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

Leonardo González-Reyes, Julio César Espinoza-Tapia, Jesús Noé Rivera Olvera et al.

We analyze how crystallite size and thermodynamic driving forces govern the reversible phase transformation between tetragonal Hausmannite–Mn _3 O _4 and cubic Bixbyite–Mn _2 O _3 in Mn–O nanoparticles using x-ray diffraction (XRD) and electron microscopy (SEM, HRTEM), complemented by a semi-empirical enthalpy-trend analysis (extended Miedema model). Nanostructured samples were synthesized by combined sonochemical and hydrothermal routes and subsequently annealed in air from 200 ^∘ C to 900 ^∘ C in 100 ^∘ C increments. Crystallographic and morphological analyses show that the forward transformation Mn _3 O _4  → Mn _2 O _3 becomes detectable by XRD near 500 ^∘ C, where the phase coherent-domain sizes are 13.86 nm for Mn _3 O _4 and 25.57 nm for Mn _2 O _3 . The reverse transformation Mn _2 O _3  → Mn _3 O _4 is observed at 900 ^∘ C (for bulk crystals, the forward oxidation and reverse reduction are typically reported near 900 ^∘ C and 1100 ^∘ C, respectively, depending on atmosphere). The forward step proceeds via a topotactic, structure-preserving pathway governed by an operational critical crystallite size of the parent Mn _3 O _4 (defined here as the Mn _3 O _4 domain size at the onset of Mn _2 O _3 detectability, ∼13.9 nm in this dataset) and diffusion-controlled oxygen transport. The reverse step is attributed to oxygen loss and lattice reconstruction while retaining partial lattice coherence. The morphological evolution from granular aggregates to octahedral particles is consistent with Ostwald ripening and coalescence-driven growth. The thermodynamic analysis indicates a slight enthalpic preference for Bixbyite–Mn _2 O _3 (Δ H _s = −82.39 kJ/(mol atom)) over Hausmannite–Mn _3 O _4 (Δ H _s = −80.82 kJ/(mol atom)). In this context, the reversible behavior under annealing in air is interpreted within a Gibbs free-energy framework for an oxygen-open system, Δ G ( T ,  p O _2 ), where entropy and oxygen chemical potential govern the transformation direction, while Miedema-derived Δ H _s values are used only as comparative stability trends.

Damilola O. Oyejobi, Ali A. Firoozi, Adekunle P. Adewuyi et al.

The properties of fly and bottom ashes produced by Morupule power stations in Botswana are analysed for their pozzolanicity. Furthermore, the influence of physical, chemical and microstructural properties of the ashes on the performance of compressive strength of fly ash–based blended cement and geopolymer mortar was evaluated. Physical characterization including particle size distribution, specific gravity and morphology was carried out with Malvern Mastersizer-2000, Le Chatelier flask apparatus and Carl Zeiss FEGSEM Gemini 500. The chemical composition and mineral characterization were determined by S8 Tiger XRF and Empyrean PANalytical 4 KW XRD systems. Blended cement mortar was produced by intergrinding CEM I Portland cement with fly ash in different proportions from 0% to 50% at 10% increments. The geopolymer mortar was synthesized by activating oxides in the ash with varying quantities of sodium hydroxide, ranging from 8 to 14 moles. The findings indicate that the two fly ashes are categorized as Class F, while bottom ashes from Plants A and B are classified as Classes F and C, respectively, due to differing oxide composition percentages. An increase in the proportion of fly ash in blended cement results in a decrease in compressive strength, with the maximum compressive strength of 52 MPa attained at 10% fly ash. The increase in sodium hydroxide concentrations affects compressive strength at various curing ages, achieving a peak compressive strength of 39 MPa at 28 days with a sodium hydroxide concentration of 14 moles. The findings were further corroborated statistically by the analysis of variance (ANOVA). The presence of heavy elements in coal ash underscores the imperative for safe reutilization of the material rather than its disposal in an ash pond. This study suggests that perceived waste can be converted into wealth, while simultaneously alleviating potential environmental and ecological hazards.

Jinhui Fan, Chonghe Wang, Xiaoyan Lu et al.

Active symmetry control - a central challenge in materials science, particularly in ferroelectrics - is achieved via mechanically assisted poling (MAP) guided by thermodynamics and phase - field modeling. This approach yields extraordinary piezoelectric coefficients (about 5,000 pC/N at 24 degC; 11,700 pC/N at 58 degC) together with about 65% optical transmittance in a classic relaxor ferroelectric, Pb(Mg1/3Nb2/3)O3-PbTiO3. Mechanical suppression of undesirable phases stabilizes a reconstructed symmetry with highly ordered domains, verified by multiple characterization techniques. The strategy is validated across several distinct ferroelectric systems. To demonstrate its practical utility, we fabricate a transparent dual-modal wearable sensor integrating continuous blood pressure monitoring via piezoelectricity with photoplethysmographic SpO2 detection, enabling high-fidelity physiological tracking. This work establishes mechanically assisted symmetry reconstruction as a pathway to multifunctional optoelectronic materials and compact wearable health technologies.

Carla Zambrano, A. Inés Fernández, Pablo Tamarit et al.

The recycling and revalorization of polymeric waste is crucial to manage the large amount generated by the automotive industry. The most common recycling practice for thermoplastics materials is mechanical recycling, based on operations such as collect, separation of contaminations, washing/shredding, dying if required, tumbling with magnet, extrusion, palletisation and injection moulding (in case of exterior parts such as bumpers) [1]. During this process there are challenges that have not yet been solved. In the case of bumpers, superficial paint is observed on used or post-industrial bumpers that have had some defect in the painting process and are rejected. Paint residues negatively impact recycled plastics, creating weak points and compromising properties like thermal stability and mechanical strength [2]. Therefore, removing paint from polymeric waste, particularly bumpers, is crucial for obtaining high-quality recycled materials and a closed loop in automotive industry [3]. The study aims to use metal particle impact technology to efficiently remove acrylic paint from polypropylene substrate whit 20 % talc load in recycled bumpers. The main objectives of the work are (i) to observe the effect of the different test conditions on the effectiveness of removal paint, (ii) to make an approach to the industrial feasibility in a real recycling environment and (iii) to study different treatments for the elimination of the metallic particles left behind by the blasting process. The results show that the angular shot and a combination of 90°/grazing angle of incidence are better than the spherical shot and other angles. To remove these metal particles ultrasonic bath presents the best results among others, but a total elimination of these remains is not obtained.

Kishor B. Shingare, Suhas Alkunte, Baosong Li et al.

Owing to their superior electro-thermo-mechanical properties, the significance of interpenetrating phase composites (IPCs) in various industries is in high demand. IPCs, characterized by infiltrating metal, ceramic, and polymer phases, provide various advantages, including a balanced mixture of strength, stiffness, and toughness, excellent thermal characteristics, wear resistance, and flexibility in microstructure and processing routes. This comprehensive review explores the realm of multifunctional reinforcing phases, specifically focusing on their integration into 3D printed composites. Within this context, the IPCs with a special spotlight on captivating world of Triply Periodic Minimal Surface (TPMS) and other cellular/lattice architectures wherein two core themes are presented and dissected: TPMS-based IPCs, which collaboratively amplify properties of another phase and interpenetrating piezoelectric phase composites (IP2Cs), which offer special advantages over conventional ones. We compiled comprehensive data on IPCs, emphasizing their effective properties, mechanical performance, fatigue and fracture behavior, energy absorption capacity, and coupled electromechanical characteristics. Furthermore, the commercial applications of architectured IPCs across industries are highlighted, along with a critical analysis of current research, identifying gaps and challenges. It highlights their pivotal role in advancing technology and addressing contemporary challenges while illuminating the uncharted possibilities presented by TPMS cellular structures in the dynamic landscape of 3D printing.

JIANG Zongjin, ZHANG Rui, LIANG Caihang et al.

In response to the high energy and water consumption of data centers, hollow fiber membrane chillers were used to produce chilled water for the system in order to improve the air conditioning system performance and reduce water consumption.The response surface methodology (RSM) was used to de sign the experimental program.Air temperature, relative humidity, air flow rate, inlet water temperature and water flow rate were selected as impact factors.And a cooling system experimental bench based on fork flow hollow fiber membrane cooler was built.The data were collated and analyzed to assess the potential impact of each impact factor on the response values, including outlet water temperature, cooling efficiency, coefficient of performance (COP) and water consumption, and their magnitude.The results show that there is a significant positive correlation between inlet water temperature and outlet water temperature.And the cooling performance of the system is optimized when the inlet water temperature is 34 ℃.In addition, increased air flow helps to increase cooling efficiency and COP, and can effectively reduce water consumption.While increasing the water flow rate can further increase COP, it can lead to an increase in outlet water temperature and an increase in water consumption.This study provides scientific basis and theoretical support for the optimal design of air conditioning cooling system in data centers.

Sage Fulco, Prashant K. Purohit, Michal K. Budzik et al.

The failure of mechanical metamaterials is a function of the interplay between the properties of the base material and the microstructural geometry. Stochastic failure properties of the base material and disordered microstructural geometries can contribute to variations in the global failure mechanics that are not captured in traditional analyses of ordered, deterministic architected materials. We present a probabilistic framework that couples stochastic material failure and geometric disorder to predict failure in lattice mechanical metamaterials. These predictions are verified through finite element analysis, which confirm that disorder and stochasticity affect both the mean and variance of the damage initiation load in a lattice, with average failure loads being generally reduced and variance increasing with higher levels of disorder and stochasticity. The fracto-cohesive length and representative volume element size are also predicted and constrain the minimum defect and lattice sizes, respectively, for failure to be considered a fracture process. The framework is extended to consider the fracture behavior of the lattice, the development of damage zones, and their impact on the steady-state fracture toughness.

Caichao Ye, Tao Feng, Weishu Liu et al.

New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively data-driven approaches for new material discovery. This perspective introduces the concepts of functional units (FUs) to fill the gap in understanding of material structure-property correlations and knowledge inheritance as the "composition-microstructure" paradigm transitions to a data-driven AI paradigm transitions. Firstly, we provide a bird's-eye view of the research paradigm evolution from early "process-structure-properties-performance" to contemporary data-driven AI new trend. Next, we highlight recent advancements in the characterization of functional units across diverse material systems, emphasizing their critical role in multiscale material design. Finally, we discuss the integration of functional units into the new AI-driven paradigm of materials science, addressing both opportunities and challenges in computational materials innovation.

Ryan Yan, D. Thomas Seidl, Reese E. Jones et al.

This paper proposes a new approach for the calibration of material parameters in local elastoplastic constitutive models. The calibration is posed as a constrained optimization problem, where the constitutive model evolution equations for a single material point serve as constraints. The objective function quantifies the mismatch between the stress predicted by the model and corresponding experimental measurements. To improve calibration efficiency, a novel direct-adjoint approach is presented to compute the Hessian of the objective function, which enables the use of second-order optimization algorithms. Automatic differentiation is used for gradient and Hessian computations. Two numerical examples are employed to validate the Hessian matrices and to demonstrate that the Newton-Raphson algorithm consistently outperforms gradient-based algorithms such as L-BFGS-B.

S. Muguda, M. Wyndham

Changming Li, Yubing Fu, Haifeng Cheng et al.

Bailin Li, Fei Luo, Xiaodong Li et al.

Rapidly growing urbanization and industrialization drive the continued development of soil stabilization and ground improvement techniques. Rice husk ash (RHA) is widely regarded as a highly promising construction material in civil engineering due to its excellent pozzolanic properties and has garnered significant attention from researchers. This paper presents an experimental study and a micro-mechanical discussion on the role of RHA in the mechanical improvement of soil. RHA was mixed with the native soil in varying proportions, ranging from 0% to 12%. Several laboratory tests were conducted, including standard proctor compaction tests, Atterberg limit tests, freeze-thaw tests, unconfined compression tests, X-ray diffraction (XRD) analysis, and scanning electron microscopy (SEM). The results indicated that the optimal moisture content (OMC) of the soil mixture increased while the maximum dry density (MDD) decreased with higher RHA dosage. The Atterberg limits of the soil mixture exhibited a positive correlation with the RHA content. A substantial enhancement in the soil's strength, stiffness, and ductility was observed upon the incorporation of RHA. It was noted that the strength loss of the untreated samples and those with 12% RHA was 34.91% and 12.89%, respectively, following 12 freeze-thaw cycles. Furthermore, XRD test results revealed that the treated specimen had an identical mineral composition to the control specimen, with no generation of hydration products. SEM analysis also highlighted that the filling effect of RHA significantly reduced pore content and pore connectivity within the soil, accompanied by a shift in the specimen's pores from mesopores to small and micropores. The excellent thermal insulation and heat retention properties of RHA, along with its pore-refining effect, make a positive contribution to enhancing the frost resistance of the specimens. These findings contribute to guiding the effective application of RHA in civil engineering, offering eco-friendly solutions for biomass waste management, and promoting the sustainable development of construction materials.

Wu Jiangxing, Wang Hanhuan, Gao Yantao et al.

To investigate the damage evolution and failure mechanisms of fiber-reinforced composite materials under complex conditions, this study conducted in situ X-ray computed tomography (CT) compression and tensile tests on plain weave two-dimensional woven SiC/SiC composite materials. The obtained CT in situ image data captured the behavior of materials during loading and after failure. Using the image reconstruction of CT data, the actual microstructure and damage evolution of the material under six consecutive loading levels were accurately revealed. Three-dimensional visualization models of the composite material were established using image processing software to analyze the damage evolution under compression and tension, and the failure mechanisms were compared. The results showed that the compression and tension failure mechanisms of SiC/SiC composite materials were similar, with the transverse cracking of the matrix being the first mode of damage, followed by delamination between layers and longitudinal matrix cracking of fiber bundles. Specifically, in terms of compression failure, the strength of the fiber bundle itself has a greater influence, and fiber fracture is the main cause of ultimate material failure. On the other hand, the primary cause of tensile failure is the presence of porosity defects generated during material fabrication. Consequently, the tensile material fails earlier and can withstand lower loads.

Ravinder S. Saini, Rayan Ibrahim H. Binduhayyim, Vishwanath Gurumurthy et al.

Abstract Aim: This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations. Methodology: Molecular docking was employed to predict the binding affinities, energetics, and steric features of MMA and PMMA with selected receptors involved in bone metabolism and tissue development, including RANKL, Fibronectin, BMP9, NOTCH2, and other related receptors. The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. Furthermore, molecular dynamics (MD) simulations over 100 nanoseconds were conducted using the GROMACS package to evaluate dynamic actions and structural stability. The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets. Results: The molecular docking studies elucidated promising interactions between PMMA and MMA with key biomolecular targets relevant to dental applications. MD simulation results provided strong evidence supporting the structural stability of PMMA complexes over time. Pharmacophore modeling highlighted the significance of carbonyl and hydroxyl groups as pharmacophoric features, indicating compounds with favorable biocompatibility profiles. Conclusion: This study underscores the potential of PMMA in dental applications, emphasizing its structural stability, molecular interactions, and safety considerations. These findings lay a foundation for future advancements in dental biomaterials, guiding the design and optimization of materials for enhanced biocompatibility. Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance. Graphical Abstract

Mohammed Yousri Silaa, Aissa Bencherif, Oscar Barambones

This paper presents a novel approach to address the challenges associated with the trajectory tracking control of wheeled mobile robots (WMRs). The proposed control approach is based on an indirect adaptive control PID using a neural network and discrete extended Kalman filter (IAPIDNN-DEKF). The proposed IAPIDNN-DEKF scheme uses the NN to identify the system Jacobian, which is used for tuning the PID gains using the stochastic gradient descent algorithm (SGD). The DEKF is proposed for state estimation (localization), and the NN adaptation improves the tracking error performance. By augmenting the state vector, the NN captures higher-order dynamics, enabling more accurate estimations, which improves trajectory tracking. Simulation studies in which a WMR is used in different scenarios are conducted to evaluate the effectiveness of the IAPIDNN-DEKF control. In order to demonstrate the effectiveness of the IAPIDNN-DEKF control, its performance is compared with direct adaptive NN (DA-NN) control, backstepping control (BSC) and an adaptive PID. On lemniscate, IAPIDNN-DEKF achieves RMSE values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.078769</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.12086</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.1672</mn></mrow></semantics></math></inline-formula>. On sinusoidal trajectories, the method yields RMSE values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.01233</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.015138</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.088707</mn></mrow></semantics></math></inline-formula>, and on sinusoidal with perturbation, RMSE values are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.021495</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.016504</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.090142</mn></mrow></semantics></math></inline-formula> in <i>x</i>, <i>y</i> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula>, respectively. These results demonstrate the superior performance of IAPIDNN-DEKF for achieving accurate control and state estimation. The proposed IAPIDNN-DEKF offers advantages in terms of accurate estimation, adaptability to dynamic environments and computational efficiency. This research contributes to the advancement of robust control techniques for WMRs and showcases the potential of IAPIDNN-DEKF to enhance trajectory tracking and state estimation capabilities in real-world applications.

Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu et al.

The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware. AI has the potential to accelerate materials discovery and design by more effectively exploring the chemical space compared to other computational methods or by trial-and-error. While substantial progress has been made on AI for materials data, benchmarks, and models, a barrier that has emerged is the lack of publicly available training data and open pre-trained models. To address this, we present a Meta FAIR release of the Open Materials 2024 (OMat24) large-scale open dataset and an accompanying set of pre-trained models. OMat24 contains over 110 million density functional theory (DFT) calculations focused on structural and compositional diversity. Our EquiformerV2 models achieve state-of-the-art performance on the Matbench Discovery leaderboard and are capable of predicting ground-state stability and formation energies to an F1 score above 0.9 and an accuracy of 20 meV/atom, respectively. We explore the impact of model size, auxiliary denoising objectives, and fine-tuning on performance across a range of datasets including OMat24, MPtraj, and Alexandria. The open release of the OMat24 dataset and models enables the research community to build upon our efforts and drive further advancements in AI-assisted materials science.

LIU Yingpeng, FU Hanguang

&#x00A0;Laser cladding nickel-base coatings are playing an important role in improving the surface properties of materials due to their high bonding strength and good wear resistance.However，the hardness and wear resistance of common nickel-based coatings are difficult to meet the application requirements of parts in harsh environment due to the lack of wear-resistant hard phase.In order to further improve the wear resistance of the coating，several strategies were proposed by domestic and foreign scholars: mechanically adding or in-situ synthesizing hard ceramic particles in the coating，or adding graphene，carbon nanotubes and rare earth oxides in the coating，or establishing a prediction model between the process parameters and the coating performance and then optimizing the process parameters using algorithms such as adaptive chaos differential evolution algorithm and gray correlation analysis，or using auxiliary processing in laser cladding.In this paper，the influence of coating composition，process parameters and auxiliary treatment on wear resistance of nickel-base composite coating was reviewed，and the development prospect of laser cladding nickel-base composite coating was put forward.

Ye Li, Jiayu He, Qiang Zhang et al.

This paper presents the design of two predefined-time active fault-tolerant controllers for the trajectory tracking of autonomous underwater vehicles (AUVs) which can address actuator faults without causing actuator saturation. The first controller offers improved steady-state trajectory tracking precision, while the second ensures a nonsingular property. Firstly, a predefined-time sliding mode controller is formulated based on a predefined-time disturbance observer by integrating a novel predefined-time auxiliary system to prevent the control input from exceeding the actuator’s physical limitations. Subsequently, a non-singular backstepping controller is introduced to circumvent potential singularities in the sliding mode controller, guaranteeing that the trajectory tracking error is uniformly ultimately bounded (UUB) within the predefined time. Additionally, theoretical analysis and simulation results are presented to illustrate the advantages of the proposed method.

Avinash Gupta, Kunnath Ranjith

Rofiques Salehin, Gregory B. Thompson, Christopher R. Weinberger

The transition metal carbides have been known to act as traps for hydrogen in steels as well as potential materials for hydrogen storage. Despite numerous experimental and a few theoretical studies, what impacts hydrogen trapping and storage in the transition metal carbides is not well understood. In this work, we use density functional theory to systematically investigate the bulk trapping and storage capabilities of the transition metal carbides. We specifically examine how trapping and storage changes with the transition metal, from the group IVB to group VIB, carbon concentration, and structure. Our results demonstrate a strong correlation between the trap energy and the number of valence electrons in the transition metal, suggesting that the group IVB transition metal carbides are the best carbides for trapping and storage. Hydrogen preferentially sits at octahedral interstices, e.g., the carbon vacancies, in the structure except in certain cases near the Me2C concentration when tetrahedral interstices, devoid of nearest neighbor carbon, can become more favorable. Our results further demonstrate that the lower the carbon concentration, the more hydrogen the transition metal carbide can store. These results demonstrate which carbides will act as the best traps for hydrogen.