Knowledge Of Materials Science Research Articles

Fracture performance of fiber reinforced geopolymer: Synergetic perspective

It is well-known the hybridization of different types of fibers exaggerates the contribution of mono fiber reinforcement on the mechanical and/or fresh properties of the concrete. Despite this fact, very few researchers have pointed out the synergetic effect of hybrid fibers on the fracture response of the geopolymer concretes. Hence, the present work seeks to extend the current knowledge of material science to cover the synergetic effect of hybrid fibers. To this end, a total of 15 mixtures, including plain matrix were manufactured. Steel, carbon, and glass fibers were incorporated into the mixtures at varying ratios. For the fracture evaluations, the rectangular prisms with the central notches were exposed to a three-point static bending test. Crack formations on the specimen surfaces were recorded through the 2D digital image correlation (DIC) method. Test results were examined in terms of some mechanical and fracture properties of geopolymer composites. Accordingly, the compressive strength, Young's modulus, flexural strength, limit of proportionality, and residual strengths were evaluated as the mechanical properties, while the fracture energy, characteristic length, effective crack length, initial stress intensity factor, and unstable stress intensity factor were considered as the fracture properties. Based on the experimental results, carbon fiber was found to be capable of increasing pre-peak mechanical response, while the steel and glass fibers were useful in improving the post-peak fracture properties of geopolymer concretes. In most cases, similar results were also attained for the hybrid fiber-reinforced concretes.

MaScQA: investigating materials science knowledge of large language models

Different materials science domains from which questions are present in Materials Science Question Answering (MaScQA) database.

Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing.

Materials science research has garnered extensive attention from industry, society, policy, and academia. However, understanding the research landscape and extracting strategic insights are challenging due to the increasing diversity and volume of publications. This study proposes a natural language processing-based protocol for extracting text-encoded topics from a large volume of scientific literature, uncovering research interests of scientific communities, as well as convergence trends. We report a topic map, representing the materials science research landscape with text-mined 257 topics regarding biocompatible materials, structural materials, electrochemistry, or photonics. We analyze the topic map in terms of national research interests in materials science, revealing competitive positions and strategies of active nations. For example, it is found that the increasing trend of research interest in machine learning topic was captured in the United States earlier than other nations. Similarly, our journal-level analyses serve as reference information for journal recommendations and trend guidance, showing that the main topics and research interests of materials science journals slightly changed over time. Moreover, we build the topic association network which can highlight the status and future potential of interdisciplinary research, revealing research fields with high centrality in the network such as machine learning-enabled composite modeling, energy policy, or wearable electronics. This study offers insightful results on current and near-future materials science research landscapes, facilitating the understanding of stakeholders, amidst the fast-evolving and diverse knowledge of materials science.

MatChat: A large language model and application service platform for materials science

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence (GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data. This specialized AI model, named MatChat, focuses on predicting inorganic material synthesis pathways. MatChat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although MatChat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. MatChat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.

Superior and ultrafast energy storage performance of relaxorantiferroelectric HfO2-based supercapacitors

Compared to electrochemical energy storage, dielectric thin film-based capacitors possess the advantages of higher voltage stability and higher break-down voltage as well as lower leakage current etc. Since HfO2 films are compatible to microelectronic process and its ferroelectricity is strategically important in memory device, the realization of their excellent energy storage performance will broaden their applications in microelectronic devices. In this study, we utilized Al3+ dopant, known for its smaller ionic radius compared to Hf4+, to induce lattice disorder in the Hf0.5Zr0.5O2 films, facilitating transformation of the Hf0.5Zr0.5O2 film from ferroelectric to antiferroelectric, and ultimately to a superparaelectric-like relaxation antiferroelectric behaviors. We have experimentally demonstrated that, by introduction of an appropriate Al doping level of x = 4.13%, the Hf0.5Zr0.5O2 thin films with a relaxor antiferroelectric characteristic can achieve a corresponding recoverable energy density of more than 100 J cm−3, an efficiency of more than 80%, and an effective enhancement of the dielectric strength to more than 6 MV cm−1. Compared to HfO2-based amorphous/crystalline films reported previously, this is a record-high energy density in atomic layer deposited HfO2/ZrO2 ternary doped oxides, and beyond that, we also demonstrate their superfast charging/discharging as a capacitor. Targeting at high-speed supercapacitor applications in integrated circuit, HfO2-based dielectric may win the competition with perovskite oxides in terms of dielectric breakdown strength and charging/discharging speed etc. Our fundamental understanding of the physics behind also enriches the knowledge of materials science and dielectric physics.

Analysis of the Main Characteristics of Infrared Thermography and Ultrasonic Methods for Diagnosing Technical Object

Diagnostics of building structures is a painstaking work that requires the use of non-destructive testing methods, the main task of which is the use of research methods for diagnostic and control studies. The method of infrared thermography and the ultrasonic method of diagnostics of technical objects are of particular importance in this regard. The use of the methods of infrared thermography and ultrasonic control makes it possible to carry out observations and monitoring of historical and modern buildings in terms of the destruction of materials and structures, while the buildings themselves remain intact. For each of these methods, different measuring instruments are used, equipped with special sensors for qualitative analysis. Achievements in laboratory testing using these methods and devices allow materials to be tested based on measurable parameters. Knowledge and experience in the field of infrared thermography and ultrasonic testing are relevant for innovative research in the field of modern materials technology, building science and maintenance methods. Such research requires an interdisciplinary approach that combines the knowledge of materials science and building science.

An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor.

In the field of soft robotics, knowledge of material science is becoming more and more important. However, many researchers have a background in only one of both domains. To aid the understanding of the other domain, this tutorial describes the complete process from polymer synthesis over fabrication to testing of a soft finger. Enough background is provided during the tutorial such that researchers from both fields can understand and sharpen their knowledge. Self-healing polymers are used in this tutorial, showing that these polymers that were once a specialty, have become accessible for broader use. The use of self-healing polymers allows soft robots to recover from fatal damage, as shown in this tutorial, which increases their lifespan significantly.

Teaching ceramic materials in mechanical engineering: An active learning experience

Teaching in higher education is a very demanding task, as students have easy access to information through a computer or mobile device, and nothing seems to surprise them. One of the main concerns of Mechanical Engineering professors is the transmission of the best general competencies and scientific knowledge, and the development of skills that will be very useful in their future professional life in a global employment market. In this way, teachers must continually innovate to motivate students and catch their attention, adopting new teaching methodologies, such as active learning. The main objectives of this teaching methodology are: to provide advanced knowledge in engineering; resolve of engineering problems; experimentation and knowledge discovering and, improvement of soft skills. This paper describes a project entitled “Production of a ceramic component,” included in the Curricular Unit of Non-Metallic Materials, proposed to the students of the third year, the first semester of a Mechanical Engineering course. Each group is assigned a ceramic component and is asked to research and prepare an oral presentation and a poster where they describe all the stages of the manufacturing process. They must consider component requirements, and the scientific and technical details of its production from materials selection to its sale. To carry out this assignment, students must review their basic knowledge of materials science, technical drawing, and materials and manufacture of ceramic components (subjects previously taught in different courses), and search in databases of material properties. Although this assignment is very challenging and time-consuming, students report in informal conversations promoted by teachers that they enjoy it and feel very motivated. They consider that this type of work better prepares them for their future professional life, by improving their soft skills.

MatSciBERT: A materials domain language model for text mining and information extraction

A large amount of materials science knowledge is generated and stored as text published in peer-reviewed scientific literature. While recent developments in natural language processing, such as Bidirectional Encoder Representations from Transformers (BERT) models, provide promising information extraction tools, these models may yield suboptimal results when applied on materials domain since they are not trained in materials science specific notations and jargons. Here, we present a materials-aware language model, namely, MatSciBERT, trained on a large corpus of peer-reviewed materials science publications. We show that MatSciBERT outperforms SciBERT, a language model trained on science corpus, and establish state-of-the-art results on three downstream tasks, named entity recognition, relation classification, and abstract classification. We make the pre-trained weights of MatSciBERT publicly accessible for accelerated materials discovery and information extraction from materials science texts.

First-principles modelling of the new generation of subnanometric metal clusters: Recent case studies

The very recent development of highly selective techniques making possible the synthesis and experimental characterization of subnanometric (subnanometer-sized) metal clusters (even single atoms) is pushing our understanding far beyond the present knowledge in materials science, driving these clusters as a new generation of quantum materials at the lower bounds of nanotechnology. When the size of the metal cluster is reduced to a small number of atoms, the d-band of the metal splits into a subnanometric d-type molecular orbitals network in which all metal atoms are inter-connected, with the inter-connections having the length of a chemical bond (1–2 Å). These molecular characteristics are at the very core of the high stability and novel properties of the smallest metal clusters, with their integration into colloidal materials interacting with the environment having the potential to further boost their performance in applications such as luminescence, sensing, bioimaging, theranostics, energy conversion, catalysis, and photocatalysis. Through the presentation of very recent case studies, this Feature Article is aimed to illustrate how first-principles modelling, including methods beyond the state-of-the-art and an interplay with cutting-edge experiments, is helping to understand the special properties of these clusters at the most fundamental level. Moreover, it will be discussed how superfluid helium droplets can act both as nano-reactors and carriers to achieve the synthesis and surface deposition of metal clusters. This concept will be illustrated with the quantum simulation of the helium droplet-assisted soft-landing of a single Au atom onto a titanium dioxide (TiO2) surface. Next, it will be shown how the application of first-principles methods have disclosed the fundamental reasons why subnanometric Cu5 clusters are resistant to irreversible oxidation, and capable of increasing and extending into the visible region the solar absorption of TiO2, of augmenting its efficiency for photo-catalysis beyond a factor of four, also considering the decomposition and photo-activation of CO2 as a prototypical (photo-) catalytic reaction. Finally, I will discuss how the modification of the same material with subnanometric Ag5 clusters has converted it into a “reporter” of a surface polaron property as well as a novel two-dimensional polaronic material.

An exploratory analysis: extracting materials science knowledge from unstructured scholarly data

PurposeThe output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including materials science, as it is impossible to manually read and extract knowledge from millions of published literature. The purpose of this study is to address this challenge by exploring knowledge extraction in materials science, as applied to digital scholarship. An overriding goal is to help inform readers about the status knowledge extraction in materials science.Design/methodology/approachThe authors conducted a two-part analysis, comparing knowledge extraction methods applied materials science scholarship, across a sample of 22 articles; followed by a comparison of HIVE-4-MAT, an ontology-based knowledge extraction and MatScholar, a named entity recognition (NER) application. This paper covers contextual background, and a review of three tiers of knowledge extraction (ontology-based, NER and relation extraction), followed by the research goals and approach.FindingsThe results indicate three key needs for researchers to consider for advancing knowledge extraction: the need for materials science focused corpora; the need for researchers to define the scope of the research being pursued, and the need to understand the tradeoffs among different knowledge extraction methods. This paper also points to future material science research potential with relation extraction and increased availability of ontologies.Originality/valueTo the best of the authors’ knowledge, there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.

Automated knowledge extraction from polymer literature using natural language processing.

SummaryMaterials science literature has grown exponentially in recent years making it difficult for individuals to master all of this information. This constrains the formulation of new hypotheses that scientists can come up with. In this work, we explore whether materials science knowledge can be automatically inferred from textual information contained in journal papers. Using a data set of 0.5 million polymer papers, we show, using natural language processing methods that vector representations trained for every word in our corpus can indeed capture this knowledge in a completely unsupervised manner. We perform time-based studies through which we track popularity of various polymers for different applications and predict new polymers for novel applications based solely on the domain knowledge contained in our data set. Using co-relations detected automatically from literature in this manner thus, opens up a new paradigm for materials discovery.

Imparting Materials Science Knowledge in the Field of the Crystal Structure of Metals in Times of Online Teaching: A Novel Online Laboratory Teaching Concept with an Augmented Reality Application

During the COVID-19 pandemic, many educators have been required to offer their courses online. A particular challenge is the implementation of practical laboratory experiments in the field of materials science. The central questions are the following: How can students carry out laboratory experiments at home? How are applications on mobile devices helpful in this context? How can experiments be organized to make students approach the topic with self-motivation and excitement? The concept presented in this paper combines the idea of practical work and the use of an augmented reality app. Guided by a well-structured online learning platform, video tutorials, and lab handouts, students were able to carry out their experiments from home. The results of the evaluation of this new laboratory experiment concept suggest that students very positively welcomed this form of education.

Identification of succinite of infrared absorption spectra

The solution of the problem of identification of amber (succinite) on the example of natural polymer material of organic origin on the basis of its infrared absorption spectra is proposed. Taking into account the growing demand for the use of such composite materials on an industrial scale and the insufficiency of existing methods for determining their composition, the proposed method can become a new solution to the problem of identification. The basis of the proposed method for the study of succinite using infrared spectroscopy is translucent spectroscopy, with the measurement of mirror and diffuse reflection. The samples amber of the Palmniken Deposit obtained in the city of Kaliningrad of the Russian Federation were used as the objects studied. The data of infrared spectra of succinite obtained by the proposed method of infrared spectroscopy are obtained and presented in comparison. Five main links on the obtained infrared spectra have been experimentally determined, which can be used as characteristic features for identification and diagnostics of succinite by the material composition. The proposed method may be of interest in the theory of knowledge of material science, research related to the processes of identification, processing of production of amber.

Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

Machine learning is becoming a valuable tool for scientific discovery. Particularly attractive is the application of machine learning methods to the field of materials development, which enables innovations by discovering new and better functional materials. To apply machine learning to actual materials development, close collaboration between scientists and machine learning tools is necessary. However, such collaboration has been so far impeded by the black box nature of many machine learning algorithms. It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics. Here, we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on prior knowledge of material science and physics, we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials. Guided by this, we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material. This material shows the largest thermopower to date.

Unsupervised word embeddings capture latent knowledge from materials science literature.

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3-10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11-13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

Baltic amber inspection: micro-macro-structural and luminescent analysis

The solution of the problem of amber (succinite) flaw detection by microscopic analysis and luminescent analysis of amber macrostructure is proposed. The proposed method allows to obtain a rapid analysis of the defective state of the material structure. Given the growing demand for the use of such materials on an industrial scale and the insufficiency of existing methods, the proposed method may be a new solution to the problem of flaw detection. The basis of the proposed method for studying the macro structure of amber is transmission spectroscopy, luminescent analysis method. Samples of raw, heat-treated and pressed amber of the Palmniken Deposit obtained in the city of Kaliningrad of the Russian Federation were used as objects of research. Samples of different degrees of transparency and heat treatment were selected. It is established that the distinctive luminescence intensity at the defect boundaries is a characteristic of the presence of internal defects of the material and indicates the location of internal fractures, microcracks and other mechanical damage. The proposed method may be of interest in the theory of knowledge of materials science, research related to the processes of flaw detection of organic materials.

SERVICE LIFE PREDICTION FOR CONCRETE COMPOSITE WITH CARBON FIBRES FOR MARINE ENVIRONMENT

As construction technologies are improved and as we go for advanced technologies in construction of infrastructure facilities the importance of concrete technology is also more demanding. The field of concrete technology has many new admixtures which improves the properties of concrete. Durability and performance of structures are the main focus now. In view of this, apart from just proportioning a concrete mix, researchers are now interested in testing the performance of the material in varied environmental conditions. Service life prediction is the evaluation of the performance of the structure over a period of time. The prediction involves knowledge of materials science, laboratory testing and data from structures that are in service. It is a complex area where interpretation of correct data has been used and it involves systematic approaches. Researchers have used numerous methodologies and mathematical formulae that are used for the service life prediction. Accelerated laboratory tests forms the basis for these kinds of predictions. This paper deals with proportioning of concrete composite that can be used in aggressive marine environment, subject to severe exposure and the service life prediction of the material in such environment. Admixtures play a major role in making concrete durable. This paper discusses the properties of concrete composite that has been customized with silica fume, fly ash and copper slag for improving the strength of concrete. Carbon fibres have been added to resist the impact of sea waves and also to improve the flexural toughness of concrete. As there are various factors that have been considered in proportioning this particular concrete mix, experimental trials have been designed with reference to central composite design of Response Surface methodology using Design Expert software. The trials were cast using these design mixes and tests were conducted for strength properties and durability parameters. The experimental results have been analyzed for ANOVA to test the accuracy of results. Multiple optimizations have been done to get the best mix with maximum strength and minimum durability issues. Article DOI: https://dx.doi.org/10.20319/mijst.2018.42.113124 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

On the Materials Science of Nature's Arms Race.

Biological material systems have evolved unique combinations of mechanical properties to fulfill their specific function through a series of ingenious designs. Seeking lessons from Nature by replicating the underlying principles of such biological materials offers new promise for creating unique combinations of properties in man-made systems. One case in point is Nature's means of attack and defense. During the long-term evolutionary "arms race," naturally evolved weapons have achieved exceptional mechanical efficiency with a synergy of effective offense and persistence-two characteristics that often tend to be mutually exclusive in many synthetic systems-which may present a notable source of new materials science knowledge and inspiration. This review categorizes Nature's weapons into ten distinct groups, and discusses the unique structural and mechanical designs of each group by taking representative systems as examples. The approach described is to extract the common principles underlying such designs that could be translated into man-made materials. Further, recent advances in replicating the design principles of natural weapons at differing lengthscales in artificial materials, devices and tools to tackle practical problems are revisited, and the challenges associated with biological and bioinspired materials research in terms of both processing and properties are discussed.

Non-empirical identification of trigger sites in heterogeneous processes using persistent homology

Macroscopic phenomena, such as fracture, corrosion, and degradation of materials, are associated with various reactions which progress heterogeneously. Thus, material properties are generally determined not by their averaged characteristics but by specific features in heterogeneity (or ‘trigger sites’) of phases, chemical states, etc., where the key reactions that dictate macroscopic properties initiate and propagate. Therefore, the identification of trigger sites is crucial for controlling macroscopic properties. However, this is a challenging task. Previous studies have attempted to identify trigger sites based on the knowledge of materials science derived from experimental data (‘empirical approach’). However, this approach becomes impractical when little is known about the reaction or when large multi-dimensional datasets, such as those with multiscale heterogeneities in time and/or space, are considered. Here, we introduce a new persistent homology approach for identifying trigger sites and apply it to the heterogeneous reduction of iron ore sinters. Four types of trigger sites, ‘hourglass’-shaped calcium ferrites and ‘island’- shaped iron oxides, were determined to initiate crack formation using only mapping data depicting the heterogeneities of phases and cracks without prior mechanistic information. The identification of these trigger sites can provide a design rule for reducing mechanical degradation during reduction.