Hasil untuk "Materials Science"

Weiran Zhang, P. Liaw, Yong Zhang

Y. Mishra, R. Adelung

Abstract In the last 15 years, more than 50,000 papers with zinc oxide (ZnO) in the title are listed within ISI database. The outstanding popularity of ZnO has many reasons; the most important one appears to be its multi-functionality, resulting in applications in physics, chemistry, electrical engineering, material science, energy, textile, rubber, additive manufacturing, cosmetics, and pharmaceutical or medicine, as well as the ease to grow all kinds of nano- and microstructures. A key structure is the tetrapod-shaped ZnO (T-ZnO), which we want to focus on in this mini-review to demonstrate the remarkable properties and multifunctionality of ZnO and motivate why even much more research and applications are likely to come in near future. As T-ZnO came into focus again mainly during the last 10 years, the big data problem in T-ZnO is not as severe as in ZnO; nevertheless, a complete overview is impossible. However, this brief T-ZnO overview attempts to cover the scopes toward advanced technologies; nanoelectronics/optoelectronics sensing devices; multifunctional composites/coatings; novel biomedical engineering materials; versatile energy harvesting candidates; and unique structures for applications in chemistry, cosmetics, pharmaceuticals, food, agriculture, engineering technologies, and many others. The 3D nanotechnology is a current mainstream in materials science/nanotechnology research, and T-ZnO contributes to this field by its simple synthesis of porous networks as sacrificial templates for any desired new cellular materials.

E. Olivetti, J. Cole, Edward Kim et al.

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains—enabled by techniques adapted from the field of natural language processing—therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. We discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

Fengman Li, Xiangyu Chen, Lipeng Ding et al.

Al–Cu–Li alloys have shown great potential for aerospace application due to their good combination of high strength and low density, but their high planar anisotropy have always hindered their application. In the present work, the influence of different deformation processes, including hot rolling (HR), cold rolling (CR), hot rolling + cold rolling (HR + CR), hot rolling + intermediate annealing + cold rolling (HR + IA + CR) on the microstructure and property anisotropy is systematically investigated for a 2195 Al–Cu–Li alloy. Among the four rolling processes, the HR sample exhibits the lowest yield strength, the highest elongation and the highest anisotropy level. The CR and HR + CR samples have a higher yield strength, decreased elongation and anisotropy level. While the HR + IA + CR sample achieves the combination high yield strength, good elongation and the lowest anisotropy level. The different rolling processes can affect the number density and size of the coarse Al7Cu2Fe phase (>1 μm), which can promote the recrystallization by PSN mechanism. As the number density of Al7Cu2Fe phase in the four samples follow: HR + IA + CR > CR > HR + CR > HR. The HR + IA + CR sample with the highest PSN particles density exhibits highest level of recrystallization and random texture distribution, giving rise to the low anisotropy of the alloy. The low number density of PSN particle and the occurrence of dynamic recrystallization suppress the recrystallization of the HR sample during solution treatment, resulting in strong anisotropy of the alloy. These results can provide key information for optimizing the mechanical properties of Al–Cu–Li alloys for aerospace applications.

Jiaming Xu, Ping Zhang, Puyou Ying et al.

Although AlTiN is the preferred hard-coating material for cutting tools, its limited thermal stability causes problems at high cutting temperatures. Multielement nitride coatings such as AlCrN coatings doped with Si, W, and Y lead to the enhanced hardness and thermal stability of cutting tools. In this study, (Al,Cr,Ti,Si,Y)N multielement nitride coatings were fabricated via multiarc ion plating from an AlCrTiSiY alloy target and vacuum annealed at various temperatures. The surface morphologies, crystal structures, mechanical properties, and wear performances of the samples were analyzed. The number of surface defects on the (Al,Cr,Ti,Si,Y)N coatings reduced as the treatment temperature increased to 1000 °C, but the number of these defects increased at higher temperatures. X-ray diffraction revealed a phase transition from the face-centered cubic structure to wurtzite-AlN of the (Al,Cr,Ti,Si,Y)N coatings after annealing at 1100 °C; the same phase transition was observed at 900 °C in (Al,Ti)N coatings, which were prepared for comparison. Nanoindentation tests revealed that the hardness of the (Al,Cr,Ti,Si,Y)N coatings peaked at 40.81 GPa after annealing at 900 °C and remained high (33.73 GPa) even after annealing at 1100 °C. Meanwhile, the hardness of the (Al,Ti)N coatings markedly decreased after annealing at 800 °C. Friction tests revealed stable wear resistance of the (Al,Cr,Ti,Si,Y)N coatings with a wear rate of ∼2 × 10−6 mm3/N·m after annealing at 1000 °C. The wear rate nearly doubled to ∼3.75 × 10−6 mm3/N·m at 1100 °C. Overall, the (Al,Cr,Ti,Si,Y)N coatings demonstrated improved thermal stability than the (Al,Ti)N coatings, showing promising potential for demanding high-temperature applications.

Ana S. Reis-Machado, Ana S. Reis-Machado, Ana V. M. Nunes et al.

Jane Greenberg, Pamela Boveda-Aguirre, John Allison et al.

The materials science community seeks to support the FAIR principles for computational simulation research. The MatCore Project was recently launched to address this need, with the goal of developing an overall metadata framework and accompanying guidelines. This paper reports on the MatCore goals and overall progress. Historical background context is provided, including a review of the principles underlying successful core metadata standards. The paper also presents selected MatCore examples and discusses future plans.

Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam et al.

Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use, and multimodal reasoning across text, code, tables, and visuals. This survey presents the first comprehensive, lifecycle-aligned taxonomy of data science agents, systematically analyzing and mapping forty-five systems onto the six stages of the end-to-end data science process: business understanding and data acquisition, exploratory analysis and visualization, feature engineering, model building and selection, interpretation and explanation, and deployment and monitoring. In addition to lifecycle coverage, we annotate each agent along five cross-cutting design dimensions: reasoning and planning style, modality integration, tool orchestration depth, learning and alignment methods, and trust, safety, and governance mechanisms. Beyond classification, we provide a critical synthesis of agent capabilities, highlight strengths and limitations at each stage, and review emerging benchmarks and evaluation practices. Our analysis identifies three key trends: most systems emphasize exploratory analysis, visualization, and modeling while neglecting business understanding, deployment, and monitoring; multimodal reasoning and tool orchestration remain unresolved challenges; and over 90% lack explicit trust and safety mechanisms. We conclude by outlining open challenges in alignment stability, explainability, governance, and robust evaluation frameworks, and propose future research directions to guide the development of robust, trustworthy, low-latency, transparent, and broadly accessible data science agents.

Jithendaraa Subramanian, Linda Hung, Daniel Schweigert et al.

Recent advances in materials discovery have been driven by structure-based models, particularly those using crystal graphs. While effective for computational datasets, these models are impractical for real-world applications where atomic structures are often unknown or difficult to obtain. We propose a scalable multimodal framework that learns directly from elemental composition and X-ray diffraction (XRD) -- two of the more available modalities in experimental workflows without requiring crystal structure input. Our architecture integrates modality-specific encoders with a cross-attention fusion module and is trained on the 5-million-sample Alexandria dataset. We present masked XRD modeling (MXM), and apply MXM and contrastive alignment as self-supervised pretraining strategies. Pretraining yields faster convergence (up to 4.2x speedup) and improves both accuracy and representation quality. We further demonstrate that multimodal performance scales more favorably with dataset size than unimodal baselines, with gains compounding at larger data regimes. Our results establish a path toward structure-free, experimentally grounded foundation models for materials science.

Moshood O. Bolarinwa, Aasif A. Dabbawala, Shamraiz Hussain Talib et al.

Single-atom catalysts (SACs) offer high efficiency and selectivity in chemical reactions but face challenges in converting CO2 to CO via the reverse water gas shift (RWGS) reactions. This study addresses these challenges by anchoring three noble metals (Ir, Pd, and Ru) onto titania (TiO2) and analyzing their performance. Comprehensive characterization techniques, including electron microscopy, confirmed the uniform dispersion of metal atoms on TiO2. Among the catalysts, Ir/TiO2 exhibited the best results, achieving an 84 % CO2 conversion rate and ∼98 % CO selectivity, surpassing Pd/TiO2 and Ru/TiO2, which gained 56 % and 52 % conversion, respectively. In-situ gas transmission electron microscopy revealed the catalytic behavior of Ir/TiO2, showing Ir atom mobility and the formation of ∼1 nm nanoclusters. Density functional theory (DFT) and in-situ diffuse reflectance infrared spectroscopy (DRIFTs) further explained that the atomically dispersed Ir sites in Ir/TiO2 follow a hydrogen-assisted mechanism, with the COOH* intermediate desorbing and dissociating into CO. These findings suggest SACs' potential to facilitate greener chemical processes and reduce greenhouse gas emissions.

Dagmara Słota, Josef Jampilek, Agnieszka Sobczak-Kupiec

Coating materials offers an intriguing solution for imparting inert implants with additional bioactive characteristics without changing underlying parameters such as mechanical strength. Metallic implants like endoprostheses or polymeric implants can be coated with a thin layer of bioactive film capable of stimulating bone-forming cells to proliferate or release a drug. However, irrespective of the final implantation site of such a coating biomaterial, it is necessary to conduct detailed mechanical and physicochemical in vitro analyses to determine its likely behavior under biological conditions. In this study, polymeric and composite coatings with hydroxyapatite obtained under UV light underwent incubation tests in four different artificial biological fluids: simulated body fluid (SBF), artificial saliva, Ringer’s fluid, and water (as the reference fluid). The potentiometric and conductometric properties, sorption capacity, and degradation rate of the coatings were examined. Furthermore, their hardness, modulus of elasticity, and deformation were determined. It was demonstrated that the coatings remained stable in SBF liquid at a pH value of around 7.4. In artificial saliva, the greatest degradation of the polymer matrix (ranging between 36.19% and 39.79%) and chipping of hydroxyapatite in the composite coatings were observed. Additionally, the effect of ceramics on sorption capacity was determined, with lower capacity noted with higher HA additions. Moreover, the evaluation of surface morphology supported by elemental microanalysis confirmed the appearance of new apatite layers on the surface as a result of incubation in SBF. Ceramics also influenced mechanical aspects, increasing hardness and modulus of elasticity. For the polymer coatings, the value was 11.48 ± 0.61, while for the composite coating with 15% ceramics, it increased more than eightfold to a value of 93.31 ± 11.18 N/mm². Based on the conducted studies, the effect of ceramics on the physicochemical as well as mechanical properties of the materials was determined, and their behavior in various biological fluids was evaluated. However, further studies, especially cytotoxicity analyses, are required to determine the potential use of the coatings as biomaterials.

Xudong Wen, Pan Long, Pan Long

Waldenê de Melo Moura, Carlos Renato dos Santos, Moisés José dos Santos Freitas et al.

The study of microgravity, a condition in which an object experiences near-zero weight, is a critical area of research with far-reaching implications for various scientific disciplines. Microgravity allows scientists to investigate fundamental physical phenomena influenced by Earth’s gravitational forces, opening up new possibilities in fields such as materials science, fluid dynamics, and biology. However, the complexity and cost of developing and conducting microgravity missions have historically limited the field to well-funded space agencies, universities with dedicated government funding, and large research institutions, creating a significant barrier to entry. This paper presents the MicroGravity Explorer Kit’s (MGX) design, a multifunctional platform for conducting microgravity experiments aboard suborbital rocket flights. The MGX aims to democratize access to microgravity research, making it accessible to high school students, undergraduates, and researchers. To ensure that the tool is versatile across different scenarios, the authors conducted a comprehensive literature review on microgravity experiments, and specific requirements for the MGX were established. The MGX is designed as an open-source platform that supports various experiments, reducing costs and accelerating development. The multipurpose experiment consists of a Jetson Nano computer with multiple sensors, such as inertial sensors, temperature and pressure, and two cameras with up to 4k resolution. The project also presents examples of codes for data acquisition and compression and the ability to process images and run machine learning algorithms to interpret results. The MGX seeks to promote greater participation and innovation in space sciences by simplifying the process and reducing barriers to entry. The design of a platform that can democratize access to space and research related to space sciences has the potential to lead to groundbreaking discoveries and advancements in materials science, fluid dynamics, and biology, with significant practical applications such as more efficient propulsion systems and novel materials with unique properties.

Miaomiao Wang, Xing Ming, Qiao Wang et al.

Magnesium potassium phosphate cement-based (MKPC) coating is regarded as an attractive green coating that improves the corrosion resistance of metals. Unfortunately, practical use remains challenging owing to the rapid solidification and hardening of MKPC coating. To overcome this issue, this work proposes a novel strategy to substitute a portion of potassium dihydrogen phosphate (KDP) with dipotassium hydrogen phosphate trihydrate (DKP) as phosphate source. Samples with or without KDP replacement were named KDP-DKP cement/coating and KDP cement/coating, respectively. The initial condensation time of KDP-DKP coating is more than 3 h, which is longer than that of KDP coating and can demand the actual construction needs. The anti-corrosion performance results display that protection efficiency (Pe%) of KDP-DKP coating is 95.33%, which is higher than 87.56% of KDP coating, indicating that the anti-corrosion performance of KDP-DKP coating is superior than that of KDP coating. Furthermore, durability and anti-corrosion mechanism of KDP-DKP coating were also investigated and explored through immersion and potentiodynamic polarization tests. The consequences demonstrate that KDP-DKP coating has excellent durability, and both magnesium and phosphate ions in the coating suppress the corrosion rate of Fe. In summary, this work developed a durable and eco-friendly MKPC coating, which provides feasible guidance for the practical application of MKPC anti-corrosion coating.

Yihan Xu, Yihan Li, Tianyan Chen et al.

Due to its exceptional quality as a biomedical metal, stainless steel is often utilized to produce a broad range of medical tools. The resistance of stainless steel to corrosion is a key indicator of how long and how effective it will serve its intended purpose, and it is an important factor in determining the biocompatibility of the material. However, due to the complex physiological environment within the human body, the corrosion management of medical-grade stainless steel is facing several challenges. In this article, an overview of the factors that influence the corrosion performance of medical-grade stainless steel is provided, and new technologies and methods that have been developed in recent years to improve the corrosion resistance are discussed. These cutting-edge methods are expected to improve the corrosion resistance and longevity of medical-grade stainless steel, providing strong support for the increased applicability of the material in the medical industry.

Prathamesh Satpute, Saurabh Tiwari, Maneet Gupta et al.

There is a significant potential for coding skills to transition fully to natural language in the future. In this context, large language models (LLMs) have shown impressive natural language processing abilities to generate sophisticated computer code for research tasks in various domains. We report the first study on the applicability of LLMs to perform computer experiments on microstructure pattern formation in model materials. In particular, we exploit LLM's ability to generate code for solving various types of phase-field-based partial differential equations (PDEs) that integrate additional physics to model material microstructures. The results indicate that LLMs have a remarkable capacity to generate multi-physics code and can effectively deal with materials microstructure problems up to a certain complexity. However, for complex multi-physics coupled PDEs for which a detailed understanding of the problem is required, LLMs fail to perform the task efficiently, since much more detailed instructions with many iterations of the same query are required to generate the desired output. Nonetheless, at their current stage of development and potential future advancements, LLMs offer a promising outlook for accelerating materials education and research by supporting beginners and experts in their physics-based methodology. We hope this paper will spur further interest to leverage LLMs as a supporting tool in the integrated computational materials engineering (ICME) approach to materials modeling and design.

Christoph Heinzl, Renata Georgia Raidou, Kristi Potter et al.

Materials science has a significant impact on society and its quality of life, e.g., through the development of safer, more durable, more economical, environmentally friendly, and sustainable materials. Visual computing in materials science integrates computer science disciplines from image processing, visualization, computer graphics, pattern recognition, computer vision, virtual and augmented reality, machine learning, to human-computer interaction, to support the acquisition, analysis, and synthesis of (visual) materials science data with computer resources. Therefore, visual computing may provide fundamentally new insights into materials science problems by facilitating the understanding, discovery, design, and usage of complex material systems. This seminar is considered as a follow-up of the Dagstuhl Seminar 19151 Visual Computing in Materials Sciences, held in April 2019. Since then, the field has kept evolving and many novel challenges have emerged, with regard to more traditional topics in visual computing, such as topology analysis or image processing and analysis, to recently emerging topics, such as uncertainty and ensemble analysis, and to the integration of new research disciplines and exploratory technologies, such machine learning and immersive analytics. With the current seminar, we target to strengthen and extend the collaboration between the domains of visual computing and materials science (and across visual computing disciplines), by foreseeing challenges and identifying novel directions of interdisciplinary work. We brought visual computing and visualization experts from academia, research centers, and industry together with domain experts, to uncover the overlaps of visual computing and materials science and to discover yet-unsolved challenges, on which we can collaborate to achieve a higher societal impact.

Qi Wang, Hechang Lei, Yanpeng Qi et al.

In this account, we will give an overview of our research progress on novel quantum properties in topological quantum materials with kagome lattice. Here, there are mainly two categories of kagome materials: magnetic kagome materials and nonmagnetic ones. On one hand, magnetic kagome materials mainly focus on the 3d transition-metal-based kagome systems, including Fe$_3$Sn$_2$, Co$_3$Sn$_2$S$_2$, YMn6Sn6, FeSn, and CoSn. The interplay between magnetism and topological bands manifests vital influence on the electronic response. For example, the existence of massive Dirac or Weyl fermions near the Fermi level signicantly enhances the magnitude of Berry curvature in momentum space, leading to a large intrinsic anomalous Hall effect. In addition, the peculiar frustrated structure of kagome materials enables them to host a topologically protected skyrmion lattice or noncoplaner spin texture, yielding a topological Hall effect that arises from the realspace Berry phase. On the other hand, nonmagnetic kagome materials in the absence of longrange magnetic order include CsV3Sb5 with the coexistence of superconductivity, charge density wave state, and band topology and van der Waals semiconductor Pd$_3$P$_2$S$_8$. For these two kagome materials, the tunability of electric response in terms of high pressure or carrier doping helps to reveal the interplay between electronic correlation effects and band topology and discover the novel emergent quantum phenomena in kagome materials.

Urko Petralanda, Yi Jiang, B. Andrei Bernevig et al.

We adapt the topological quantum chemistry formalism to layer groups, and apply it to study the band topology of 8,872 entries from the computational two-dimensional (2D) materials databases C2DB and MC2D. In our analysis, we find 4,073 topologically non-trivial or obstructed atomic insulator entries, including 905 topological insulators, 602 even-electron number topological semimetals, and 1,003 obstructed atomic insulators. We thus largely expand the library of known topological or obstructed materials in two dimensions, beyond the few hundreds known to date. We additionally classify the materials into four categories: experimentally existing, stable, computationally exfoliated, and not stable. We present a detailed analysis of the edge states emerging in a number of selected new materials, and compile a Topological 2D Materials Database (2D-TQCDB) containing the band structures and detailed topological properties of all the materials studied in this work. The methodology here developed is implemented in new programs available to the public, designed to study the topology of any non-magnetic monolayer or multilayer 2D material.

Brian Wright, Peter Alonzi, Ali Rivera

The definition of Data Science is a hotly debated topic. For many, the definition is a simple shortcut to Artificial Intelligence or Machine Learning. However, there is far more depth and nuance to the field of Data Science than a simple shortcut can provide. The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science. This model is based on identifying a unified understanding of the data work done across all areas of Data Science. It represents a generational leap forward in how we understand and teach Data Science. In this paper we will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI. From this foundation we will present our Undergraduate Major curriculum in Data Science and demonstrate how it prepares students to be well-rounded Data Science team members and leaders. The paper will conclude with an in-depth overview of the Foundations of Data Science course designed to introduce students to the field while also implementing proven STEM oriented pedagogical methods. These include, for example, specifications grading, active learning lectures, guest lectures from industry experts and weekly gamification labs.