Low-dimensional carbon materials, including carbon chains, graphene nanoribbons, and carbon nanotubes, exhibit unique structural features and outstanding mechanical and electronic properties, making them highly promising for applications in nanoelectronic devices and composite materials. Interfacial interactions play a crucial role in determining device stability and material performance. However, the influence of rotational configuration and finite-size effects on the equilibrium interfacial distance remains insufficiently understood. In this work, molecular dynamics (MD) simulations were performed to systematically investigate the dependence of cohesive energy and equilibrium distance on rotation angle in three representative finite-sized low-dimensional carbon systems. The results show that, within the range of structural parameters considered, the equilibrium distance generally decreases with increasing rotation angle, while the extent of this variation depends strongly on system geometry. Specifically, the effect becomes more pronounced with increasing longitudinal size, but weakens with increasing transverse size. When the longitudinal dimension reaches approximately 1000 & Aring;, a noticeable discontinuity in the equilibrium distance may emerge in the small-angle regime. The size dependence is therefore found to be governed primarily by the longitudinal dimension and is particularly significant in systems with relatively large aspect ratios. Cross-validation with a theoretical model based on the Gaussian integration method shows good agreement with the simulation results, with only minor deviations. In addition, key factors affecting interfacial behavior, including lattice length, stacking configuration, rotation center, and nanotube chirality, were identified. Furthermore, nonlinear fitting of the theoretical data using the Levenberg-Marquardt (LM) algorithm yielded an optimized empirical expression that accurately describes the relationship between equilibrium distance and rotation angle. These findings provide a quantitative basis for understanding and regulating interfacial interactions in finite-sized low-dimensional carbon materials.

Hybrid composites have been widely known for their significant advantages in improving carrying characteristics and balancing cost and performance, and bio-inspired designs (e.g., helical structures) can also enhance mechanical properties. However, the combined effects of hybrid and bio-inspired helical ply on mechanical properties remain insufficiently studied. Herein, the hybrid structures and bio-inspired helical ply angles were employed as design variables. The effects of varying hybrid structures and bio-inspired designs on flexural properties were systematically investigated via three-point bending tests, combined with digital image correlation (DIC) and scanning electron microscopy (SEM) to characterize strain distribution and failure modes. The results demonstrate that a specific hybrid layup increased flexural strength and energy absorption by 15.9% and 194% compared with the pure carbon fiber laminate. The double-helical structure significantly enhanced energy absorption maintaining high modulus/strength in contrast with the benchmark hybrid structure. Based on a progressive damage finite element model, which accurately simulated the flexural responses of laminates, Hybrid structure and helical ply angle were synchronously optimized using the Taguchi-gray relational analysis method. The optimized architecture exhibited a 33.5% improvement in flexural performance and a 20.1% reduction in material cost compared with the benchmark design.

Thermal rectification materials, requiring nanoscale asymmetric structures, are essential for enhancing energy efficiency and thermal management. However, their development is hindered by limited tunability, size dependence, and complex manufacturing. To overcome these challenges, this theoretical study employs a nanoscale weaving approach, integrating machine learning and molecular dynamics simulations to design graphene nanoribbon-based asymmetric structures. The resulting infinite periodic woven system eliminates size effects while dynamically tuning the interface coupling strength, constraint ratio, and interlayer distance, achieving precise control of heat flow while preserving the intrinsic material properties of the building units. This strategy is predicted to achieve a thermal rectification ratio of 0.34 2.68, and the control range is 1.8 times higher than that of existing materials. The rectifying woven material is theoretically predicted to enhance the chip heat dissipation efficiency by 50

Fiber Bragg grating (FBG) sensors are widely used in aerospace monitoring and intelligent manufacturing due to their high sensitivity, yet their deployment relies on manual assembly, limiting precision, integration density, and conformality under extreme conditions. Here, we propose a programmable direct-FBG-patterning (DFP) assembly paradigm for one-step integration of intrinsically brittle multiplexed FBG arrays onto arbitrary curved and non-developable surfaces. A mechanics-optics coupled framework identifies three minimum bending radii governed by interfacial debonding, fiber fracture, and optical attenuation, defining the achievable feature size and multidirectional sensing capability. Guided by this framework, cross-fiber routing and conformal assembly strategies enable high-density sensor networks along a single continuous fiber beyond the limits of manual assembly. We further demonstrate robust and versatile FBG sensor integration in applications including structural displacement reconstruction, phonation, and gesture monitoring, establishing a general strategy for quasi-distributed sensing beyond the constraints of manual assembly.

Femtosecond laser machining of silicon carbide (SiC) has attracted widespread attention due to its negligible thermal damage and outstanding machining performance. Although available experiments have confirmed the formation of recast layers arising from molten material flow during femtosecond laser ablation, few numerical models have further touched upon the issue for SiC ablation. This study focuses on the effect of molten flow on ablation morphologies of SiC under femtosecond laser by integrating an improved multi-physics prediction model with experimental characterizations. The evolution mechanisms of optical properties and carrier density, as well as their contributions to final ablation morphologies are revealed. In particular, the improved multi-physics prediction model considering a dual material removal mechanism, that is, evaporation and phase explosion, significantly promotes simulation accuracy. Compared with the average error of 29.4% for the ablation depth between traditional simulations without molten flow and experiments, the error in current simulations considering molten flow decreases to merely 8.56%. In addition, increasing laser oblique incidence angle leads to positional offset of ablation crater center and thicker recast layers at the crater front edge, which agrees well with the results in available experiments. This work provides a deep understanding of ablation mechanisms of femtosecond laser, which is of great importance for advancing femtosecond laser machining techniques.

Real-time dynamic strain monitoring is critical for assessing the structural health of soft and impact-prone intelligent structures, yet it remains challenging due to the difficulty of reliably integrating sensors for internal strain detection. Here, we present an integrated design-to-fabrication strategy based on hybrid 3D printing that combines fused filament fabrication (FFF) and direct ink writing (DIW) to embed strain sensors within soft structures. This strategy enables internal dynamic strain monitoring under impact loading while eliminating adhesive-dependent manual assembly and providing superior physical protection compared with traditional surface-mounted methods. Dynamic strain responses at multiple internal locations are experimentally measured under controlled impact conditions and systematically validated using finite element simulations incorporating a nonlinear visco-hyperelastic constitutive model. The results demonstrate repeatable strain responses under repeated impacts as well as location-dependent strain characteristics that enable impact zone identification. To illustrate the versatility of the proposed framework, several proof-of-concept demonstrations are presented, including impact monitoring in protective helmets, finger-bending detection in wearable devices, hermeticity monitoring in tube furnaces, and multi-zone impact localization. Overall, this work establishes an efficient and mechanically validated framework for integrating sensing functionalities into soft structures under dynamic loading.

Crack growth is a primary failure mechanism in engineering structures, and its real-time monitoring is critical for reliable damage evaluation and life prediction. In this study, a customizable crack monitoring sensor array is developed using direct ink writing (DIW) technology. The sensing concept translates crack-induced mechanical rupture into discrete, stepwise resistance variations, providing a direct and unambiguous electrical signature of crack initiation and propagation in real time. Here, the real-time capability refers to the synchronous electrical response triggered by fracture events, rather than high-speed temporal resolution. Owing to the high design flexibility of DIW, the sensor architecture can be readily tailored in terms of geometry, signal response, and system integration. Experimental validation demonstrates the capability of the proposed sensor to monitor multiple crack modes, including quasi-static fracture, dynamically unstable brittle fracture, fatigue crack growth, and crack-path deflection, across different substrate materials. The results highlight the effectiveness of the DIW-fabricated sensor array as a versatile and scalable platform for event-based, multi-mode crack monitoring in structural health monitoring applications.

Interlayer layup configurations strongly dominate the mechanical properties of hybrid composite laminates. However, the influence of their structural parameters on the microscopic mechanical behavior and failure mechanisms remains unclear. To address this, we design a novel bio-inspired hybrid carbon/glass fiber laminate, where carbon fiber and glass fiber represent the hard and soft phases, respectively. The nonlinear mechanical behavior of 28 different layup types of hybrid carbon/glass fiber laminates is investigated through an integrated approach combining three-point bending tests and finite element analysis (FEA). Real-time monitoring of damage initiation and evolution is achieved using acoustic emission (AE) and digital image correlation (DIC), while a machine learning algorithm decodes AE features to classify damage modes. Corroborated by scanning electron microscopy (SEM), this multi-technique methodology systematically reveals the progressive damage behavior and failure mechanisms of the hybrid composites. Parametric analysis further elucidates the synergistic influence of dispersion degree and hybrid ratio, identifying the [G3C3]2 laminate as the optimal configuration at a dispersion degree of 0.27 and a hybrid ratio of 0.5. Compared to the [C]12 laminate, this design achieves a 32.78% higher flexural strength and a 45.82% greater energy absorption, yielding a superior comprehensive performance index of 0.77. This study establishes a bio-inspired hybrid strategy that provides a foundational framework for designing thin-walled composite structures with tailorable mechanical performance.

A508-III steel is the primary material for nuclear reactor pressure vessel (RPV). Its dynamic fracture toughness is a key indicator for assessing reactor safety under extreme transient loads, which induce pronounced rate dependence in the material's fracture resistance. To investigate the influence of loading rate on the fracture behavior of A508-III steel, this study conducts a series of three-point bending fracture tests. The results show that A508-III steel exhibits not only strain rate dependence but also significant three-dimensional (3D) effect under the same loading rate. To investigate how this 3D constraint influences the dynamic fracture toughness, we developed a Johnson-Cook (J-C) plastic constitutive model and a rate-dependent cohesive zone model (CZM). Using a finite element model (FEM) validated against experiments, we find that during stable crack growth, the fracture energy increases with the loading rate and decreases with the specimen thickness. Further analysis indicates that the out-of-plane constraint factor at the crack tip regulates the plastic zone size and energy dissipation, thereby governing the 3D effect on fracture toughness. This work provides a fundamental understanding and theoretical basis for the dynamic fracture resistance design and safety assessment of RPVs.

Epoxy resins are widely used in energy storage devices, electronic encapsulation, and structural adhesives owing to their excellent adhesion and electrical insulation properties. However, conventional epoxy systems still face challenges related to mechanical robustness and long-term service reliability. Molecular dynamics (MD) simulations provide an effective means to investigate structure-property relationships in crosslinked epoxy networks, yet their application is often hindered by the complexity of curing reactions and the limited efficiency and reproducibility of traditional atomistic modeling workflows. In this work, a lightweight graphical user interface (GUI) was developed to facilitate automatic and user-friendly generation of reaction-mapping templates for reactive molecular dynamics simulations based on the AutoMapper-LAMMPS fix bond/react framework. Using this platform, a ternary epoxy system comprising epoxy resin, curing agent, and a mesogenic comonomer was established. By systematically varying the crosslinking degree and comonomer content, the effects of network topology and local molecular ordering on mechanical behavior were elucidated. The results provide molecularlevel insight into strategies for enhancing epoxy toughness without significantly compromising stiffness and demonstrate a generalizable and efficient methodology for simulating complex crosslinked polymer systems.

To address the challenge of non-equilibrium microstructural control during laser directed energy deposition (LDED) of Inconel 718 superalloy, this study systematically investigated the influence mechanisms of interlayer cooling time on microstructural evolution and mechanical properties. The thermal history under varying interlayer cooling times was systematically investigated through integrated simulation and experimental approaches. Multiscale characterization techniques were employed to elucidate dendrite morphology transformation and grain orientation distribution characteristics, and the quantitative relationship between the cooling rate and the primary dendrite arm spacing was established. The mechanical enhancement mechanism was revealed through room-temperature tensile testing combined with Schmid factor analysis. Experimental results demonstrate that prolonging interlayer dwell time significantly alters thermal history within molten pools, achieving grain refinement and columnar-to-equiaxed transition through coordinated regulation of temperature gradient and solidification rate, while concurrently inducing < 100 > texture weakening. Mechanical testing revealed 12.4 % and 10.8 % strength improvements in scanning direction and building direction respectively when implementing 3-minutes cooling intervals, with fracture surfaces exhibiting dimple-dominated ductile characteristics. Notably, large irregular Laves phases were identified as detrimental to ductility. This research establishes theoretical foundations for in-situ microstructural control in laser additive manufacturing of nickel-based superalloys.

Ultrathin copper (Cu) films are indispensable in micro- and nano-electronic devices but suffer from severe environmental oxidation due to their high surface-to-volume ratio. Here, we report a plasma-induced pre-oxidation strategy that enhances the environmental stability of Cu nanofilms using an ultrathin aluminum (Al) layer with a critical thickness as low as ∼3 nm. Al layers were physically vapor deposited onto Cu surfaces (Cu@Al) and subsequently exposed to low-power plasma treatment (Cu@Al-P), enabling controlled in situ formation of a compact and structurally uniform Al2O3 barrier. Electrical measurements under ambient air, high temperature and high-temperature & high-humidity conditions show that the Cu@Al-P films with 3 nm Al exhibit significantly improved oxidation resistance. Under these conditions, the resistance variation of Cu@Al-P is ∼2.9 times, ∼9.34 times, and ∼5.75 times lower than that of Cu@Al, respectively. Compared to bare Cu, the improvements are even more significant, with Cu@Al-P exhibiting resistance variations that are ∼5.84 times, ∼27.19 times and ∼23.93 times lower. X-ray photoelectron spectroscopy, scanning Kelvin probe microscopy characterization and parallel resistance model calculation confirm that plasma treatment enables the rapid formation of a more uniform and stable Al2O3 barrier, which effectively suppresses oxygen diffusion and stabilizes the electrical properties of the Cu films. This work establishes a critical thickness criterion for ultrathin barrier design and provides a simple, scalable and fabrication-compatible strategy to enhance the environmental stability of copper-based nanoscale electronic systems and other ultrathin metallic nanostructures.

Tube-fin heat exchangers (TFHEs) often face a fundamental constraint between heat transfer enhancement and flow resistance. This study proposes a bio-inspired airfoil fin configuration to improve the balance between heat transfer and flow resistance, enhancing overall thermal-hydraulic performance. An integrated optimization framework combining three-dimensional computational fluid dynamics (CFD), artificial neural network (ANN) surrogate modeling, and a non-dominated sorting genetic algorithm with elite strategy (NSGA-II) is developed to achieve balanced performance. Five key geometric and operational parameters are selected as optimization variables to capture the coupling between fin geometry and flow behavior. Bayesian-regularized neural networks are trained to predict the Colburn factor (j) and friction factor (f) with high accuracy, yielding coefficients of determination (R2) above 0.99. The NSGA-II algorithm identifies Pareto-optimal configurations that maximize heat transfer while minimizing flow resistance. The optimized bio-inspired tube-fin heat exchanger achieves a 16.39% improvement in overall thermal-hydraulic performance compared with the conventional plain-fin model. This work establishes a data-driven framework for the intelligent design and optimization of TFHEs, offering guidance for next-generation high-efficiency thermal management systems.

Mechanical degradation due to repeated impact is generally considered inevitable in layered materials, posing a critical challenge for their durability in extreme environments. To address this, classical molecular dynamics simulations are employed to investigate the response of multilayer graphene, both pristine and with atomic-scale defects, under high-velocity impact loading. The simulations reveal that impact-induced formation of permanent interlayer sp3 bonds occurs at threshold pressures of approximately 72 GPa in pristine graphene, and 35 GPa and 30 GPa in graphene containing 2% and 3% vacancy defects, respectively. These bonds lead to an enhancement of ultimate strength and strain by up to 20% and 60% in defective bilayers, while a reduction of approximately 30% is observed in pristine samples. Further analysis indicates a non-monotonic relationship between sp3 bond density and thermal conductance, with an initial suppression followed by partial recovery as bond density increases. This behavior suggests that the mechanical reinforcement is closely linked to defect-assisted interlayer coupling and associated phonon transport modulation. The findings establish a defect-sensitive strategy for mechanical enhancement in two-dimensional materials and provide a potential pathway for nondestructive defect detection based on thermal transport signatures.

Reverse osmosis (RO), despite its widespread adoption, suffers from high energy consumption and limited water flux, which constrain its scalability for addressing global water scarcity. Recent progress in two-dimensional (2D) materials, such as graphene, has opened new opportunities for next generation desalination membranes owing to their atomic thickness and superior transport properties. In this study, we investigate the desalination performance of graphene-based nanoslit membranes via molecular dynamics (MD) simulations, comparing flexible and rigid membrane models. The flexible configuration exhibits significantly enhanced water permeability and salt rejection, thereby improving overall desalination efficiency. Furthermore, non-equilibrium molecular dynamics (NEMD) simulations were employed to unravel the pressure-dependent desalination efficiency (50-450 MPa), revealing that steric exclusion governed by hydrated ion size dominated the separation mechanism. While increasing external pressure (50-150 MPa) enhances permeability, a decline occurs at 250 MPa. This decline is primarily attributed to the energy penalty associated with hydrogen bond breakage during water transport through the confined slit. In contrast to nanopore-based architectures, this slit configuration bypasses the requirement for intricate etching processes, thereby offering a theoretical foundation for streamlined manufacturing protocols. This study provides molecular-level insights into the rational design of highperformance two-dimensional (2D) membranes and supports the rational optimization of desalination processes from atomic-scale principles to engineering applications.

Additive manufacturing (AM)-enabled embedded sensing structures, owing to their advantages of structure–function integration, internal monitoring capability, and high anti-interference performance, have been increasingly adopted in aerospace, wearable electronics, and soft robotics. However, their realization is not merely a matter of sensor encapsulation. Introducing sensing elements into host structures may disrupt fabrication continuity, create heterogeneous interfaces, distort strain transfer, weaken structural integrity, and compromise long-term measurement reliability. Therefore, achieving robust embedded sensing performance requires the coordinated design of materials, processes, embedding geometries, sensing interfaces, and service conditions. This review systematically examines recent advances in AM-enabled embedded sensing structures from the perspective of reliable measurement and structural integrity. The integration strategies are classified into three major paradigms: post-embedding, print-interruption-enabled embedding, and hybrid AM-based embedding. Their key bottlenecks are further analyzed across four coupled dimensions, namely manufacturing continuity, mechanical integrity, sensing fidelity, and service reliability. The review reveals that the field is evolving from discrete sensor incorporation toward intrinsic self-sensing structures, accompanied by increasing integration density and stricter requirements for material compatibility, interfacial control, and full-lifecycle reliability assessment. By linking embedding strategies with measurement fidelity and structural performance, this review clarifies how sensor integration affects both sensing accuracy and load-bearing integrity, thereby providing guidance for the design of reliable AM-enabled embedded sensing structures in practical monitoring applications.

Hierarchical micro/nanocomposite architectures offer a promising passive strategy for mitigating ice accretion in engineering systems, yet scalable composite designs with quantitatively verified anti-icing performance remain limited. In this study, we design and investigate a hierarchical superhydrophobic surface integrating micro-(10 mu m) and nano-scale (100 nm) polystyrene spheres to achieve enhanced anti-icing performance. Comparative experiments between single-scale microstructured and nanostructured surfaces reveal that the hierarchical surface exhibits a significantly prolonged icing delay time, more than twice that of either single-scale surface. In addition, the critical de-icing force on hierarchical surfaces is reduced by 31% compared with that of nano-structured surfaces, indicating weakened ice-surface interaction and easier ice removal. These superior properties arise from the formation of a stable air cushion and the suppression of heterogeneous ice nucleation induced by dual-scale roughness. Furthermore, the surface maintains its performance after repeated icing/deicing cycles, confirming its mechanical and functional durability. This study provides a mechanistic understanding and a structural design guideline for next-generation passive anti-icing coatings based on interfacial morphology control.

Two-dimensional (2D) materials show advantages as surface-enhanced Raman scattering (SERS) substrates over traditional noble metals for their chemically inert flat surface and uniform SERS signals. However, due to their low density of free electrons, the enhancement mechanism of 2D materials is mainly restricted to chemical enhancement instead of electromagnetic enhancement, resulting in a relatively low enhancement factor. In this work, we report a sensitive 2D SERS substrate based on chemical vapor-deposited MoN nanosheets. The maximum enhancement factor can be 3 x 105 with a limit of detected concentration of 4 x 10-8 M, which is comparable to those noble metal SERS substrates without hot spots. Furthermore, distinct thickness-dependent sensitivity was observed in the MoN SERS substrate; higher sensitivity can be achieved by decreasing the thickness of the nanosheets. The Raman enhancement mechanism is dominated by the surface plasmon resonance of MoN nanosheets, which is evidenced by UV-vis absorption spectroscopy and first-principles calculation. The MoN SERS substrate also shows excellent Raman signal uniformity within the nanosheets, as well as high thermal stability, which can be annealed in air for more than 300 degrees C and maintain its SERS activity. The high stability of MoN nanosheets allows them to be reused for 20 cycles without obvious signal decay. The overall superior properties in terms of high sensitivity, Raman signal uniformity, stability, and reusability make MoN nanosheets an ideal 2D SERS substrate for practical applications.

Cellulose-based triboelectric nanogenerators (TENGs) are increasingly studied as potential candidates for advancing sustainable wearable electronics due to their biodegradability, self-powering capability, and high sensitivity. However, the near-electroneutrality of cellulose and its lack of efficient charge storage sites result in rapid charge dissipation. This study's synergistic approach of constructing deep traps and built-in electric fields effectively promotes charge trapping. This approach achieved nearly 2 orders of magnitude improvement in the deep-trap density of the modified cellulose and a 74% reduction in the charge dissipation rate, compared with cellulose, yielding a charge density as high as 332 μC/m2, comparable to the output produced by the ion injection. The integrated TENG demonstrates reliable and high-sensitivity signal transmission as a wearable electronic device. This study presents a simple and scalable strategy for fabricating high-performance cellulose-based TENGs, underscoring the significant potential of cellulose in sustainable self-powered wearable electronics.

Hybrid additive subtractive manufacturing combines the advantages of additive manufacturing and subtractive machining, however the trigger timing of additive subtractive alternate manufacturing (ASAM) needs to be further investigated. This study investigates the microstructural regulation and mechanical performance enhancement mechanisms of ASAM Inconel 718 superalloy by different interlayer milling strategies and reveals the connection between microstructure and mechanical properties. An in-situ observation method was first employed to optimize the Z-axis lifting parameter in laser directed energy deposition (LDED). Subsequently, through comparative analysis of different hybrid processing strategies and conventional LDED, the thermomechanical effects of interlayer milling on phase composition, grain evolution, and mechanical properties were systematically evaluated. The results show that ASAM enhances cooling rates through alternating processing, facilitating the transformation of coarse Laves phases into refined granular structures alongside grain refinement. The solid-solution is the major strengthening while the secondary strength comes from the microstructure effects (dislocations, etc.). Coordinated dislocation-twin interactions and slip band transmission across granular phases contribute to superior strength-ductility synergy in ASAM specimens. This research provides theoretical foundations for multiscale performance optimization in complex structural components.