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

Zeyu Zhang, Long Wan, Yong Yang et al.

Overcoming the strength-ductility trade-off dilemma is paramount for advanced materials engineering. Herein, we prepared 7075 aluminium alloys with superior strength and ductility via additive friction stir deposition (AFSD) and subsequent heat treatment. Compared with the commercial base material, the heat-treated 7075 aluminium alloy maintained a high ultimate tensile strength of 556 MPa, while the uniform elongation increased from 12.2% to 26.7%, exhibiting the highest strength-ductility synergy reported among commercial Al-Zn-Mg-Cu alloy systems. Grain boundary sliding was activated via the equiaxed grains to accommodate substantial plastic strain. This method provides a promising and cost-effective pathway for developing strength-ductility on Al-Zn-Mg-Cu alloys.

Ming Han, Ge Sun, Paul F. Nealey et al.

Abstract Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials.

Ha Eun Kang, Seong-Do Kim, Young Soo Yoon et al.

Nickel-rich layered oxide cathodes, typified by compositions such as LiNi₁₋ₓ₋ᵧCoₓMnᵧO₂ (NCM) have garnered significant attention as high-energy-density candidates for next-generation lithium-ion batteries. However, their widespread deployment is hindered by a confluence of structural degradation, surface instability, and poor interfacial compatibility under high voltage cycling. To address these multifaceted limitations, this review comprehensively examines recent advances in surface coating and bulk doping strategies, which have emerged as pivotal approaches for enhancing the electrochemical stability and longevity of Ni-rich cathodes. Surface coatings including oxides, phosphates, and fluorides have been shown to effectively mitigate electrolyte-induced parasitic reactions and reinforce cathode–electrolyte interfaces. Simultaneously, elemental doping at transition-metal, lithium, and oxygen sites offer promising pathways to suppress cation disorder, stabilize layered frameworks, and facilitate Li⁺ transport. Emphasis is placed on site-specific doping mechanisms, the role of multi-site (co-)doping, and their synergistic interplay with surface modification layers. By synthesizing recent findings, this review delineates how the judicious integration of coating and doping techniques can enable the rational design of Ni-rich cathodes with enhanced structural integrity, rate capability, and cycle life.

Hongtao Guo Shuai Li Shu Li

This paper systematically reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have significantly accelerated material prediction and design, their complexity has also caused difficulties in understanding and application for researchers in traditional fields. In response to the above issues, this paper first analyzes the inherent challenges in the research and development of polymer materials, including structural complexity and the limitations of traditional trial-and-error methods. To address these problems, it focuses on introducing key basic technologies such as molecular descriptors and feature representation, data standardization and cleaning, and records a number of high-quality polymer databases. Subsequently, it elaborates on the key role of machine learning in polymer property prediction and material design, covering the specific applications of algorithms such as traditional machine learning, deep learning, and transfer learning; further, it deeply expounds on data-driven design strategies, such as reverse design, high-throughput virtual screening, and multi-objective optimization. The paper also systematically introduces the complete process of constructing high-reliability machine learning models and summarizes effective experimental verification, model evaluation, and optimization methods. Finally, it summarizes the current technical challenges in research, such as data quality and model generalization ability, and looks forward to future development trends including multi-scale modeling, physics-informed machine learning, standardized data sharing, and interpretable machine learning.

Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick et al.

Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting a snowball state with a smaller increase in instellation compared with planets on circular orbits; this enables eccentric planets to exhibit warmer conditions along a broad range of instellation. This study emphasizes the significance of incorporating an albedo parameterization for the formation of CO2 ice into climate models to accurately assess the habitability of eccentric planets, as we show that, even at moderate eccentricities, planets with Earth-like atmospheres can reach surface temperatures cold enough for the condensation of CO2 onto their surfaces, as can planets receiving low amounts of instellation on circular orbits.

Yue Yang, Zhenyu Xiao, Yu Mao et al.

Chiral phonons, circularly polarized lattice vibrations carrying intrinsic angular momentum, offer unprecedented opportunities for controlling heat flow, manipulating quantum states through spin-phonon coupling, and realizing exotic transport phenomena. Despite their fundamental importance, a universal framework for identifying and classifying these elusive excitations has remained out of reach. Here, we address this challenge by establishing a comprehensive symmetry-based theory that systematically classifies the helicity and the velocity-angular momentum tensor underlying phonon magnetization in thermal transport across all 230 crystallographic space groups. Our approach, grounded in fundamental representations of phononic angular momentum, reveals three distinct classes of crystals: achiral crystals with vanishing angular momentum, chiral crystals with s-wave helicity, and achiral crystals exhibiting higher-order helicity patterns beyond the s-wave. By performing high-throughput computations and symmetry analysis of the dynamical matrices for 11614 crystalline compounds, we identified 2738 materials exhibiting chiral phonon modes and shortlisted the 170 most promising candidates for future experimental investigation. These results are compiled into an open-access Chiral Phonon Materials Database website, enabling rapid screening for materials with desired chiral phonon properties. Our theoretical framework transcends phonons--it provides a universal paradigm for classifying chiral excitations in crystalline lattices, from magnons to electronic quasiparticles.

Felix E. B. Brettner, Stefanie Gier, Annika Haessler et al.

Abstract Inflammation plays a critical role in the pathophysiology of many diseases, and dysregulation of the involved signaling cascades often culminates in uncontrollable disease progression and, ultimately, chronic manifestation. Addressing these disorders requires balancing inflammation control while preserving essential immune functions. Cyclodextrins (CDs), particularly β-CD, have gained attention as biocompatible biomaterials with intrinsic anti-inflammatory properties, and chemical modification of their backbone offers a promising strategy to enhance their physicochemical properties, adaptability, and therapeutic potential. This study evaluated and characterized the immunomodulatory effects of amphiphilic CD derivatives, which self-assemble into nanoparticles, compared to soluble parent β-CD. In a human macrophage model, CD nanoparticles demonstrated superior anti-inflammatory activity, with derivative-specific effects tied to their physicochemical properties, surpassing the soluble β-CD control. Alongside the downregulation of key pro-inflammatory markers, significant reductions in inflammasome activation and changes in lipid profiles were observed. The findings of this study underscore the potential of cyclodextrin-based nanoparticles as versatile biomaterials for treating the complex pathophysiology of various acute and chronic inflammation-associated disorders.

Toni Utech, Henning Kruppa, Anya Vollpracht et al.

Biological materials found in nacre or glass sponges reveal specific layered organic and inorganic structures known for their fracture toughness caused by crack deflection and bridging. This work aims to bio-mimic the brick-and-mortar (BnM) structure from nacre and the layer-by-layer (LbL) structure from the glass sponge filaments to incorporate these effects into the contact zone of carbon fiber-reinforced concrete (CFRC) in order to prevent brittle composite failure. To build up BnM- and LbL-structure materials such as nanoclays, sodium water glass, and polymer dispersions were selected since they are well-established low-cost materials in building. Nanoclays were analyzed regarding their size, dispersibility and exfoliation. Montmorillonite (MMT) was used to be mixed with polymers to produce self-assembled BnM-structured films and coatings. Also, LbL-structures were formed by alternating layers of sodium water glass and polymer. Scanning electron microscopy and energy-dispersive X-ray spectroscopy were used to verify the morphology. The MMT-containing coatings demonstrated enhanced nucleation potential when exposed to cementitious eluate. Micromechanical pull-out tests on single carbon fibers with BnM- and LbL-coatings embedded in concrete demonstrate the potential to increase composite toughness. The successful implementation of the bio-inspired structures using affordable materials lays the groundwork for their scalability and integration into composite structures for building.

Iñigo Robredo, Yuanfeng Xu, Yi Jiang et al.

We conducted a high-throughput search for topological magnetic materials on 522 new, experimentally reported commensurate magnetic structures from MAGNDATA, doubling the number of available materials on the Topological Magnetic Materials database. This brings up to date the previous studies which had become incomplete due to the discovery of new materials. For each material, we performed first-principle electronic calculations and diagnosed the topology as a function of the Hubbard U parameter. Our high-throughput calculation led us to the prediction of 250 experimentally relevant topologically non-trivial materials, which represent 47.89% of the newly analyzed materials. We present five remarkable examples of these materials, each showcasing a different topological phase: Mn${}_2$AlB${}_2$ (BCSID 1.508), which exhibits a nodal line semimetal to topological insulator transition as a function of SOC, CaMnSi (BCSID 0.599), a narrow gap axion insulator, UAsS (BCSID 0.594) a 5f-orbital Weyl semimetal, CsMnF${}_4$ (BCSID 0.327), a material presenting a new type of quasi-symmetry protected closed nodal surface and FeCr${}_2$S${}_4$ (BCSID 0.613), a symmetry-enforced semimetal with double Weyls and spin-polarised surface states.

Chenzi ZHAI, Xiaodong GUO, Wei WANG

In order to accurately predict the risk level of ancient timber structures under earthquake,22 evaluation indexes were selected, including hazard of disaster-causing factors, susceptibility assessment of disaster environment, and vulnerability of disaster-bearing body, combined with seismic damage characteristics and structural characteristics of ancient timber structures, and an earthquake risk assessment model was established by using the improved matter-element extension model. In this model, closeness degree is used instead of correlation degree, which improves the accuracy of evaluation results. This model was used to evaluate the risk of an ancient timber structure under different ground motion levels. The results show that the ancient timber structure is at moderate risk when the peak acceleration of ground motion is 0.05g, 0.10g, and 0.20g, and when it encounters earthquakes with peak ground accelerations of 0.40g, it becomes at a higher risk.

CHEN Qiang, LI Shun, ZHU Li'an et al.

As one kind of advanced high temperature structural and functional materials, it is necessary for fiber reinforced silicon carbide matrix composites (SiC CMCs) in the field of thermal management (TM) to combine the efficient heat transfer and high temperature heat resistance. Common fibers reinforced SiC CMCs, such as carbon fibers reinforced SiC CMCs (Cf/SiC or Cf/C-SiC), silicon carbide based fiber reinforced SiC CMCs (SiCf/SiC), etc., have a low degree of graphitization of the reinforcing fiber and are difficult to form an effective heat transport network. The latest research progress on the preparation and properties of fiber reinforced SiC CMCs with highly thermal conductivity was reviewed in this paper. The heat transport ability of fiber reinforced SiC CMCs can be improved by introducing highly thermal conductive phase, optimizing interfacial structure, making silicon carbide crystal coarse-grained, and designing preform structure. Moreover, the development of the fiber reinforced SiC CMCs with highly thermal conductivities was prospected, that is, comprehensively considering the factors that affect the performance of SiC CMCs, flexibly using the structure-activity relationship between the microstructure and properties of the composites, in order to prepare fiber reinforced SiC CMCs with stable size, excellent properties.

Zihan Lin, Carlos Monton, Stefan Bringuier et al.

W-SiC composite material is a promising plasma-facing material candidate alternative to pure W due to the low neutron activation, low impurity radiation, and low tritium diffusivity of SiC while leveraging the high erosion resistance of the W armor. Additionally, W and SiC have high thermomechanical compatibility given their similar thermal expansion rates. The present study addresses the synthesis and performance of compositionally graded W-SiC films fabricated by pulsed-DC magnetron sputtering. Compositional gradients were characterized using transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), and crystallographic information was obtained using electron diffraction and X-ray diffraction (XRD). Samples were exposed to L-mode deuterium plasma discharges in the DIII-D tokamak using the Divertor Material Evaluation System (DiMES). Post-mortem characterizations were performed using scanning electron microscopy (SEM) and XRD. Electron diffraction and XRD showed that the compositionally graded W-SiC films were composed of polycrystalline W and amorphous SiC with amorphous W+SiC interlayers. No macroscopic delamination or microstructural changes were observed under mild exposure conditions. This study serves as a preliminary examination of W-SiC compositionally graded composites as a potential candidate divertor material in future tokamak devices.

Joshua F. Belot, Valentin Taufour, Stefano Sanvito et al.

Technologies that function at room temperature often require magnets with a high Curie temperature, $T_\mathrm{C}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known data sets of experimental Curie temperatures, we develop machine-learning models to make rapid $T_\mathrm{C}$ predictions solely based on the chemical composition of a material. We train a random forest model and a $k$-NN one and predict on an initial dataset of over 2,500 materials and then validate the model on a new dataset containing over 3,000 entries. The accuracy is compared for multiple compounds' representations ("descriptors") and regression approaches. A random forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. A random forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-$T_\mathrm{C}$ materials and under-predict high-$T_\mathrm{C}$ materials. For exhaustive searches to find new high-$T_\mathrm{C}$ materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

Sichao Qu, Min Yang, Tenglong Wu et al.

Spatially exponential distributions of material properties are ubiquitous in many natural and engineered systems, from the vertical distribution of the atmosphere to acoustic horns and anti-reflective coatings. These media seamlessly interface different impedances, enhancing wave transmission and reducing internal reflections. This work advances traditional transfer matrix theory by integrating analytical solutions for acoustic exponential materials, which possess exponential density and/or bulk modulus, offering a more accurate predictive tool and revealing the physical mechanism of broadband anti-reflection for sound propagation in such non-uniform materials. Leveraging this method, we designed an acoustic dipole array that effectively mimics exponential mass distribution. Through experiments with precisely engineered micro-perforated plates, we demonstrate an ultra-low reflection rate of about 0.86% across a wide frequency range from 420 Hz to 10,000 Hz. Our modified transfer matrix approach underpins the design of exponential materials, and our layering strategy for stacking acoustic dipoles suggests a pathway to more functional gradient acoustic metamaterials.

Shijia Liu, Ayse Ay, Qiaochu Luo et al.

The integration of non-enzymatic glucose sensing entities into device designs compatible with industrial production is crucial for the broad take-up of non-invasive glucose sensors. Copper and its oxides have proven to be promising candidates for electrochemical glucose sensing. They can be fabricated in situ enabling integration with standard copper metallisation schemes for example in printed circuit boards (PCBs). Here, copper oxide electrodes are prepared on flexible polyimide substrates through direct annealing of patterned electrode structures. Both annealing temperature and duration are tuned to optimise the sensor surface for optimum glucose detection. A combination of microscopy and spectroscopy techniques is used to follow changes to the surface morphology and chemistry under the varying annealing conditions. The observed physico-chemical electrode characteristics are directly compared with electrochemical testing of the sensing performance, including chronoamperommetry and interference experiments. A clear influence of both aspects on the sensing behaviour is observed and an anneal at 250 °C for 8 h is identified as the best compromise between sensor performance and low interference from competing analytes.

LIU Xin - hua, GUAN Jun - xia, WANG Li - hong, FU Zhan - da, GAO Yu - hua, WEI Jin - fang, WANG Lei, YANG Yong, LI Wen - tao

Polyepoxysuccinic acid (PESA) is a green and environmental scale inhibitor. Its application is limited due to its single functional group. In order to enhance its performance, an experiment was carried out by using serine (Ser) as the modifier to modify the PESA, and the optimal synthesis process conditions for the modification were investigated. The structure of the target product was characterized by Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR). The thermal stability of the modified product was investigated through thermogravimetric experiment. Results showed that under the conditions that the temperature was 90 ℃, the mass ratio of polyepoxysuccinic acid to serine was 1.0∶0.8, and the action time was 2 h, the best scale inhibition performance of the polyepoxysuccinic acid derivative (Ser - PESA) could be obtained. When the dosage of PESA and Ser - PESA was 10 mg/L, the scale inhibition rate against calcium carbonate could reach 86% and 96% respectively. The FTIR and SEM images of calcium scale showed that the structure of calcium carbonate was changed from stable calcite structure to unstable vaterite structure after adding PESA and Ser - PESA.

Khang Hoang

This chapter illustrates the use of defect physics as a conceptual and theoretical framework for understanding and designing battery materials. It starts with a methodology for first-principles studies of defects in complex transition-metal oxides. The chapter then considers defects that are activated in a cathode material during synthesis, during measurements, and during battery use. Through these cases, it discusses possible defect landscapes in the material and their implications, guidelines for materials design via defect-controlled synthesis, mechanisms for electronic and ionic conduction and for electrochemical extraction and (re-)insertion, and effects of doping. Although specific examples are taken from studies of battery cathode materials, the computational approach and discussions are general and applicable to any ionic, electronic, or mixed ionic-electronic conducting materials.

Kaveh Edalati

Superfunctional materials are defined as materials with specific properties being superior to the functions of engineering materials. Numerous studies introduced severe plastic deformation (SPD) as an effective process to improve the functional and mechanical properties of various metallic and non-metallic materials. Moreover, the concept of ultra-SPD - introducing shear strains over 1000 to reduce the thickness of sheared phases to levels comparable to atomic distances - was recently utilized to synthesize novel superfunctional materials. In this article, the application of ultra-SPD for controlling atomic diffusion and phase transformation and synthesizing new materials with superfunctional properties is discussed. The main properties achieved by ultra-SPD include: (i) high-temperature thermal stability in new immiscible age-hardenable aluminum alloys; (ii) room-temperature superplasticity for the first time in magnesium and aluminum alloys; (iii) high strength and high plasticity in nanograined intermetallics; (iv) low elastic modulus and high hardness in biocompatible binary and high-entropy alloys; (v) superconductivity and high strength in the Nb-Ti alloys; (vi) room-temperature hydrogen storage for the first time in magnesium alloys; and (vii) superior photocatalytic hydrogen production, oxygen production, and carbon dioxide conversion on high-entropy oxides and oxynitrides as a new family of photocatalysts.

Sebastian Pech, Markus Lukacevic, Josef Füssl

Fracture mechanics is crucial for many fields of engineering, as precisely predicting failure of structures and parts is required for efficient designs. The simulation of failure processes is, from a mechanical and a numerical point of view, challenging, especially for inhomogeneous materials, where the microstructure influences crack initiation and propagation and leads to complex crack patterns. The phase field method for fracture is a promising approach to encounter such materials since it is able to describe complex fracture phenomena like crack kinking, branching and coalescence. Moreover, it is a largely mesh independent approach, given that the mesh is homogenous around the crack. However, the broadly used formulation of the phase field method is limited to isotropic materials and does not account for preferable fracture planes defined through the material's microstructure. In this work, the method is expanded to take orthotropic constitutive behavior and preferable directions of crack propagation into account. We show that by using a stress-based split and multiple phase field variables with preferable fracture planes, in combination with a hybrid phase field approach, a general framework can be found for simulating anisotropic, inhomogeneous materials. The stress-based split is based on fictitious crack faces and is, herein, expanded to anisotropic materials. Furthermore, a novel hybrid approach is used, where the degradation of the sound material is performed based on a smooth traction free crack boundary condition, which proves to be the main driving factor for recovering observed crack patterns. This is shown by means of a detailed analysis of two examples: a wooden single edge notched plate and a wood board with a single knot and complex fiber directions. In both cases, the proposed novel hybrid phase field approach can realistically reproduce complex failure modes.

Minfang Yeh, Ren-Yuan Zhu

Future HEP experiments present stringent challenges to calorimeter materials in radiation tolerance, time response and project cost. The 2019 report of the DOE Basic Research Needs Study on High Energy Physics Detector Research and Development points out three priority research directions for future calorimetry. Following these research directions letters of interest were submitted to the Snowmass organized by the Division of Particles and Fields of the American Physics Society. This report summarizes materials to be developed in the form of inorganic, liquid (oil- and water-based), and plastic scintillators and wavelength shifters to advance HEP calorimetry to face the challenges in radiation hardness, fast timing, and cost-effectiveness. Some of these materials may also find applications for future HEP time-of-flight system, and beyond HEP in nuclear physics, hard X-ray imaging and medical instruments.