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

Jehoon Moon, Gisoo Lee, Jaehee Lee et al.

Long range order and symmetry in heterogeneous materials architected on crystal lattices lead to elastic and inelastic anisotropies and thus limit mechanical functionalities in particular crystallographic directions. Here, we present a facile approach for designing heterogeneous disordered materials that exhibit nearly isotropic mechanical resilience and energy dissipation capabilities. We demonstrate, through experiments and numerical simulations on 3D-printed prototypes, that near-complete isotropy can be attained in the proposed heterogeneous materials with a small, finite number of random spatial points. We also show that adding connectivity between random subdomains leads to much enhanced elastic stiffness, plastic strength, energy dissipation, shape recovery, structural stability and reusability in our new heterogeneous materials. Overall, our study opens avenues for the rational design of a new class of heterogeneous materials with isotropic mechanical functionalities for which the engineered disorder throughout the subdomains plays a crucial role.

Zhaobo Liu, Xiaoming Shi, Jing Wang et al.

Abstract Ferroelectricity in crystalline hafnium oxide has attracted considerable attention because of its potential application for memory devices. A recent breakthrough involves electric-field-induced crystallization, allowing HfO2-based materials to avoid high-temperature crystallization, which is unexpected in the back-end-of-line process. However, due to the lack of clarity in understanding the mechanisms during the crystallization process, we aim to employ theoretical methods for simulation, to guide experimental endeavors. In this work, we extended our phase-field model by coupling the crystallization model and time-dependent Ginzburg-Landau equation to analyze the crystalline properties and the polarization evolution of Hf0.5Zr0.5O2 thin film under applying an electric field periodic pulse. Through this approach, we found a wake-up effect during the process of crystallization and a transformation from orthorhombic nano-domains to the stripe domain. Furthermore, we have proposed an innovative artificial neural synapse concept based on the continuous polarization variation under applied electric field pulses. Our research lays the theoretical groundwork for the advancement of electric-field-induced crystallization in the hafnium oxide system.

Ge Li, Ying Tan, Zhuojun Li et al.

Water-based or waterborne polyurethane matting resins find extensive application in surface coating to diminish gloss, offering a pleasant tactile experience and a matte aesthetic. This review represents the inaugural effort to consolidate the recent advancements in waterborne polyurethane matting resins, encompassing both physical and chemical matting types. The exploration commences with an introduction to a range of innovative matting agents tailored for the formulation of physical matting waterborne polyurethane resins. Subsequently, a thorough analysis and discussion unfold, delving into the synthesis, characterization, and matting mechanisms of chemical matting waterborne polyurethane resins. This comprehensive discussion draws upon a decade of dedicated research work by our group, contributing fresh perspectives to the evolution of chemical matting techniques. In conclusion, the review not only addresses the current state but also outlines potential challenges and future trends. This forward-looking perspective is intended to offer guidance for the design and synthesis of innovative waterborne polyurethane matting resins.

Amira S Diab, Haifa A Alqhtani, May Bin-Jumah et al.

Cystoseira myrica marine macroalgae (CSR) were used to produce metallic zinc nanoparticle composites by utilizing the phytochemicals naturally found in the algae. This involves homogenizing the residuals of CSR (10 g), zinc nitrate solution (5 M; 100 ml), and methanol liquid extract (100 ml) at 30 °C for 24 h of sonication and stirring, followed by filtration and drying. This resulted in a hybrid bio-composite (Zn/CSR), which demonstrated strong antioxidant and antidiabetic properties when compared to zinc oxide (ZnO) and CSR used separately. The Zn/CSR hybrid showed excellent antioxidant activity against common radicals such as DPPH (91.5 ± 1.66%), nitric oxide (90.4 ± 1.2%), ABTS (92.2 ± 1.9%), and O _2 ^·− (27.8 ± 1.12%) ( p < 0.05), performing better than the standard antioxidant, ascorbic acid. Regarding its antidiabetic properties, the Zn/CSR composite significantly inhibited key enzymes involved in diabetes, including both commercial enzyme forms ( α -amylase (80.3 ± 1.65%), α -glucosidase (96.6 ± 1.11%), amyloglucosidase (95.8 ± 1.3%)) and their crude intestinal forms ( α -amylase (72.3 ± 1.5%), α -glucosidase (94.2 ± 1.7%)) ( p < 0.05). This improvement increases the impact of the green CSR extract in reducing the agglomeration behaviors of the loaded metal and the formation of a capping layer from the phytochemicals on its surface, in addition to the beneficial effects of the CSR as substrate, which enhances the biological functions of the loaded metal and its interaction interfaces. The Zn/CSR composite also outperformed commercial miglitol drugs and slightly surpassed acarbose in effectiveness. Given the high cost and potential side effects of current medications, the Zn/CSR composite could be a cost-effective alternative for antioxidant and antidiabetic treatments. These findings also emphasize the role of CSR-derived phytochemicals and algae residues in enhancing the biological activity of the metal nanoparticles.

Srestha Ghosh, Shinjini Chaudhuri, Subhabrata Guha et al.

Hydrogels are networks of three-dimensional cross-linked polymers, which possess the capacity to absorb and retain water. Hydrogels have proven to be adaptable and versatile, making them useful in various biomedical applications such as tissue engineering and regenerative medicine. Among the various types of hydrogels, peptide-based hydrogels are most suited for biological applications due to their special features, which include biodegradability, mechanical stability, biocompatibility, capacity to retain more water, injectability, and elasticity like that of tissues. In this review, we will present the recent advancements that have occurred in the field of peptide-based hydrogels concerning its biomedical applications especially delivery of targeted delivery, wound healing, tissue engineering, stem cell therapy, etc.

Allaire, Grégoire, Coron, Jean-Michel, Girault, Vivette

Atanur Teoman, Engin Göde, Barış Çetin et al.

A ceramic/composite add-on armour system with innovative ceramic geometry (cylindrical) against 14.5 × 114 mm API/B32 projectile was developed and ballistic performance of the armour was investigated both experimentally and numerically. Numerical analysis was used to calculate exit velocities of the projectile after passing through the ceramic/composite layer (before penetrating the Armox 500T which simulates hull structure of an armoured vehicle) and also contributed to the selection of optimum ceramic thickness. The calculated projectile velocity-time curves (from numerical analysis) for three different ceramic thicknesses are given comparatively in the study. The curve characteristics are the same for three different analyses. The duration of the total absorption of the projectile energy is about 0.2 microseconds (ms). There were differences in the transmission of the stress wave and the delamination in the Ultra-High-Molecular-Weight Polyethylene (UHMWPE) layers differed as ceramic thickness increases. The separation between the layers varied with the change in projectile energy. As a result of the ballistic test, the armour prevented 14.5 × 114 mm API/B32 ammunition with desired damage mechanisms. In the x-ray image taken after the shootings, it was seen that the ceramic damage was local which enhanced multi-hit resistance capability and the geometry of the cylindrical alumina played an important role in the localization of the ceramic zone damage during the projectile penetration process. Due to this cylindrical ceramic geometry, the projectile moving on after the moment of impact constantly encounters a curved and new surface, and thus it is deflected and exposed to more wear. The areal density of the armour was also reduced by using the UHMWPE (which is one of the composite material whose fibres have the lowest density and good mechanical properties) composite plate as the backing plate.

Yuma Iwasaki, Ryo Toyama, Takahiro Yamazaki et al.

Exploring vast material spaces efficiently is challenging in materials science. Autonomous methods for material search – integrating machine learning and ab initio calculations – have emerged as powerful alternatives to traditional approaches, which are often time-consuming and limited in scope. Although these autonomous methods have been applied to various material systems, the extensive material space of B2 structured materials for half-metallicity remains largely unexplored. Herein, we introduce a simulation-based autonomous search approach to identify B2 structured alloys exhibiting high spin polarization of sp conduction electrons (Psp), sp minority spin band gap (Gsp), and Curie temperature (Tc). The proposed method explores the material space of disordered quaternary B2 magnetic alloys using the Korringa – Kohn – Rostoker coherent potential approximation and Bayesian optimization. Over a continuous search of approximately 100 days, the system identified Co1.0Mn0.7Al0.3 as a promising candidate, demonstrating high values of Psp, Gsp, and Tc. Although additional experimental and theoretical validation is necessary, this study demonstrates the potential of autonomous material search methods to expedite material discovery and enhance material property optimization.

Bhupesh Bishnoi

Materials informatics, data-enabled investigation, is a "fourth paradigm" in materials science research after the conventional empirical approach, theoretical science, and computational research. Materials informatics has two essential ingredients: fingerprinting materials proprieties and the theory of statistical inference and learning. We have researched the organic semiconductor's enigmas through the materials informatics approach. By applying diverse neural network topologies, logical axiom, and inferencing information science, we have developed data-driven procedures for novel organic semiconductor discovery for the semiconductor industry and knowledge extraction for the materials science community. We have reviewed and corresponded with various algorithms for the neural network design topology for the materials informatics dataset.

Pu Zhang, Junmin Hu, Jixing Yu et al.

Altan Yılmaz

Gavin Towler, Ray Sinnott

Jia Jin, Zhongti Sun, Tianran Yan et al.

Sluggish sulfur reduction reaction (SRR) kinetics remains a formidable challenge in Li–S electrochemistry. In this sense, the rational design of single‐atom species has become a burgeoning practice to expedite sulfur redox, where the underlying catalytic mechanism otherwise remains elusive. Herein, a class of metal single‐atom modified porous carbon nanofiber films (MSA PCNFs, M = Fe, Co, or Ni), fabricated via a generic synthetic strategy, as mediators to boost SRR kinetics is reported. Throughout electrokinetic measurement and operando instrumental probing, NiSA PCNF is evidenced to harness the catalytic superiority toward the rate‐determining step (i.e., liquid–solid conversion) of the SRR process. Density functional theory (DFT) simulations further reveal that the catalytic features of M–N–C moieties in catalyzing the Li2S precipitation rely heavily upon the coordination environments of adjacent carbon atoms and d‐orbital configurations of metal centers. In response, the thus‐derived S/NiSA PCNF cathode realizes an encouraging areal capacity of 14.12 mAh cm−2 under elevated sulfur loading (10.2 mg cm−2) and lean electrolyte usage (E/S ratio ≈ 5.5 μL mg−1). This work offers insight into the identification of exact catalytic moieties for different transition metal M–N–C single‐atom SRR mediators, showcasing a meaningful guidance and potential impact on Li–S catalysis.

Cefe López

Artificial intelligence is gaining strength and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems and processes can be devised and optimized thanks to machine learning techniques and such progress can be turned into in-novative computing platforms. Future materials scientists will profit from understanding how machine learning can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by compu-tation to account for the origins, procedures and applications of artificial intelligence. Machine learning and its methods are reviewed to provide basic knowledge on its implementation and its potential. The materials and systems used to implement artificial intelligence with electric charges are finding serious competition from other information carrying and processing agents. The impact these techniques are having on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materi-als and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom as exemplified by the power to discover unheard of mate-rials or physical laws buried in data.

Dominik Farka, Theresia Greunz, Cigdem Yumusak et al.

We set out to demonstrate the development of a highly conductive polymer based on poly-(3,4-ethylenedithia thiophene) (PEDTT), PEDOTs structural analogue historically notorious for structural disorder and limited conductivities. The caveat therein was previously described to lie in intra-molecular repulsions. We demonstrate how a tremendous >2600-fold improvement in conductivity and metallic features, such as magnetoconductivity can be achieved. This is achieved through a careful choice of the counter-ion (sulphate) and the use of oxidative chemical vapour deposition (oCVD). It is shown that high structural order on the molecular level was established and the formation of crystallites tens of nanometres in size was achieved. We infer that the sulphate ions therein intercalate between the polymer chains, thus forming densely packed crystals of planar molecules with extended π-systems. Consequently, room-temperature conductivities of above 1000 S cm−1 are achieved, challenging those of conventional PEDOT:PSS. The material is in the critical regime of the metal–insulator transition.

Sebastiaan P. Huber, Emanuele Bosoni, Marnik Bercx et al.

The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods aiming to solve similar problems is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use any one for a given task. Leveraging recent advances in managing reproducible scientific workflows, we demonstrate how developing common interfaces for workflows that automatically compute material properties can tackle the challenge mentioned above, greatly simplifying interoperability and cross-verification. We introduce design rules for reproducible and reusable code-agnostic workflow interfaces to compute well-defined material properties, which we implement for eleven different quantum engines and use to compute three different material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer's expertise of the quantum engine directly available to non-experts. Full provenance and reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure. All workflows are made available as open-source and come pre-installed with the Quantum Mobile virtual machine, making their use straightforward.

HUANG Song, CHEN ZhiPing, LI You

Shakedown analysis is a powerful tool for the ratchet ＆ alternating plasticity prediction of the pressure piping under thermal-mechanical loads. In order to achieve the shakedown analysis containing the synergetic influence of the kinematic hardening and the temperature-dependent properties of the material,a numerical method was developed. The presenting method is an extension of the two-surface model for shakedown analysis and is based on the basis reduction method. The idea is to transfer the nonlinear programming resulting from the shakedown analysis with kinematic hardening and temperature-dependent nature into an elastic perfectly plastic one which can be solved with less effort. Numerical examples indicated that the proposed method is accurate. This method has the potential to improve the accuracy of failure prediction for the piping suffering thermal-mechanical loads and therefore is of engineering values.

Anran Wei, Jie Xiong, Weidong Yang et al.

With the achievement on the additive manufacturing, the mechanical properties of architectured materials can be precisely designed by tailoring microstructures. As one of the primary design objectives, the elastic isotropy is of great significance for many engineering applications. However, the prevailing experimental and numerical methods are normally too costly and time-consuming to determine the elastic isotropy of architectured materials with tens of thousands of possible microstructures in design space. The quick mechanical characterization is thus desired for the advanced design of architectured materials. Here, a deep learning-based approach is developed as a portable and efficient tool to identify the elastic isotropy of architectured materials directly from the images of their representative microstructures with arbitrary component distributions. The measure of elastic isotropy for architectured materials is derived firstly in this paper to construct a database with associated images of microstructures. Then a convolutional neural network is trained with the database. It is found that the convolutional neural network shows good performance on the isotropy identification. Meanwhile, it exhibits enough robustness to maintain the performance under fluctuated material properties in practical fabrications. Moreover, the well-trained convolutional neural network can be successfully transferred among different types of architectured materials, including two-phase composites and porous materials, which greatly enhance the efficiency of the deep learning-based approach. This study can give new inspirations on the fast mechanical characterization for the big-data driven design of architectured materials.

Peter Pickerill, Heiko Joshua Jungen, Miroslaw Ochodek et al.

Context: Within the field of Mining Software Repositories, there are numerous methods employed to filter datasets in order to avoid analysing low-quality projects. Unfortunately, the existing filtering methods have not kept up with the growth of existing data sources, such as GitHub, and researchers often rely on quick and dirty techniques to curate datasets. Objective: The objective of this study is to develop a method capable of filtering large quantities of software projects in a resource-efficient way. Method: This study follows the Design Science Research (DSR) methodology. The proposed method, PHANTOM, extracts five measures from Git logs. Each measure is transformed into a time-series, which is represented as a feature vector for clustering using the k-means algorithm. Results: Using the ground truth from a previous study, PHANTOM was shown to be able to rediscover the ground truth on the training dataset, and was able to identify "engineered" projects with up to 0.87 Precision and 0.94 Recall on the validation dataset. PHANTOM downloaded and processed the metadata of 1,786,601 GitHub repositories in 21.5 days using a single personal computer, which is over 33% faster than the previous study which used a computer cluster of 200 nodes. The possibility of applying the method outside of the open-source community was investigated by curating 100 repositories owned by two companies. Conclusions: It is possible to use an unsupervised approach to identify engineered projects. PHANTOM was shown to be competitive compared to the existing supervised approaches while reducing the hardware requirements by two orders of magnitude.

K. Tian, B. Tian, A.A. Volinsky et al.

Isothermal hot compression experiments were carried out using the Gleeble-1500D thermal mechanical simulator. The flow stress of the Cu-1%Zr and Cu-1%Zr-0.15%Y alloys was studied at hot deformation temperature of 550°C, 650°C, 750°C, 850°C, 900°C and the strain rate of 0.001 s–1, 0.01 s–1, 0.1 s–1, 1 s–1, 10 s–1. Hot deformation activation energy and constitutive equations for two kinds of alloys with and without yttrium addition were obtained by correlating the flow stress, strain rate and deformation temperature. The reasons for the change of hot deformation activation energy of the two alloys were analyzed. Dynamic recrystallization microstructure evolution for the two kinds of alloys during hot compression deformation was analyzed by optical and transmission electron microscopy. Cu-1%Zr and Cu-1%Zr-0.15%Y alloys exhibit similar behavior of hot compression deformation. Typical dynamic recovery occurs during the 550-750°C deformation temperature, while dynamic recrystallization (DRX) occurs during the 850-900°C deformation temperature. High Zr content and the addition of Y significantly improved Cu-1%Zr alloy hot deformation activation energy. Compared with hot deformation activation energy of pure copper, hot deformation activation energy of the Cu-1%Zr and Cu-1%Zr-0.15%Y alloys is increased by 54% and 81%, respectively. Compared with hot deformation activation energy of the Cu-1%Zr alloy, it increased by 18% with the addition of Y. The addition of yttrium refines grain, advances the dynamic recrystallization critical strain point and improves dynamic recrystallization.