellent performance, including a wide sensing range (0–60 kPa), high sensitivity (1.45 kPa⁻¹, 0–10 kPa), and stable responses over 410 cycles. The working mechanism of the MACF sensor is discussed in detail. Notably, the sensor maintains stable sensing performance under extreme humidity (40–80 RH%) and temperature conditions (20–100 °C). Furthermore, the sensor successfully detects and differentiates various human physiological signals, such as finger movements and wrist pulses. This study presents a simple fabrication method that achieves a balance between sensitivity and sensing range in cotton fabric-based sensors, demonstrating promising potential for applications in flexible wearable devices, electronic skins, and bionic robots.

The rapid dissipation of shear stress and frictional energy in the matrix of polymer-based self-lubricating composites can improve its friction-reduction and anti-wear performances. In this paper, regenerated lignocellulose (RLC) with a flexible architecture was used to assist ball-milling to exfoliate bulk molybdenum disulfide (MoS₂) and introduce them into an epoxy (EP) resin matrix to improve the mechanical and tribological properties of the final products. The abundant functional groups (hydroxyl and aldehyde groups) in RLC have an additional reaction with the active hydrogen atoms or epoxy groups in the epoxy resin, improving the curing performance of the EP matrix and enhancing the flexibility and interfacial strength of the carbon fibers/epoxy composites. Due to the simultaneous introduction of rigid MoS₂ nanosheets and flexible plant-fiber constructs in the EP matrix, external stresses can be transferred from the polymer matrix to reinforcement fibers more efficiently. The tensile strength and toughness of the final products were increased by 42.61% and 53.58%, the friction coefficient and wear rate were reduced by 34.78% and 30.77% over the RLC-EP composites. This approach of using RLC to assisted exfoliate MoS₂ nanosheets and building "flexible & rigid" transition framework in EP matrix provides a valuable reference for improving the interfacial strength and friction properties of polymer-based self-lubricating composites.

High-entropy structure in layered compounds, especially for transitional metal dichalcogenides (TMDCs), has powered the field with disorder and versatile chemical composition, showing great potential in various functional applications, including energy storage, catalysis, and so on. However, the reported high-entropy phases are mainly in 1T phases, and the 2H phases are rare and about 3R phases are still lacking. Here, phase engineering on high-entropy TMDCs is achieved via tuning the chemical composition in (Mo_0.5W_0.5)_1-x(Nb_0.5Ta_0.5)_xSe_2+d, 0 ≤ x < 1, and -0.1 ≤ d ≤ 0.3. The effect of A phase diagram is built, guiding the synthesis of pure 2H/3R phases in a wide composition/entropy range. The increase of VB-group element content and over-dosed Se will facilitate the formation of 3R phases, vice for the 2H phases. Thermodynamic first-principles calculations evaluate the stability of phases in different polytypes and compositions, matching well with the composition-dependent crystalline habits. Moreover, optimized thermoelectric performance with the zT value reached 0.36 @ 723 K in the 2H phase of x = 0.2, attributing to the low thermal conductivity caused by the high-entropy effect, being one of highest among (Mo/W)Se₂-based materials. Our work enriches the high-entropy TMDCs with versatile polytypes, expanding their potential uses for various fields.