ABSTRACT Heterostructured materials are an emerging class of materials with superior performances that are unattainable by their conventional homogeneous counterparts. They consist of heterogeneous zones with dramatic (>100%) variations in mechanical and/or physical properties. The interaction in these hetero-zones produces a synergistic effect where the integrated property exceeds the prediction by the rule-of-mixtures. The heterostructured materials field explores heterostructures to control defect distributions, long-range internal stresses, and nonlinear inter-zone interactions for unprecedented performances. This paper is aimed to provide perspectives on this novel field, describe the state-of-the-art of heterostructured materials, and identify and discuss key issues that deserve additional studies. IMPACT STATEMENT This paper delineates heterostructured materials, which are emerging as a new class of materials with unprecedented properties, new materials science and economic industrial production. GRAPHICAL ABSTRACT
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atoms into a suitable representation. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine-learning methods for chemistry and materials science. This review summarizes the current understanding of the nature and characteristics of the most commonly used structural and chemical descriptions of atomistic structures, highlighting the deep underlying connections between different frameworks and the ideas that lead to computationally efficient and universally applicable models. It emphasizes the link between properties, structures, their physical chemistry, and their mathematical description, provides examples of recent applications to a diverse set of chemical and materials science problems, and outlines the open questions and the most promising research directions in the field.
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.
New tools enable new ways of working, and materials science is no exception. In materials discovery, traditional manual, serial, and human-intensive work is being augmented by automated, parallel, and iterative processes driven by Artificial Intelligence (AI), simulation and experimental automation. In this perspective, we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle. We show, using the example of the development of a novel chemically amplified photoresist, how these technologies’ impacts are amplified when they are used in concert with each other as powerful, heterogeneous workflows.
ObjectivesRetinal pigment epithelium (RPE) cell transplantation holds therapeutic promise for retinal degenerative diseases, but longitudinal monitoring of graft survival and efficacy remains clinically challenging. The aim of this study is to develop a simple and effective method for the therapeutic quantification of RPE cell transplantation and immune rejection in vivo.MethodsA nanoprobe was developed and modified to label donor RPE cells, and used to monitor the position and intensity of the fluorescence signal in vivo. Immunofluorescence staining and single-cell RNA sequencing (scRNA-seq) were used to characterize the cell types showing the fluorescence signal of the nanoprobe and to determine the composition of the immune microenvironment associated with subretinal transplantation.ResultsThe spatial distribution of the fluorescence signal of the nanoprobe corresponded with the site of transplantation, but the signal intensity decreased over time, while the signal distribution extended to the choroid. Additionally, the nanoprobe fluorescence signal was detected in the liver and spleen during long-term monitoring. Conversely, in mice administered the immunosuppressive drug cyclosporine A, the decrease in signal intensity was slower and the expansion of the signal distribution was less pronounced. Immunofluorescence analysis revealed a significant temporal increase in the proportion of macrophages with nanoprobe-labeled cells following transplantation. The stability and cell-penetrating ability of the nanoprobe enables the labeling of immune cell niches in RPE transplantation. Additionally, scRNA-seq analysis of nanoprobe-labeled cells identified MDK and ANXA1 signaling pathway in donor RPE cells as initiators of the immune rejection cascade, which were further amplified by macrophage-mediated pro-inflammatory signaling.ConclusionNear-infrared fluorescent nanoprobes represent a reliable method for in vivo tracing of donor RPE cells and long-term observation of nanoprobe distribution can be used to evaluate the degree of immune rejection. Molecular analysis of nanoprobe-labeled cells facilitates the characterization of the dynamic immune cell rejection niche and the landscape of donor-host interactions in RPE transplantation.
Mutairu O. Ajiboye, Ayodele A. Daniyan, Paul C. Okonkwo
et al.
Abstract This study presents the first investigation of halogen-substituted aniline-derived Schiff bases (SB1, SB2, SB3) as corrosion inhibitors for mild steel in Nigerian tar sand environments. Key novelty includes introducing inhibition power as a new gravimetric-based performance metric for alkaline conditions where electrochemical methods are limited. Tar sand from Ilubirin was processed with 0.58 M NaOH at 90 °C for 24 h with inhibitors at concentrations of 25–150 ppm. Gravimetric analysis, SEM–EDS, and Langmuir isotherm modelling revealed a significant corrosion rate with effectiveness order SB3 > SB2 > SB1. SB3 achieved 94.4% inhibition efficiency at 150 ppm due to a favourable molecular structure promoting enhanced adsorption. Langmuir analysis confirmed chemisorption (ΔG°ads > − 20 kJ mol−1), while microstructural evaluation demonstrated excellent surface protection. This research demonstrates the effectiveness of inhibition power in assessing corrosion inhibitors using gravimetric data due to the limitations of electrochemical measurement in tar sand environments. The study concludes that Schiff-based compounds offer promising solutions for corrosion control in a harsh alkaline tar sand processing environment.
Chemical technology, Physical and theoretical chemistry
Nikoleta V. Nikolaidou, Anastasios Asvestas, Agathi Anthoula Kaminari
et al.
Religious panel paintings (icons) play a pivotal role in the rituals of the Eastern Orthodox Christian Church. However, their continuous use often results in physical degradation, prompting remedial interventions. Quite commonly, alterations were treated by simply applying new paint layers directly over the decayed original, while in some cases, old icons were overpainted merely as a means to renovate and modernize them. Therefore, numerous overpainted icons are currently housed in churches, museums, and private collections across Greece. This study focuses on the investigation of a post-Byzantine icon of Christ Pantokrator, which displays extensive overpainting while retaining a few visible fragments of the original composition. The objective was to assess the extent and condition of preservation of the original artwork, to identify materials and techniques used both in the initial painting and in subsequent restoration phases, and to distinguish between those phases. To achieve these aims, a fully non-invasive diagnostic methodology was implemented, including visible light photography, ultraviolet radiation imaging (UVR/UVL), hyperspectral imaging (MuSIS HS), infrared reflectography (IRRef), X-ray radiography, and macroscopic X-ray fluorescence scanning (MA-XRF). The findings confirm that the original painting remains substantially preserved and is of high artistic quality. Moreover, analysis revealed at least two distinct phases of overpainting, likely dating from the 20th century, while the results suggest that the original artwork probably dates to the first half of the 18th century. The study highlights the need to use complementary techniques in order to non-invasively assess complex artifacts like overpainted icons and offers valuable insights into historical restoration practices providing foundation for future conservation planning.
Abstract To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly relevant for metallic materials processed through additive manufacturing, which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials. Furthermore, capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties. To address these challenges, we propose the physical spatial mapping of metal diffraction latent space features. This approach integrates (i) point diffraction data encoding via variational autoencoders or contrastive learning and (ii) the physical mapping of the encoded values. Together, these steps offer a method to comprehensively describe metal microstructures. We demonstrate this approach on a wrought and additively manufactured alloy, showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models. This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties.
Materials of engineering and construction. Mechanics of materials, Computer software
This paper systematically reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have significantly accelerated material prediction and design, their complexity has also caused difficulties in understanding and application for researchers in traditional fields. In response to the above issues, this paper first analyzes the inherent challenges in the research and development of polymer materials, including structural complexity and the limitations of traditional trial-and-error methods. To address these problems, it focuses on introducing key basic technologies such as molecular descriptors and feature representation, data standardization and cleaning, and records a number of high-quality polymer databases. Subsequently, it elaborates on the key role of machine learning in polymer property prediction and material design, covering the specific applications of algorithms such as traditional machine learning, deep learning, and transfer learning; further, it deeply expounds on data-driven design strategies, such as reverse design, high-throughput virtual screening, and multi-objective optimization. The paper also systematically introduces the complete process of constructing high-reliability machine learning models and summarizes effective experimental verification, model evaluation, and optimization methods. Finally, it summarizes the current technical challenges in research, such as data quality and model generalization ability, and looks forward to future development trends including multi-scale modeling, physics-informed machine learning, standardized data sharing, and interpretable machine learning.