Miriana Russo, Corrado Santoro, Federico Fausto Santoro
et al.
Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations.
Materials of engineering and construction. Mechanics of materials, Production of electric energy or power. Powerplants. Central stations
Strain engineering provides a powerful route for tuning the electronic properties of two-dimensional (2D) materials, but exploring the full multidimensional strain space with density functional theory (DFT) is computationally prohibitive due to the nonlinear coupling between normal and shear components. In this work, we introduce a Transformer-based, multi-target surrogate model framework that achieves DFT-level bandgap prediction accuracy, reaching a mean absolute error of 0.0103 eV while retaining full interpretability through attention-weight analysis. The learned self-attention map consistently identifies shear strain as the interaction center that influences both bandgap and phonon stability, an insight not readily captured by classical feature-importance metrics. This work establishes attention-based architectures as physically interpretable surrogate models for multi-property prediction, offering a generalizable strategy for accelerating deep elastic strain engineering in materials informatics.
Jalaj Maheshwari, Wensi Wu, Christopher N. Zelonis
et al.
Finite element (FE) simulations emulating transcatheter pulmonary valve (TPV) system deployment in patient-specific right ventricular outflow tracts (RVOT) assume material properties for the RVOT and adjacent tissues. Sensitivity of the deployment to variation in RVOT material properties is unknown. Moreover, the effect of a transannular patch stiffness and location on simulated TPV deployment has not been explored. A sensitivity analysis on the material properties of a patient-specific RVOT during TPV deployment, modeled as an uncoupled HGO material, was conducted using FEBioUncertainSCI. Further, the effects of a transannular patch during TPV deployment were analyzed by considering two patch locations and four patch stiffnesses. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Sensitivity analysis revealed that the shear modulus of the ground matrix (c), fiber modulus (k1), and fiber mean orientation angle (gamma) had the greatest effect on 95th %ile stress, whereas only c had the greatest effect on 95th %ile Lagrangian strain. First-order sensitivity indices contributed the greatest to the total-order sensitivity indices. Simulations using a transannular patch revealed that peak stress and strain were dependent on patch location. As stiffness of the patch increased, greater stress was observed at the interface connecting the patch to the RVOT, and stress in the patch itself increased while strain decreased. The total enclosed volume by the TPV device remained unchanged across all simulated patch cases. This study highlights that while uncertainties in tissue material properties and patch locations may influence functional outcomes, FE simulations provide a reliable framework for evaluating these outcomes in TPVR.
Karoliina Kajander, Nicole Nowak, Negin Vaziri
et al.
Abstract Macrophage metabolism is closely linked to their phenotype and function, which is why there is growing interest in studying the metabolic reprogramming of macrophages. Bioactive glass (BG) S53P4 is a bioactive material used especially in bone applications. Additionally, BG S53P4 has been shown to affect macrophages, but the mechanisms through which the possible immunomodulatory effects are conveyed remain unclear. According to the results presented here, the lipopolysaccharide (LPS) induced suppression in oxidative phosphorylation is rescued in macrophages cultured with BG S53P4 before the inflammatory stimulus. Additionally, BG S53P4-exposed macrophages expressed lower mRNA levels of inflammatory cytokines Il6 and Il1b, as well as demonstrated decreased activation of inflammatory interferon regulatory factor (IRF) and NF-κB pathways and nitrogen oxide secretion in response to LPS. These results did not rely on cells being in direct contact with the material as similar effects were observed in the presence of BG S53P4-conditioned medium. Our findings link the immunomodulatory properties of BG S53P4 and macrophage metabolism, which improves our understanding of the mechanisms underlying the clinical efficacy of bioactive glasses. Graphical Abstract
Materials of engineering and construction. Mechanics of materials, Medical technology
Two-dimensional (2D) materials have shown broad application prospects in fields such as energy, environment, and aerospace owing to their unique electrical, mechanical, thermal and other properties. With the development of artificial intelligence (AI), the discovery and design of novel 2D materials have been significantly accelerated. However, due to the lack of basic theories of material synthesis, identifying reliable synthesis processes for theoretically designed materials is a challenge. The emergence of large language model offers new approaches for the reliability prediction of material synthesis processes. However, its development is limited by the lack of publicly available datasets of material synthesis processes. To address this, we present the Material Synthesis 2025 (MatSyn25), a large-scale open dataset of 2D material synthesis processes. MatSyn25 contains 163,240 pieces of synthesis process information extracted from 85,160 high-quality research articles, each including basic material information and detailed synthesis process steps. Based on MatSyn25, we developed MatSyn AI which specializes in material synthesis, and provided an interactive web platform that enables multifaceted exploration of the dataset (https://matsynai.stpaper.cn/). MatSyn25 is publicly available, allowing the research community to build upon our work and further advance AI-assisted materials science.
The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. This review provides a comprehensive overview of smart, machine learning (ML)-driven approaches, emphasizing their role in predicting material properties, discovering novel compounds, and optimizing material structures. Key methodologies in this field include deep learning, graph neural networks, Bayesian optimization, and automated generative models (GANs, VAEs). These approaches enable the autonomous design of materials with tailored functionalities. By leveraging AutoML frameworks (AutoGluon, TPOT, and H2O.ai), researchers can automate the model selection, hyperparameter tuning, and feature engineering, significantly improving the efficiency of materials informatics. Furthermore, the integration of AI-driven robotic laboratories and high-throughput computing has established a fully automated pipeline for rapid synthesis and experimental validation, drastically reducing the time and cost of material discovery. This review highlights real-world applications of automated ML-driven approaches in predicting mechanical, thermal, electrical, and optical properties of materials, demonstrating successful cases in superconductors, catalysts, photovoltaics, and energy storage systems. We also address key challenges, such as data quality, interpretability, and the integration of AutoML with quantum computing, which are essential for future advancements. Ultimately, combining AI with automated experimentation and computational modeling is transforming the way materials are discovered and optimized. This synergy paves the way for new innovations in energy, electronics, and nanotechnology.
The tunability of physical properties in transition metal dichalcogenides (TMDCs) through point defect engineering offers significant potential for the development of next-generation optoelectronic and high-tech applications. Building upon prior work on machine learning-driven material design, this study focuses on the systematic introduction and manipulation of point defects in MoS2 to tailor their properties. Leveraging a comprehensive dataset generated via density functional theory (DFT) calculations, we explore the effects of various defect types and concentrations on the mate rial characteristics of TMDCs. Our methodology integrates the use of pre-trained large language models to generate defect configurations, enabling efficient predictions of defect-induced property modifications. This research differs from traditional methods of material generation and discovery by utilizing the latest advances in transformer model architecture, which have proven to be efficient and accurate discrete predictors. In contrast to high-throughput methods where configurations are generated randomly and then screened based on their physical properties, our approach not only enhances the understanding of defect-property relationships in TMDCs but also provides a robust framework for designing materials with bespoke properties. This facilitates the advancement of materials science and technology.
Arun Negemiya, Selvarajan Rajakumar, Tushar Sonar
et al.
The dissimilar welding of Inconel 718 (IN-718) alloy and AISI 410 martensitic stainless steel (MSS-410) is crucial in advanced gas turbines, and ultra-supercritical power plants to meet the demands of different operating conditions and lower the cost. However, the dissimilar fusion welding of IN-718/MSS-410 is challenging due to the differences in thermal expansion coefficient, physical and mechanical properties of base metals. In this study, the solid-state vacuum diffusion bonding (VDB) technology is employed to develop the dissimilar IN-718/MSS-410 joints. The aim of this study is to find the optimal combination of VDB parameters such as diffusion bonding pressure-DBP (MPa), diffusion bonding temperature-DBT (°C) and diffusion bonding time-DBt (min) for enhancing the strength of IN-718/MSS-410 joints. The response surface methodology (RSM) was integrated for designing the experimental matrix. The strength performance of VDB joints was evaluated by conducting the lap shear strength (LSS) and bonding strength (BS) tests. The mathematical LSS and BS predicting models were established using regression analysis and verified employing the variance analysis. The microstructural features were analyzed using optical and scanning electron microscopy (SEM). The X-ray diffractometer (XRD) was employed to identify the phases evolution in the joint interface. The experimental results revealed that the IN-718/MSS-410 joints diffusion bonded using the DBP of 14 MPa, DBT of 960 °C and DBt of 90 min exhibited the greater LSS of 280 MPa and BS of 373 MPa. The prediction models accurately predicted the LSS and BS of IN-718/MSS-410 joints within 2 % error at 95 % confidence. It is primarily concerned with developing the optimal bonding width with the fewest possible embrittlement implications and better joining interface coalescence. According to variance analysis, the DBt was the most significant parameter influencing the LSS and BS of joints followed by the DBP and DBT.
Materials of engineering and construction. Mechanics of materials
Homogenization is a technique for the analysis of complex materials by replacing them with equivalent homogeneous materials that exhibit similar properties. By constructing a three-dimensional (3D) porous material model and employing homogenization technique, effective properties of the hydrogel pore structure were calculated. It is found that the microstructure of hyperelastic materials has considerable influence on their macroscopic mechanical properties, pores with a radius of up to 65 microns at a small strain can significantly reinforce material structure and improve its fatigue resistance. This work highlights the potential of engineering pore structures for the enhancement of mchanical properties and durability of hydrogels.
Benedikt Sterr, Sebastian Gajek, Andrew Hrymak
et al.
We extend the laminate based framework of direct Deep Material Networks (DMNs) to treat suspensions of rigid fibers in a non-Newtonian solvent. To do so, we derive two-phase homogenization blocks that are capable of treating incompressible fluid phases and infinite material contrast. In particular, we leverage existing results for linear elastic laminates to identify closed form expressions for the linear homogenization functions of two-phase layered emulsions. To treat infinite material contrast, we rely on the repeated layering of two-phase layered emulsions in the form of coated layered materials. We derive necessary and sufficient conditions which ensure that the effective properties of coated layered materials with incompressible phases are non-singular, even if one of the phases is rigid. With the derived homogenization blocks and non-singularity conditions at hand, we present a novel DMN architecture, which we name the Flexible DMN (FDMN) architecture. We build and train FDMNs to predict the effective stress response of shear-thinning fiber suspensions with a Cross-type matrix material. For 31 fiber orientation states, six load cases, and over a wide range of shear rates relevant to engineering processes, the FDMNs achieve validation errors below 4.31% when compared to direct numerical simulations with Fast-Fourier-Transform based computational techniques. Compared to a conventional machine learning approach introduced previously by the consortium of authors, FDMNs offer better accuracy at an increased computational cost for the considered material and flow scenarios.
Porous ceramics are favored in a multitude of applications, such as filters, catalyst supports, and tissue engineering scaffolds. However, conventional fabrication techniques find it particularly challenging to preserve sufficient mechanical strength in highly porous ceramics. Although unidirectional freeze casting can fabricate porous ceramics with high strength vertically, the strength in other directions is inadequate due to a lack of lateral structural control. Herein, inspired by the cancellous bone, we propose a novel multi-directional freeze casting technique to prepare highly mechanically efficient porous ceramics. A multi-directional temperature field is ingeniously designed to mimic the stress-responsive growth pattern of the cancellous bone. To further the lateral structural control, ceramic fibers are incorporated to form mineral bridging. In this process, alumina-mullite composite ceramics are prepared with hierarchical structures, including micro-level multi-oriented struts, sub-micro-level interlamellar bridges and nano-level eutectic phases. They endow the ceramics with high porosity (∼75%) and high strength in all 3D spatial directions (8.4–20.1 MPa), while effectively preventing the catastrophic brittle failure. Therefore, the mechanically enhanced porous ceramics demonstrate the remarkable controllability of multi-directional freeze casting in hierarchical structures. Also, our work opens up a new horizon for fabricating highly mechanically efficient porous materials, including hierarchically structured biomimetic ceramics.
Materials of engineering and construction. Mechanics of materials
Alessandro Taloni, Michele Vodret, Giulio Costantini
et al.
Micro and nanoscale materials have remarkable mechanical properties, such as enhanced strength and toughness, but usually display sample-to-sample fluctuations and non-trivial size effects, a nuisance for engineering applications and an intriguing problem for science. Our understanding of size-effects in small-scale materials has progressed considerably in the past few years thanks to a growing number of experimental measurements on carbon based nanomaterials, such as graphene carbon nanotubes, and on crystalline and amorphous micro/nanopillars and micro/nanowires. At the same time, increased computational power allowed atomistic simulations to reach experimentally relevant sample sizes. From the theoretical point of view, the standard analysis and interpretation of experimental and computational data relies on traditional extreme value theories developed decades ago for macroscopic samples, with recent work extending some of the limiting assumptions of the original theories. In this review, we discuss the recent experimental and numerical literature on micro and nanoscale fracture size effects, illustrate existing theories pointing out their advantages and limitations and finally provide a tutorial for analyzing fracture data from micro and nanoscale samples. We discuss a broad spectrum of materials but provide at the same time a unifying theoretical framework that should be helpful for materials scientists working on micro and nanoscale mechanics.
The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)-based methods are faster and more accurate than DFT-based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.
Similar to Poisson's effect, mechanical coupling is a directional indirect response by a directional input loading. With the advance in manufacturing techniques of 3D complex geometry, architected materials with unit cells of finite volume rather than a point yield more degrees of freedom and foster exotic mechanical couplings such as axial-shear, axial-rotation, axial-bending, and axial-twisting. However, most structural materials have been built by the ad hoc design of mechanical couplings without theoretical support of elasticity, which does not provide general guidelines for mechanical couplings. Moreover, no comprehensive study of all the mechanical couplings of 3D lattices with symmetry operations has been undertaken. Therefore, we construct the decoupled micropolar elasticity tensor of 3D lattices to identify individual mechanical couplings correlated with the point groups. The decoupled micropolar elasticity tensors, classified with 32 point groups, provide 15 mechanical couplings for 3D lattices. Our findings help provide solid theoretical guidelines for the mechanical couplings of 3D structural materials with potential applications in various areas, including active metamaterials, sensors, actuators, elastic waveguides, and acoustics.
The purpose of this study is to investigate the influence of alloy composition on the evolvement rule of microstructure for ZL201 cast aluminium alloy. The ZL201 casting samples with different components were prepared by gravity diecasting process, the phase composition was characterized and calculated by x-ray diffraction (XRD) and thermo-calc (TC) software respectively, the evolvement rule of microstructures was observed by metallographic microscope (OM) and scanning electron microscope (SEM), and simulated by ProCAST software. The results show that the Al _2 Cu ( θ ) formed by Cu and Al matrix, which increased the free energy and reduced the precipitation temperature for the alloy system. For the microstructure of ZL201, the nucleation direction of Al-Cu was along the positive direction of z -axis, the morphology was a regular ribbed morphology; Al-Mn and Al-Ti were along the negative directions of x - and y -axis and morphologies were columnar. Besides, the nucleation mode of casting center was dominated by the growth pattern of Al-Cu, and that of casting surface was dominated by Al-Ti and Al-Mn.
Materials of engineering and construction. Mechanics of materials, Chemical technology
Atomic modulations of two-dimensional materials using scanning tunneling microscope (STM) tip-induced forces modifies their mechanical and electrical properties. In situ topographic and spectroscopic probing through electrical tunneling has been used for straining sheets of graphite, monolayer graphene and NbSe2. The findings also resolve a thirty-five-year-old controversy involving numerous proposed models to explain the source of anomalously high measured atomic amplitudes (of up to 24 Angstroms, expected 0.2 Angstroms) from atomic corrugation on graphite surfaces. Our findings attributes the anomaly to surface elastic deformation characteristics of soft 2D monatomic sheets of graphene and graphite in contrast to NbSe2 which is associated with their local bonding configurations. The tip-induced deformations are shown to induce controlled strain on the material surface atomically and it offers a new way for strain engineering. Topographic deformation of formed graphitic Moire patterns reveals the inter-layer van der Waals (vdW) strength varying across its domains. In-situ tunneling spectroscopy associated with straining of the Moire domains reveals electronic flat band formation controllably thereby creating a platform for many-body correlations. The paper cautions anomalous observations when probing 2D materials at small gap distances with their strain induced effects and provides guidelines to exploit or avoid this effect.