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Quantum materials and metal‐organic framework (MOFs) materials describe two attractive research areas in physics and chemistry. Yet, with very few exceptions, these fields have been developed with little overlap. This review aims to summarize these efforts and outline the huge potential of considering MOFs as quantum materials, called quantum MOFs. Quantum MOFs exhibit macroscopic quantum states over wide energy and lengths scales. Examples are topological materials and superconductors, to name but a few. In contrast to conventional quantum materials, MOFs exhibit promising unconventional degrees of freedom such as buckling, interpenetration, porosity, and rotations, stimulating the design of novel quantum phases of matter.

The aim of this work is to explore the possibility of using a magnetic ionic liquid as both a lubricant and sealant in a vacuum pump. The current lubricant in use is due to become obsolete due to incoming legislation on fluorinated compounds. The long-term goal is to use a magnetic field to hold the novel lubricant in place to create a better seal and thus reduce pressure more effectively whilst also lubricating the necessary mechanical parts. This paper explores physical properties (contact angle, viscosity, coefficient of friction (COF) and magnetism) and chemical properties (thermal stability and chemical stability as deduced by X-ray photoelectron spectroscopy (XPS)) of three ionic liquids based on imidazolium cations with varying length of alkyl side chain and a tetrachloroferrate anion along with two control lubricants which are used currently in the field and based upon perfluoropolyether (PFPE). The work shows that the three magnetic ionic liquids possess a lower COF value than the two control lubricants when measured using a Bruker UMT TriboLab suggesting they may perform better in the real life vacuum pump. Upon analysis of the three ionic liquid samples after tribological testing using XPS, it was shown that the ionic liquid samples displayed minor differences in spectra suggesting good stability with no obvious decomposition or degradation of the sample.

Abstract Biomineralization is the intricate process by which living organisms orchestrate the formation of organic–inorganic composites by regulating the nucleation, orientation, growth, and assembly of inorganic minerals. As our comprehension of biomineralization principles deepens, novel strategies for fabricating inorganic materials based on these principles have emerged. Researchers can also harness biomineralization strategies to tackle challenges in both materials' science and biomedical fields, demonstrating a thriving research field. This review begins by introducing the concept of biomineralization and subsequently shifts its focus to a recently discovered chemical concept: inorganic ionic oligomers and their cross‐linking. As a novel approach for constructing inorganic materials, the inorganic ionic oligomer‐based strategy finds applications in biomimetic regeneration and repair of hard tissues, such as teeth and bones. Aside from innovative methods for material fabrication, biomineralization has emerged as an alternative method for tackling biomedical challenges by integrating materials with biological organisms, facilitating advancements in biomedical fields. Emerging material‐biological integrators play a critical role in areas like vaccine improvement, cancer therapy, universal blood transfusion, and arthritis treatment. This review highlights the profound impact of biomineralization in the development and design of high‐performance materials that go beyond traditional disciplinary boundaries, potentially promoting breakthroughs in materials science, chemical biology, biomedical, and numerous other domains.

This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we evaluated using SuperMat, based on superconductor research, and MeasEval, a generic measurement evaluation corpus. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline). We introduce a novel methodology for the comparative analysis of intricate material expressions, emphasising the standardisation of chemical formulas to tackle the complexities inherent in materials science information assessment. For NER, LLMs fail to outperform the baseline with zero-shot prompting and exhibit only limited improvement with few-shot prompting. However, a GPT-3.5-Turbo fine-tuned with the appropriate strategy for RE outperforms all models, including the baseline. Without any fine-tuning, GPT-4 and GPT-4-Turbo display remarkable reasoning and relationship extraction capabilities after being provided with merely a couple of examples, surpassing the baseline. Overall, the results suggest that although LLMs demonstrate relevant reasoning skills in connecting concepts, specialised models are currently a better choice for tasks requiring extracting complex domain-specific entities like materials. These insights provide initial guidance applicable to other materials science sub-domains in future work.

Abstract The transient and elusive intermediate states are the keys in self‐assembly processes, which are common phenomena shaping the structure, properties, and functionalities of assembled materials across many scientific domains. However, the understanding about the intermediate states of self‐assembly process is always challenging and limited. In this review, we focus on these states by combining theoretical and experimental approaches. By examining a wide variety of self‐assembly systems that span from biological to metal–organic nanostructures, this review uncovers the wealth of intermediate states of self‐assembled materials. In addition to combining the current knowledge, it will identify challenges and provide a new insight into the opportunities for future research.

Abstract Generative adversarial networks (GANs), as a powerful tool for inverse materials discovery, are being increasingly applied in various fields of materials science. This review provides systematic investigations on the applications of GANs from a group of different aspects. The basic principles of GANs are first introduced; then a detailed review of GANs‐based studies regarding distinct scenarios across composition design, processing optimization, crystal structure search, microstructure characterization and defect detection is presented. At the end, several challenges and possible solutions are discussed and outlined. This overview highlights the efficacy of GANs in materials science, and may stimulate the further use of GANs for more intriguing achievements.

Generative Artificial Intelligence (GAI) is attracting the increasing attention of materials community for its excellent capability of generating required contents. With the introduction of Prompt paradigm and reinforcement learning from human feedback (RLHF), GAI shifts from the task-specific to general pattern gradually, enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships. Here, we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology. The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected. Taking ChatGPT as an example, we explore the potential applications of general GAI in generating multiple materials content, solving differential equation as well as querying materials FAQs. Furthermore, we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions. This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

This paper reports the synthesis and physical properties of graphene oxide/neodymium oxide (GO/Nd2O3) composites. The GO/Nd2O3 composite was prepared by a simple Sonochemical Method, and graphene oxide was synthesized by a modified hummers method. The synthesized composites were characterized by using X-Ray Diffraction (XRD), Fourier Transform Infra-Red (FT-IR), Scanning Electron Microscopy-Energy Dispersive X-ray (SEM-EDX), Raman Spectroscopy, and UV–Vis Spectroscopy. The XRD data confirmed that the GO/Nd2O3 composite had a hexagonal phase structure and showed that the more Nd2O3 nanoparticles were added to GO, the more the crystallinity index increased. The FT-IR spectra showed six types of bonds of functional groups of the GO/Nd2O3 composite, namely the hydroxyl (-OH), carbonyl (C]O), (C]C), (C–O–H), (C–O–C), and the absorption band at 678 cm−1 shows REOs (Nd2O3). Raman spectroscopic analysis showed that the intensity of the ratio of ID/IG was (∼0.85) with the size of sp2 graphitic area of ∼22.6 nm. The addition of Nd2O3 particles to GO did not affect the intensity ratio of ID/IG and was almost the same for the GO and GO/Nd2O3 composite. SEM results show that the composite has a porous three-dimensional carbon network structure. The optical properties were analyzed using UV–Visible spectroscopy. The results of Urbach Energy showed that the GO/Nd2O3 composite is a semiconductor material with an Urbach Energy value of 0.050–0.063 eV, showing the potential application of GO/Nd2O3 composites in electronic devices.

The emergence of autonomous experimental systems integrating machine learning and robots is ushering in a paradigm shift in materials science. Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention. A current direction focuses on discovering unexpected materials and theories with unconventional research approaches. This article reviews the latest achievements and discusses the impact of autonomous experimental systems, which will fundamentally change the way we understand research. Moreover, as autonomous experimental systems continue to develop, the need to think about the role of human researchers becomes more pressing. While machine learning and robotics can free us from the repetitive aspects of research, we need to understand the strengths and limitations of machine learning and robots and focus on how humans can perform higher creativity. In addition, we also discuss inventorship and authorship in the era of autonomous systems.

Abstract A surge of interest has been brought to all‐solid‐state batteries (ASSBs) as they show great prospects for enabling higher energy density and improved safety compared to conventional liquid batteries. Na Super Ionic CONductors (NaSICONs) proposed by Goodenough and Hong in 1976 are the most promising materials class for Na‐based ASSBs owing to their excellent ion conductivity (>1 mS cm−1), high thermal and chemical/electrochemical stability, as well as good chemical/electrochemical compatibility with electrode materials. The major challenge facing NaSICON‐type electrolytes is the generally high interfacial resistance and thus sluggish charge transfer kinetics across the NaSICON/cathode interface. Great endeavors in the past few years have led to progress in the improvement of the ion‐conducting property, and a dramatic decrease in the NaSICON/electrode interface resistance. Excellent cycling performance and rate capability have been achieved through interface engineering. In this review article, we summarize the state‐of‐the‐art findings for various derivatives of NaSICON structured solid electrolytes, with the aim of providing a deeper understanding of the underlying mechanism for the improvement of ion conductivity, and the intrinsic reasons for the enhanced interface charge transfer kinetics. These strategies can be readily extended to other solid electrolytes. We hope this review will inspire more work on NaSICON‐type solid electrolytes and solid‐state batteries.

We conducted a global survey on the effects of the COVID-19 pandemic on the research activities of materials scientists by distributing a questionnaire on 9 October 2020 with a response deadline of 23 October 2020. The questions covered issues such as access to labs, effectiveness of online conferences, and effects on doctoral students for the period covering the first lockdowns until the relaxation of restrictions in late September 2020 in many countries. The survey also included online interviews with eminent materials scientists who shared their local experiences during this period. The interviews were compiled as a series of audio conversations for The STAM Podcast that is freely available worldwide. Our findings included that the majority of institutes were not prepared for such a crisis; researchers in China, Japan, and Singapore were able to resume research much quicker – for example after approximately one month in Japan – than their counterparts in the US and Europe after the first lockdowns; researchers adapted to using virtual teleconferencing to maintain contact with colleagues; and doctoral students were the hardest hit by the pandemic with deep concerns about completing their research and career prospects. We hope that the analysis from this survey will enable the global materials science community to learn from each other’s experiences and move forward from the unprecedented circumstances created by the pandemic.

X-ray photoelectron spectroscopy (XPS) is a powerful tool to study surface properties (< 10 nm) and is being widely used in almost all branches of science and engineering. This review provides a pedagogical description of the fundamental understanding of XPS based surface characterization with the necessary background and key concepts, details on primary factors influencing surface analysis, issues in XPS analysis and identification of chemical bonding/oxidation state of elements. The basic theory of XPS and the most frequently used “core level peaks” analysis in the conventional use of XPS are presented, along with the details of valence band analysis. The challenges encountered during surface analysis, especially for phase composition identification, are briefly discussed. Further, a few selected recent applications of the XPS technique in various scientific fields are also highlighted. The recent development in the area of surface phase identification by valence band analysis using XPS, along with its advantages and challenges in the determination of nanoscale thin film phase composition are also detailed. This review also provides an overview of the significance of XPS as a tool for surface characterization in the field of material science, nanoscience, mining and mineral extraction, metallurgy, semiconductors, coatings, inorganic materials, tribology, organic materials, corrosion science and electrochemistry. The review should be an ideal material for researchers and also serves as an excellent reference for freshers who plan to begin research on this topic.

In this study, we present an approach to create a visual research topic map for materials science researchers from a large collection of archived research papers using natural language processing (NLP). We apply this approach on SAMURAI, a directory service for the researchers of the National Institute for Materials Science (NIMS), Japan. Visualization of research content will support exploratory searches by maximizing the information absorbance and intuitively capturing the research characteristics of each materials science researcher. In addition, a research topic map can connect researchers with similar topics aiming to find potential collaborators. Collaboration can support the advance of scientific research. We analyze all available publications of a researcher using frequent term analysis. In addition, materials science knowledge resources were utilized including dictionaries and automatic extraction for the material names. Noise reduction was implemented using stop word filtering. The topics were then visualized using a word cloud technique for each researcher. An analysis of the topic similarity was conducted to find researchers that share similar topics leading to the creation of a topic map for each researcher. The approach aims at maximizing information absorbance for public knowledge by applying NLP approaches to information mining from materials science research papers. NLP analysis and visualization code are available https://github.com/ThaerDieb/Topic_map.