Hasil untuk "Materials Science"

M. Jones, N. Seeman, C. Mirkin

M. Titirici, Robin J. White, N. Brun et al.

F. Würthner, T. Kaiser, C. R. Saha-Möller

Weifeng Wei, Xinwei Cui, Weixing Chen et al.

K. Shibata, O. Iwamoto, T. Nakagawa et al.

M. Aulton

D. Badgujar, M. B. Talawar, S. N. Asthana et al.

Ting Zhu, Ju Li

L. Mañosa, A. Planes

Wenting Li, B. Dong, Zhengxian Yang et al.

Mina Zolfaghari, Abbas Yadegar, Atefe Rezaei et al.

In the present research, zinc oxide (ZnO) nanoparticles (NPs) were biosynthesized through reduction by Anvillea garcinii leaf extract. A. garcinii leaves contain bioactive sesquiterpenes, terpenoids, and phenolic compounds, which are likely responsible for the reduction and stabilization of ZnO NPs. Compared to conventional physicochemical approaches, this synthesis method has several advantages, including simplicity, low cost, sustainability, and replicability. In this study, the impacts of various calcination (annealing) temperatures (60 °C and 500 °C) and different pHs (8, 10, and 12) on the properties of green-synthesized ZnO NPs were evaluated. Characterization was performed by analytical instruments including UV-Vis spectroscopy, Fourier transform infrared (FT-IR) spectroscopy and X-ray diffraction (XRD) analyses, nanoparticle analyzer, and field emission scanning electron microscope (FE-SEM). The UV–Vis adsorption spectra of the ZnO NPs revealed a prominent peak at approximately 230 nm. The observed peaks in FTIR spectra align well with those reported in various studies on ZnO NPs. By microscopic observation and XRD validation, the spherical and hexagonal nature of ZnO NPs was confirmed. The pH and temperature used were effective on the particle size, so that the smallest NPs (16.4 nm) were obtained with the help of the most alkaline synthesis medium (pH 12) and oven drying (60 °C). While the largest dimension (63 nm) corresponded to the NPs synthesized under the lowest pH (8) and dried with a 500 °C furnace. Synthesized NPs exhibited high antioxidant properties. The small sizes of biosynthesized ZnO NPs and their phytochemical-coated surfaces affected their biological activity. The cytotoxic impact of NPs on the gastric cancer cells was dose-dependent, and IC50 values for ZnO prepared at 60 and 500 °C (coded as ZnO-60 and ZnO-500) were 35.11 and 42.7 μg/mL respectively. In addition, they were potent antimicrobial agents against Gram-negative bacteria Escherichia coli and 3 strains of Helicobacter pylori, and Gram-positive bacterium Staphylococcus aureus. The green synthesis of ZnO NPs represents a sustainable approach that minimizes environmental impact while producing effective nanomaterials. By using natural plant extracts, researchers can develop cost-effective and eco-friendly methods for NP production, enhancing their potential applications across diverse sectors such as medical fields, environmental science, and materials engineering.

Yuqi An, Zhenbin Wang

Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles calculations in crystal structure prediction (CSP). However, their effectiveness in identifying new, complex materials remains uncertain. Here, we systematically assess the capability of a uMLIP (i.e.,M3GNet) to accelerate CSP in quaternary oxides. Through extensive exploration of the Sr-Li-Al-O and Ba-Y-Al-O systems, we show that uMLIP can rediscover experimentally known materials absent from its training set and identify seven new thermodynamically and dynamically stable compounds. These include a new polymorph of Sr2LiAlO4 (P3221) and a new disordered phase, Sr2Li4Al2O7 (P1_bar). Furthermore, our results show stability predictions based on the semilocal PBE functional require cross-validation with higher-level methods, such as SCAN and RPA, to ensure reliability. While uMLIPs substantially reduce the computational cost of CSP, the primary bottleneck has shifted to the efficiency of search algorithms in navigating complex structural spaces. This work highlights both the promise and current limitations of uMLIP-driven CSP in the discovery of new materials.

Yingli Liu, Chen Niu, Zhuo Wang et al.

Abstract Discovering new materials with excellent performance is a hot issue in the materials genome initiative. Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.

Lívia Vásárhelyi, Zoltán Kónya, Á. Kukovecz et al.

Abstract Micro-computed tomography (CT) is an X-ray tomography technique with (sub)micron resolution, typically using an X-ray tube with cone-beam geometry as a source and a rotating sample holder. While conventional CT maintained a strong position in life science and low-resolution high-energy CT became widespread in industrial quality control, micro-CT has enjoyed a boost in interest from the materials science research community in the past decade. The key reasons behind this are the versatile, non-destructive nature of micro-CT as a characterization method offering also in situ and in operando possibilities and the fact that micro-CT has become indispensable in developing and verifying computational material models as well. The goal of the present mini review is to give a concise introduction of the method to newcomers and showcase a few impressive recent results that can help in devising even more innovative future uses of micro-CT. After a brief overview of alternative three-dimensional imaging techniques, we review the basics of micro-CT covering important concepts such as resolution, magnification, and the Hounsfield unit. The second part of the article summarizes characteristic materials science micro-CT applications in bioinspired materials, structural materials, porous natural materials, energy storage, energy conversion, and filtration.

Qiaoli Chen, C. Dwyer, Guan Sheng et al.

Electron microscopy allows the extraction of multidimensional spatiotemporally correlated structural information of diverse materials down to atomic resolution, which is essential for figuring out their structure–property relationships. Unfortunately, the high‐energy electrons that carry this important information can cause damage by modulating the structures of the materials. This has become a significant problem concerning the recent boost in materials science applications of a wide range of beam‐sensitive materials, including metal–organic frameworks, covalent–organic frameworks, organic–inorganic hybrid materials, 2D materials, and zeolites. To this end, developing electron microscopy techniques that minimize the electron beam damage for the extraction of intrinsic structural information turns out to be a compelling but challenging need. This article provides a comprehensive review on the revolutionary strategies toward the electron microscopic imaging of beam‐sensitive materials and associated materials science discoveries, based on the principles of electron–matter interaction and mechanisms of electron beam damage. Finally, perspectives and future trends in this field are put forward.

Alfonso J Carrillo, María Balaguer, Cecilia Solís et al.

Nanoparticle exsolution is a powerful technique for functionalizing redox oxides in energy applications, particularly at high temperatures. It shows promise for solid oxide fuel cells and electrolyzers. However, exsolution of other chemistries like metal oxides is not well studied, and the mechanism is poorly understood. This work explores oxide exsolution in PrBa _1− _x Co _2 O _6− _δ ( x = 0, 0.05, 0.1, 0.15) double perovskites, practiced electrodes in proton ceramic fuel cells and electrolyzers. Oxide exsolution in PrBa _1− _x Co _2 O _6− _δ aimed at boosting the electrocatalytic activity and was evaluated by varying intrinsic materials-related properties, viz. A-site deficiency and external parameters (temperature, under fixed time, and p O _2 = 10 ^−5 atm conditions). The materials were analyzed with conventional characterization tools and synchrotron-based small-angle x-ray scattering. Unlike metal-nanoparticle exsolution, increasing the A-site deficiency did not enhance the extent of oxide-nanoparticle exsolution, whereas larger nanoparticles were obtained by increasing the exsolution temperature. Combined Raman spectroscopy and electron microscopy analysis revealed that BaCoO _3 , Co _3 O _4 , and amorphous BaCO _3 nanoparticles were formed on the surface of the double perovskites after the reductive treatments. The present results demonstrate the complexity of oxide-nanoparticle exsolution in comparison with metal-nanoparticle exsolution. Further materials screening and mechanistic studies are needed to enhance our understanding of this method for functionalizing proton ceramic electrochemical cells (PCEC) electrodes.

Ying Liu, Francesco Di Stasio, Chenghao Bi et al.

Near-Infrared (NIR) light emitting metal halides are emerging as a new generation of optical materials owing to their appealing features, which include low-cost synthesis, solution processability and adjustable optical properties. NIR emitting perovskite-based light-emitting diodes (LEDs) have reached an external quantum efficiency (EQE) over 20% and a device stability of over 10,000 h. Such results have sparked an interest in exploring new NIR metal halide emitters. In this review, we summarize several different types of NIR-emitting metal halides, including lead/tin bromide/iodide perovskites, lanthanide ions doped/based metal halides, double perovskites, low dimensional hybrid and Bi3+/Sb3+/Cr3+ doped metal halides, and assess their recent advancements. The characteristics and mechanisms of narrow-band or broadband NIR luminescence in all these materials are discussed in detail. We also highlight the various applications of NIR-emitting metal halides and provide an outlook for the field.

Wenkai Ning, Musen Li, Jeffrey R. Reimers et al.

Large Language Models (LLMs) are increasingly utilized for large-scale extraction and organization of unstructured data owing to their exceptional Natural Language Processing (NLP) capabilities. Empowering materials design, vast amounts of data from experiments and simulations are scattered across numerous scientific publications, but high-quality experimental databases are scarce. This study considers the effectiveness and practicality of five representative AI tools (ChemDataExtractor, BERT-PSIE, ChatExtract, LangChain, and Kimi) to extract bandgaps from 200 randomly selected Materials Science publications in two presentations (arXiv and publisher versions), comparing the results to those obtained by human processing. Although the integrity of data extraction has not met expectations, encouraging results have been achieved in terms of precision and the ability to eliminate irrelevant papers from human consideration. Our analysis highlights both the strengths and limitations of these tools, offering insights into improving future data extraction techniques for enhanced scientific discovery and innovation. In conjunction with recent research, we provide guidance on feasible improvements for future data extraction methodologies, helping to bridge the gap between unstructured scientific data and structured, actionable databases.

E. Giessen, P. Schultz, N. Bertin et al.

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

Wei‐Hung Chiang, D. Mariotti, R. M. Sankaran et al.

Microplasmas are low‐temperature plasmas that feature microscale dimensions and a unique high‐energy‐density and a nonequilibrium reactive environment, which makes them promising for the fabrication of advanced nanomaterials and devices for diverse applications. Here, recent microplasma applications are examined, spanning from high‐throughput, printing‐technology‐compatible synthesis of nanocrystalline particles of common materials types, to water purification and optoelectronic devices. Microplasmas combined with gaseous and/or liquid media at low temperatures and atmospheric pressure open new ways to form advanced functional materials and devices. Specific examples include gas‐phase, substrate‐free, plasma‐liquid, and surface‐supported synthesis of metallic, semiconducting, metal oxide, and carbon‐based nanomaterials. Representative applications of microplasmas of particular importance to materials science and technology include light sources for multipurpose, efficient VUV/UV light sources for photochemical materials processing and spectroscopic materials analysis, surface disinfection, water purification, active electromagnetic devices based on artificial microplasma optical materials, and other devices and systems including the plasma transistor. The current limitations and future opportunities for microplasma applications in materials related fields are highlighted.