Advanced Engineering Materials最新文献

Water-based paints commonly used on exterior surfaces, such as those in marine environments, swimming pools, transportation, railings, and construction, often exhibit poor mechanical strength and antimicrobial efficacy. This study presents an innovative approach to enhance these properties by incorporating hollow ceramic microcore (HCM) microadditives derived from fly ash waste. A core-shell fabrication technique is employed to produce HCM@TiO₂ microadditives, where the mullite (Al₆Si₂O₁₃) based HCM core is encapsulated with a shell of anatase-phase titania (TiO₂). Adding 4 wt% of these microadditives to the water-based paint significantly improve its properties, including a 90% reduction in bacterial growth and notable enhancements in creep resistance and hardness. Mechanical testing demonstrate a 9.14% increase in hardness (from 0.288 to 0.317 GPa) and a 22.67% increase in reduced modulus (from 6.589 to 8.516 GPa), along with improved creep resistance. Surface characterization show that the areal roughness (Sa) increased from 0.198 to 0.275 μm, promoting stronger interlocking, while gloss values decrease moderately from 71.1 to 60.0 GU, indicating a trade-off between durability and optical finish. These findings highlight the dual functionality of HCM@TiO₂ as a sustainable additive that simultaneously improves antibacterial efficacy and mechanical resilience, demonstrating a promising pathway for waste valorization and next-generation eco-friendly coatings.

The cryogenic neuromorphic computing (NC) system is considered a potential future of computing due to its low energy consumption and high parallel computation power when combined with quantum computing (QC). However, the electronic synapse in this system, which serves as the memory function, should be positioned as close as possible to the QC to store the information generated by the QC, and it must operate effectively at cryogenic temperatures. In this work, the properties of the LiCoO₂ (LCO) electronic synapse at both cryogenic and room temperatures have been thoroughly investigated. The Li-ion nanoreservoir, Al-rich LiCoO₂ (LACO), is believed to perform three major functions: stabilizing the Li ions, decreasing the interfacial electric field, and reducing the leakage current. Additionally, cryogenic temperatures slow down Li-ion and electron diffusion, enhancing the influence of the electric field. As a result, the bottom electrode and temperature factors improve the performance of the LCO electronic synapse in terms of memory window (from ≈1.6 to ≈423), linearity (long-term-potentiation linearity from 3.89 to 0.97, long-term-depression linearity from −4.56 to −3.58), and spike-time-dependent plasticity (STDP) characteristics (a twofold improvement in the STDP window and a 1.5-times faster spike response time).

Magnetoactive metamaterials (MMs) represent a cutting-edge class of smart materials that integrate magnetoactive material with architected mechanical metastructures, enabling dynamic control over their mechanical, acoustic, and elastic properties through the application of external magnetic fields. This review presents an in-depth summary of recent progress in MMs, emphasizing their design strategies, manufacturing methods, and wide-ranging applications in areas like biomedical devices, soft robotics, and adaptive structures. The study particularly explores the integration of magnetoactive soft composite materials with mechanical metamaterials, highlighting their ability to achieve tunable physical and mechanical property changes, shape morphing, and wave manipulation. Key fabrication methods, including 3D/4D printing and conventional molding techniques, are discussed, emphasizing their role in creating complex, functional architectures. Additionally, the influence of embedded hard and soft magnetic particles on the performance of MMs made of soft elastomeric matrix is examined, emphasizing their role in achieving contactless actuation, rapid response, and multifunctionality. The review concludes with future research directions, advocating for the integration of machine learning techniques for optimized metamaterial design. The review may serve as a valuable resource for researchers and engineers aiming to harness the potential of these advanced adaptive materials for next-generation technologies.

The tribological behavior of thermo-responsive poly(N-isopropylacrylamide) (PNIPAAm)-based microgels is investigated for use as water-dispersible lubricant additives. Two types of microgels are synthesized using a surfactant-free emulsion polymerization method: MG0, consisting of pure PNIPAAm with a volume phase transition temperature (VPTT) of ≈33 °C, and MG16, consisting of PNIPAAm copolymerized with hydrophobic tert-butyl acrylamide, exhibiting a lower VPTT of around 23 °C. Swelling and lubrication performance are evaluated at 20 and 40 °C. Both microgels significantly reduce friction and wear compared to water alone. At 20 °C, MG0 remains fully swollen and provides effective wear protection through hydrated microgel lubrication. MG16, being near its VPTT, exhibits partial collapse and slightly higher wear. At 40 °C, MG16 demonstrates improved wear resistance, attributed to enhanced film compaction in the collapsed state. Raman spectroscopy and scanning electron microscopy–energy-dispersive X-ray spectroscopy confirm that carbon-rich tribofilms are formed via tribochemical reactions. MG0 produces more graphitic films, while MG16 generates amorphous carbon structures. These findings highlight the tunability of microgel composition for designing adaptive, water-based lubricants for temperature-sensitive applications.

High-entropy alloys (HEAs) represent a frontier in materials science, offering many promising features suitable for high-demand applications in nuclear and space sectors, such as exceptional mechanical properties. However, a major challenge in these fields is accurately predicting the behavior of HEAs under extreme conditions, such as radiation exposure or elevated operating temperatures, in order to maintain the integrity of the materials. Machine learning (ML) provides powerful tools to address this challenge. ML techniques, including ML interatomic potentials (MLIP), enable the modeling and prediction of complex behaviors in HEAs. This review focuses on ML to enhance the understanding of phase stability, mechanical properties, and radiation damage prediction in these complex alloys. The potential of ML to accelerate the discovery/optimization of new HEA compositions with good performance under extreme conditions is also discussed. Ultimately, the aim is to highlight the transformative role of ML in the field of HEAs under extreme conditions, in light of developing novel materials suitable for harsh environments.

Oxide Dispersion Strengthening Effect

The image shows the fracture surfaces of a 3 mm standard tensile test sample. 19 layers of 100 µm stainless-steel-sheets are diffusion bonded. Ten of them are coated with submicron alumina on both sides, and separated by uncoated layers. Partial failure and tearing out of a layer in the middle can be detected. At the perimeter of the tensile test sample, starting failure of several layers is visible. More information can be found in the Research Article by Thomas Gietzelt and co-workers (10.1002/adem.202500616).

Localized Electrochemical Deposition

In their Research Article (10.1002/adem.202500718), Wanfei Ren, Jinkai Xu, and co-workers develop a copper–nickel core-shell reinforcement structure. The deposited nickel shell layer significantly reduces the defects generated during the deposition of the copper core structure. After conformal deposition, the Cu–Ni core-shell structure significantly enhances the mechanical properties of the copper. Compared with the pure copper structure, the mechanical properties are improved by 124%, indicating a significant strengthening effect, while the adaptability to actual environments has also been enhanced.

Puncture testing on carbon steel plates is performed at varying velocities with and without composite metal foam (CMF) buffer panel using numerical and experimental approaches. CMF is a lightweight material made with airtight hollow spheres embedded inside metallic matrix. In this study, three numerical modeling approaches are utilized for CMF panel located between puncture head and carbon steel plate. First, homogeneous CMF framework is utilized without entrapped air in model. The nonhomogeneous CMF framework is then extended, but leads to nonconclusive results due to extra-ordinary resources required owing to the size and complexity of CMF. Finally, a new nonhomogeneous numerical model is developed to incorporate air using fluid cavity to reduce computation time while maintaining accuracy. The specific energy absorbed via CMF panel in experimental approach compared to that of fluid cavity model and carbon steel plate model are 28.14, 32.57, and 0.60 J g⁻¹, respectively. This study validates the applicability of fluid cavity model to accurately predict the performance of complex CMF under puncture against experimental testing. Higher energy absorption associated with CMF underscores its significance in preventing puncture in carbon steel plate. It is concluded that lightweight steel CMF can absorb puncture and impact energies more efficiently than heavy solid steel.

The tribological properties of epoxy-sealed 8YSZ (8YSZ-RE) coating in dry friction, deionized water, 0.1 mol L⁻¹ (m) HCl, and NaOH solutions are comparatively studied. Its microstructure is characterized by scanning electron microscope (SEM), X-ray diffractometer, and field emission scanning electron microscope. The friction and wear behaviors under various environments are conducted using a CSM ball-on-disc tribometer. The morphologies and mechanical properties of the worn surfaces are analyzed via SEM, 3D profilometer, nanoindentation, and optical microscope. The corrosion resistance of 8YSZ-RE coating in 0.1 m HCl and NaOH solution is studied using by electrochemical corrosion tester. Results indicate that 8YSZ-RE coating exhibited superior tribological performances in HCl and NaOH solution compared to deionized water and dry friction, mainly due to hydroxide layer formation on the worn surface. The liquid medium can remove the fine wear debris generated during friction, making it hard to gather on the worn surface. The friction coefficients of 8YSZ-RE coating in NaOH and HCl solution are 0.26 and 0.14, respectively. The friction coefficient under dry sliding reaches a maximum of 0.66. The tribological performances of 8YSZ-RE coating are best in a 0.1 m HCl solution. This is attributed to the presence of a hydrodynamic lubrication layer and its strong corrosion resistance.

Ultrasonic vibration effectively reduces porosity in laser directed energy deposition by altering molten pool dynamics. To understand the interactions between bubble evolution and ultrasonic vibration (UV)-induced melt flow, experiments utilizes 1Cr12Ni3MoVN powders containing hollow particles, enabling statistical pore analysis. Multiphysics modeling reveals that UV-generated inertial forces dominate melt flow, competing with Marangoni forces. This interplay accelerates melt circulation, generating localized pressure sufficient for cavitation. Bubble behavior depends critically on size: Small bubbles (less than 20 μm) collapse due to cavitation; medium bubbles (20–50 μm) gain upward velocity and escape pool edges; large bubbles (100 μm) remain trapped at the pool bottom, deformed by shear forces. Crucially, UV treatment significantly enhances mechanical properties: yield strength rises from 905 to 984 MPa, tensile strength from 1283 to 1310 MPa, and elongation improves by 31%. Unlike traditional remelting (which increases elongation but reduces strength), UV method concurrently improves strength and ductility. This approach offers a promising pathway for enhancing the reliability of additively manufactured components.