Hasil untuk "Materials of engineering and construction. Mechanics of materials"

Yi Zhou, Ying Wang, Zheng-Ming Huang

The accumulation mechanisms and spatial distribution of debonding damage in fiber-reinforced polymer composites under combined tension and shear loading are not well understood. To tackle this research gap, X-ray computed tomography has been utilized to observe the debonding damage between epoxy and CFPR rods in cruciform specimens. An in-situ loading rig was used to perform tension tests, with different combined stresses applied by varying the orientation of the embedded reinforcements. After loading, the damage volumes have been extracted and visualized in 3D to comprehensively examine the spatial distribution of debonding. The in-situ and ex-situ experimental results confirm that the distribution and propagation of interface cracks are largely dependent on the ratio of tension to shear and the position of adjacent reinforcements. Additionally, critical parameters such as debonding angles, crack opening displacements, and deflecting angles have been quantitatively analyzed. Based on the results, the authors proposed three representative models to describe the dimensional properties of interface cracks, and an empirical formula that reveals the positional correlation between interface cracks on adjacent reinforcements. Considering the universality of combined loading in engineering applications, a full understanding of the resulting debonding damage can significantly contribute to the optimization of composite design methodologies.

Stephen M. Budy, Scott T. Iacono

Abstract The development of conductive polymer composites is critical for advancing low-cost, flexible electrochemical sensors. In this study, carbon black-polyvinylidene fluoride (CB-PVDF) composites were fabricated using various processing methods, extrusion, injection molding, spin coating, and solution casting, to investigate the relationship between morphology, crystallinity, and electrochemical performance in sensor applications. Structural integrity of the PVDF matrix was confirmed via NMR spectroscopy, with FTIR and Raman analyses revealing the presence of multiple crystalline phases and minor spectral shifts due to carbon black addition. Thermal analysis via TGA and DSC showed high thermal stability across all composites, with degradation temperatures remaining above 430 °C and crystallinity varying by processing method. Scanning electron microscopy (SEM) revealed significant differences in carbon black dispersion, with solution-processed films demonstrating more uniform distribution compared to thermally processed samples. Electrochemical sensor analysis using cyclic voltammetry (CV) indicated that sanded extruded CB-PVDF fibers exhibited the highest electroactive surface area (23.8 m2/g) and the most consistent redox activity across a range of solvents, outperforming injection molded and solution cast films. These results highlight the critical role of processing in tailoring composite properties and identify sanded extruded CB-PVDF fibers as a promising platform for high-performance electrochemical sensor applications.

Xianwei Ren, Weizheng Liang, Ying Zhang et al.

Herein, we investigate the effect of stress-aging on the precipitate characteristics, grain structure, dislocation evolution, and mechanical properties of an Al–10Zn–3Mg–2.5Cu (wt.%) alloy. Stress aging was performed at 120 °C for 24 h under applied stresses of 135–450 MPa, which considerably enhanced the tensile strength to 700 MPa and resulted in dislocation multiplication as the dominant strengthening mechanism. However, the alloy ductility was constrained to 3.7 %–5.4 %. The stress-aged specimens with 270 MPa and 450 MPa were subjected to artificial aging (140 °C–160 °C). This considerably enhanced the strength–ductility synergy, endowing a tensile strength of 700 MPa along with elongations of 9.1 % and 6.5 %. ε-CuZn4 precipitation was facilitated by synergistic high-alloying and dislocation effects. This rare phase effectively suppressed the coarsening of η-phase, thereby preserving the intrinsic strength, while its superior capability to trap and accumulate dislocations significantly enhanced the ductility. Thus, high-stress aging and thermal treatment offered transformative phase-transformation pathways, distinct from conventional η-phase evolution, making it ideal for fabricating high-strength Al–Zn–Mg–Cu alloys.

Hang Wang, Shihua Ma, Weijiang Zhao et al.

Recent investigations indicate that high entropy alloys (HEAs) may exhibit distinctive thermal characteristics compared to traditional alloys. Through a blend of experiments and atomistic simulations, this study showcases that the lattice thermal conductivity of a highly distorted single-phase B2 alloy with the composition of (CoNi)50(TiZrHf)50 is as low as less than 1 W/(m·K), akin to that of ceramics like alumina, and remains stable across temperatures from 300 to 900 K. This remarkable thermal behavior is attributed to significant lattice distortion and atomic mass variation within this alloy. These findings suggest potential applications for distorted HEAs in thermal insulation technologies tailored for challenging environments.

Zhongsheng Yang, Songyu Wang, Rongtian Cao et al.

Partially recrystallized precipitation-hardened high entropy alloys have shown excellent mechanical properties with yield strength up to 2 ​GPa. However, the recrystallization kinetics of precipitation-hardened high entropy alloys are not adequately revealed due to the complex interactions between precipitation and recrystallization. Here, we report an abnormal recrystallization behavior of precipitation-hardened high-entropy alloys that the recrystallization kinetics at 600 ​°C are faster than those at 800 ​°C. Moreover, the recrystallization period at 800 ​°C is significantly shorter than that at 600 ​°C. Through comparative analysis, we attributed the abnormal recrystallization kinetics to the delayed pinning effect caused by slow precipitation kinetics at 600 ​°C while the shortened recrystallization period to the high recrystallization nucleation rate by particle-stimulated nucleation at 800 ​°C.

Lulu Li, Xidi Sun, Yuchen Guo et al.

Abstract Conductive hydrogels are a class of multifunctional composites constructed by introducing conductive components into a three‐dimensional polymer network, combining the high water‐content, stretchability, and biocompatibility of traditional hydrogels. In recent years, researchers have developed stimuli‐responsive conductive hydrogels (SRCHs) through molecular functionalization design, which can respond to external stimuli such as mechanical stress, temperature, pH, light, electric field, etc., and realize electrical signal output or mechanical behavior modulation, so as to satisfy the requirements of smart devices for dynamic sensing and active response of materials. Thanks to the synergistic effect of active environmental responsiveness and electrical conductivity, SRCHs show a broad application prospect in smart sensing and actuation. However, due to the complexity of the environment, it is still difficult to utilize SRCHs materials to construct sophisticated smart devices. This paper systematically reviews the progress of SRCHs in material design and smart sensing and actuation applications in the past five years, focuses on their stimuli‐responsive mechanisms and performance optimization strategies, and summarizes the current challenges and future development directions, with a view to providing theoretical references and technological inspirations for the development of next‐generation smart materials.

Chu-Liang Fu, Mouyang Cheng, Nguyen Tuan Hung et al.

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the curse of dimensionality. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

Francesco Monticone, N. Asger Mortensen, Antonio I. Fernández-Domínguez et al.

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials--metamaterials and metasurfaces--can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward--toward new scientific discoveries and technological advancements.

Po-Yao Chang

Topological nodal-line semimetals (NLSMs) are a new family of topological materials characterized by electronic band crossings that form lines in the Brillouin zone. These NLSMs host exotic nodal-line structures and exhibit distinct features such as drumhead surface states and unique electromagnetic responses. This review classifies various NLSM types based on their nodal structures and protecting symmetries, highlighting that these nodal-line structures can form links, knots, and chains. We discuss their characteristic electromagnetic responses, including Landau level spectroscopy, optical conductivity, and permittivity. Furthermore, the strong correlation effects in these NLSMs modify their semimetallic phases and lead to novel quantum phases where magnetism and superconductivity intertwine.

Teddy Koker, Mit Kotak, Tess Smidt

Foundation models for materials modeling are advancing quickly, but their training remains expensive, often placing state-of-the-art methods out of reach for many research groups. We introduce Nequix, a compact E(3)-equivariant potential that pairs a simplified NequIP design with modern training practices, including equivariant root-mean-square layer normalization and the Muon optimizer, to retain accuracy while substantially reducing compute requirements. Nequix has 700K parameters and was trained in 100 A100 GPU-hours. On the Matbench-Discovery and MDR Phonon benchmarks, Nequix ranks third overall while requiring a 20 times lower training cost than most other methods, and it delivers two orders of magnitude faster inference speed than the current top-ranked model. We release model weights and fully reproducible codebase at https://github.com/atomicarchitects/nequix.

Jingqiu Zeng, Kaiqian Zhang, Weiming Sun et al.

Matan Mayer

Recovery and re-utilization of materials are regarded as key strategies for reducing greenhouse gas emissions in the built environment. Within those end-of-use scenarios, recycling is one of the widely used tactics, demonstrated by established infrastructure and developed supply chain networks in many geographic locations. While recycling is an increasingly common practice in the built environment, accurately defining recycling quality in order to compare technologies and material types remains methodologically contested. This is mainly due to the vast spectrum of scenarios that typically fall under the term ‘recycling’. Remanufacturing, downcycling, upcycling, and even direct reuse are all referred to as types of recycling in non-scientific circles, depending on the sector they occur in. The main challenge in assessing the material recovery quality of those solutions is that they exist on a continuum without clear divisions. Within that context, this article presents and compares four methods for assessing recyclability. The featured methods measure recycling potential from different perspectives: economic dimensions of the recycling industry; patterns of resource depletion; the energy cost of recycling; and the carbon intensity of recovery processes. The scientific foundations of the four methods are presented and a range of widely used construction materials are tested. The performance of materials is then compared across the four assessment methods to note observations and gain insights. Some of the materials are found to consistently outperform others, whereas some materials perform well on one method while performing poorly on others. This comparative study is followed by a discussion that looks at the limitations of each approach and reasons, or lack thereof, for the adoption of one method over the others in industry and academia. Lastly, the article looks at future research trajectories and examines the path ahead for recycling in the construction industry.

Hanzhong Liu, Yongda Yan, Jiwen Cui et al.

Micro diamond tools are indispensable for the efficient machining of microstructured surfaces. The precision in tool manufacturing and cutting performance directly determines the processing quality of components. The manufacturing of high-quality micro diamond tools relies on scientific design methods and appropriate processing techniques. However, there is currently a lack of systematic review on the design and manufacturing methods of micro diamond tools in academia. This study systematically summarizes and analyzes modern manufacturing methods for micro diamond tools, as well as the impact of tool waviness, sharpness, and durability on machining quality. Subsequently, a design method is proposed based on the theory of cutting edge strength distribution to enhance tool waviness, sharpness, and durability. Finally, this paper presents current technical challenges faced by micro diamond tools along with potential future solutions to guide scientists in this field. The aim of this review is to contribute to the further development of the current design and manufacturing processes for micro diamond cutting tools.

Weijun Liu, Zhixiang Liu, Shuai Xiong et al.

The uniaxial compressive strength (UCS) of the backfilling body plays a crucial role in backfilling safety. To study the feasibility of preparing new cementitious materials by Ti tailings, 90 sets of backfilling ratio tests and UCS tests with different Ti tailings replacement ratios were carried out. The test results showed that the UCS tended to decrease as the ratio of Ti tailings replacing cement increased, and the UCS decreased sharply after the ratio of Ti tailings replacing cement exceeded 80%; however, the UCS increased at 28 days when the ratio of Ti tailings replacing cement did not exceed 10%. Meanwhile, to accurately predict the UCS, the whale optimization algorithm and random forest (WOA-RF), particle swarm optimization and random forest (PSO-RF), and sparrow search algorithm and random forest (SSA-RF) hybrid models were constructed. Moreover, the input parameters of the models include the ratio of Ti tailings, concentration, curing age, ratio of P.O42.5 cement and cement-sand ratio. In addition, a single RF model was constructed for comparative analysis, the four models (RF, WOA-RF, PSO-RF, and SSA-RF) were trained and tested, and the root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) of the four models were obtained: (0.532, 0.922, 0.416), (0.254, 0.975, 0.209), (0.367, 0.961, 0.300) and (0.318, 0.965, 0.258), respectively. The research showed that the WOA-RF model achieves better performance (R2 = 0.975), which proves that it is a better prediction model for predicting the USC of new cementitious filler with Ti tailings replacement.

Mingwang Fu

Luis Enrique Rosas-Hernandez, Jose Luis Cabellos, Angiolo Huamán et al.

Atoms within moiré bilayers relax in-plane to minimize elastic energy [e.g., Cazeaux et al., J. Elast. 154, 443 (2023)]; such relaxation brings their space group symmetries down to P1. Here, the ab initio second harmonic generation (SHG) of twisted and atomistically optimized hBN bilayers was determined at four twist angles ($θ=38.21^{\circ}$, $60.00^{\circ}$, $73.17^{\circ}$, and $98.21^{\circ}$) and for three displacements $\boldsymbolτ$ measured away from the ground state $AA^{\prime}$ configuration. All moiré bilayers have a P1 space symmetry after structural optimization. This situation is quite different to monolayers with hexagonal lattices, which retain a three-fold symmetry. We point out that the actual symmetries of the SHG reported for hBN bilayers on two experimental works do not coincide with the sixfold symmetric theoretical profiles they provide [either $\sin^2(3φ)$ or $\cos^2(3φ)$], and show that the intrinsic low structural symmetry of (atomically optimized) hBN bilayer moirés can in fact be read out from experimental SHG intensity profiles--which are tunable by $θ$ and by the frequency of light $ω$: The SHG is most definitely not sixfold-symmetric because moirés do not retain a three-fold symmetry. Furthermore, an extrinsic twofold symmetry of the SHG emission is realized by tilting the pump by an angle $α$ away from the 2D material's normal, regardless of $θ$ and $ω$. The design of in-plane and ultrathin sources of SHG with low symmetry could be useful for the eventual creation of entanglement sources from 2D materials.

Sifan Wang, Tong-Rui Liu, Shyam Sankaran et al.

Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of microstructures, material properties under any loading conditions and We demonstrate the accuracy and computational efficiency of Micrometer through applications in computational homogenization and multiscale modeling, where Micrometer achieves 1\% error in predicting macroscale stress fields while reducing computational time by up to two orders of magnitude compared to conventional numerical solvers. We further showcase the adaptability of the proposed model through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in mechanical analysis of solid materials. Our work represents a significant step towards AI-driven innovation in computational solid mechanics, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

K. Grigoras, N. Yurttagul, J.-P. Kaikkonen et al.

We fabricate and characterize superconducting through-silicon vias and electrodes suitable for superconducting quantum processors. We measure internal quality factors of a million for test resonators excited at single-photon levels, on chips with superconducting vias used to stitch ground planes on the front and back sides of the chips. This resonator performance is on par with the state of the art for silicon-based planar solutions, despite the presence of vias. Via stitching of ground planes is an important enabling technology for increasing the physical size of quantum processor chips and is a first step toward more complex quantum devices with 3-D integration.

Sajjan Sheoran, Preeti Bhumla, Saswata Bhattacharya

Spin-orbit coupling (SOC) effects occurring in noncentrosymmetric materials are known to be responsible for nontrivial spin configurations and a number of emergent physical phenomena such as electrical control of spin degrees of freedom and spin-to-charge conversion. The materials preserving a uniform spin configuration in the momentum-space, known as persistent spin texture (PST), provide long carrier spin lifetimes through persistent spin helix (PSH) mechanism. However, most of the PST studied till now are attributed to the linear in \textbf{\textit{k}} splitting and cease to exist locally around certain high-symmetry-point of first Brillouin Zone (FBZ). The persistent spin textures with purely cubic spin splittings have drawn attention owing to unique benefits in spin transport. Here, by using the relativistic first-principles calculations supplemented with \textbf{\textit{k.p}} analysis, we report the emergence of purely cubic splitting (PCS) belonging to $D_{3h}$ point group, which is enforced by in-plane mirror and three-fold rotation operations. In addition, the in-plane mirror symmetry operation sustains the PST in larger region (i.e. full planes) of FBZ alongside giant spin splitting. Our results also demonstrate how application of uniaxial strain could be envisaged to tune the magnitude of the PCS, preserving the PST. The observed PSTs provide a route to non-dephasing spin transport with larger spin-Hall conductivity, thus offering a promising platform for future spintronics devices.

Meysam Najimi, Nader Ghafoori