Science and Technology of Advanced Materials最新文献

A body-centered cubic (bcc) FeCo(B) is a current standard magnetic material for perpendicular magnetic tunnel junctions (p-MTJs) showing both large tunnel magnetoresistance (TMR) and high interfacial perpendicular magnetic anisotropy (PMA) when MgO is utilized as a barrier material of p-MTJs. Since the p-MTJ is a key device of current spintronics memory, i.e. spin-transfer-torque magnetoresistive random access memory (STT-MRAM), it attracts attention for further advance to explore new magnetic materials showing both large PMA and TMR. However, there have been no such materials other than FeCo(B)/MgO. Here, we report, for the first time, PMA in metastable bcc Co-based alloy, i.e. bcc CoMnFe thin films which are known to exhibit large TMR effect when used for electrodes of MTJs with the MgO barrier. The largest intrinsic PMAs were about 0.6 and 0.8 MJ/m³ in a few nanometer-thick CoMnFe alloy film and multilayer film, respectively. Our ab-initio calculation suggested that PMA originates from tetragonal strain and the value exceeds 1 MJ/m³ with optimizing strain and alloys composition. The simulation of the thermal stability factor indicates that the magnetic properties obtained satisfy the requirement of the data retention performance of X-1X nm STT-MRAM. The large PMA and high TMR effect in bcc CoMnFe/MgO, which were rarely observed in materials other than FeCo(B)/MgO, indicate that bcc CoMnFe/MgO is one of the potential candidates of the materials for X-1X nm STT-MRAM.

Formaldehyde (FA) is a reactive toxic volatile organic compound (VOC), produced both exogenously from the environment and endogenously within most organisms, and poses significant health risks to humans at elevated concentrations. Consequently, the development of reliable and sensitive FA sensing technologies is crucial for environmental monitoring, industrial safety, and public health protection. This review will provide a concise overview of FA sensing methodologies, highlighting key principles, sensing mechanisms, and recent advancements. The main aim of this review article is to comprehensively discuss recent advancements in FA sensors utilizing small molecules, nanoparticles, organic materials, and polymers, along with their successful applications across various fields, with particular emphasis on in situ FA sensing using polymeric probes due to their advantages over small molecular probes. Additionally, it will discuss prospects for future design and research in this area. We anticipate that this article will aid in the development of next-generation polymeric FA sensing probed with improved physicochemical properties.

We introduce our proprietary Materials Informatics (MI) technologies and our chemistry-oriented methodology for exploring new inorganic functional materials. Using machine learning on crystal structure databases, we developed 'Element Reactivity Maps' that displays the presence or the predicted formation probability of compounds for combinations of 80 × 80 × 80 elements. By analysing atomic coordinates with Delaunay tetrahedral decomposition, we established the concept of Delaunay Chemistry. This enabled us to design crystal structures by combining Delaunay tetrahedra of known compounds and to develop the 'Crystal Cluster Simulator' web system. We also developed the Starrydata2 web system to collect large-scale experimental data on material properties from plot images in academic papers. This dataset supported us to select candidate materials for new thermoelectric materials through various data analyses. In large-scale synthesis experiments involving over 7,000 samples, we discovered numerous new phases, including solid solutions of known structures in new combinations of elements. Using sodium metal in synthesis and our proprietary ion diffusion control technologies, we discovered new cage-like compounds by extracting monovalent cations from materials with nano-framework structures, as well as new intercalation compounds. The Element Reactivity Maps were also used to select barrier metals for device electrodes, and an autonomous contact resistance measurement system is under development.

New phosphors are consistently in demand for advances in solid-state lighting and displays. Conventional trial-and-error exploration experiments for new phosphors require considerable time. If a phosphor host suitable for the target luminescent property can be proposed using computational science, the speed of development of new phosphors will significantly increase, and unexpected/overlooked compositions could be proposed as candidates. As a more practical approach for developing new phosphors with target luminescent properties, we looked at combining experiments with machine learning on the topics of emission wavelength, full width at half maximum (FWHM) of the emission peak, temperature dependence of the emission spectrum (thermal quenching), new phosphors with new chemical composition or crystal structure, and high-throughput experiments.

Glioma is the most common primary malignant tumor of the central nervous system in adults. Although immunotherapy, especially tumor vaccines, has made some progress in the treatment of gliomas compared with surgery and radiotherapy. However, the lack of specific or relevant tumor antigens severely limits the further development of tumor vaccines. Here, we report a bio-derived vaccine (TMV@CpG) derived from glioma cell membrane vesicles and carrying TLR9 agonist CpG as adjuvant, which was loaded onto the GelMA microneedle to obtain the microneedle vaccine (MN-TMV@CpG). Microneedle vaccine fully utilize the innate immune cells rich in the skin, inducing stronger cellular immune responses. In subcutaneous tumor models, MN-TMV@CpG reversed the immune-suppressing microenvironment of tumor, and effectively inhibited tumor progression. In an intracranial tumor model, MN-TMV@CpG significantly prolonged the survival duration and induced stronger immune memory responses in tumor bearing mice when combined with anti-PD1 mAb. These results suggest that bio-derived nanovaccines can be used as a potential antitumor immunotherapy strategy.

Computational approaches using theoretical calculations and data scientific methods have become increasingly important in materials science and technology, with the development of relevant methodologies and algorithms, the availability of large materials data, and the enhancement of computer performance. As reviewed herein, we have developed computational methods for the design and prediction of inorganic materials with a particular focus on the exploration of semiconductors and dielectrics. High-throughput first-principles calculations are used to systematically and accurately predict the local atomic and electronic structures of polarons, point defects, surfaces, and interfaces, as well as bulk fundamental properties. Machine learning techniques are utilized to efficiently predict various material properties, construct phase diagrams, and search for materials satisfying target properties. These computational approaches have elucidated the mechanisms behind material functionalities and explored promising materials in combination with synthesis, characterization, and device fabrication. Examples include the development of ternary nitride semiconductors for potential optoelectronic and photovoltaic applications, the exploration of phosphide semiconductors and the optimization of heterointerfaces toward the improvement of phosphide-based photovoltaic cells, and the discovery of ferroelectricity in layered perovskite oxides and the theoretical understanding of its origin, all of which demonstrate the effectiveness of our computer-aided materials research.

Developing electrochemical energy storage and conversion systems, such as capacitors, batteries, and fuel cells is crucial to address rapidly growing global energy demands and environmental concerns for a sustainable society. Significant efforts have been devoted to the structural design and engineering of various electrode materials to improve economic applicability and electrochemical performance. The yolk-shell structures represent a special kind of core-shell morphologies, which show great application potential in energy storage, controlled delivery, adsorption, nanoreactors, sensing, and catalysis. Their controllable void spaces may facilitate the exposure of more active sites for redox reactions and enhance selective adsorption. Based on different nanoarchitectonic designs and fabrication techniques, the yolk-shell structures with controllable structural nanofeatures and the homo- or hetero-compositions provide multiple synergistic effects to promote reactions on the electrode/electrolyte interfaces. This review is focused on the key structural features of yolk-shell architectures, highlighting the recent advancements in their fabrication with adjustable space and mono- or multi-metallic composites. The effects of tailorable structure and functionality of yolk-shell nanostructures on various electrochemical processes are also summarized.

In live organisms, cells are embedded in tissue-specific extracellular matrix (ECM), which provides chemical and mechanical signals important for cell differentiation, migration, and overall functionality. Careful reproduction of ECM properties in artificial cell scaffolds is necessary to get physiologically relevant results of in vitro studies and produce robust materials for cell and tissue engineering. Nanoarchitectonics is a contemporary way to building complex materials from nano-scale objects of artificial and biological origin. Decellularized ECM (dECM), remaining after cell elimination from organs, tissues and cell cultures is arguably the closest equivalent of native ECM achievable today. dECM-based materials can be used as templates or components for producing cell scaffolds using nanoarchitectonic approach. Irrespective of the form, in which dECM is used (whole acellular organ/tissue, bioink or hydrogel), the local stiffness of the dECM scaffold must be evaluated, since the fate of seeded cells depends on the mechanical properties of their environment. Careful dECM characterization is also necessary to reproduce essential ECM traits in artificial cell scaffolds by nanoparticle assembly. Atomic force microscopy (AFM) is a valuable characterization tool, as it allows simultaneous assessment of mechanical and topographic features of the scaffold, and additionally evaluate the efficiency of decellularization process and preservation of the extracellular matrix. This review depicts the current application of AFM in the field of dECM-based materials, including the basics of AFM technique and the use of flicker-noise spectroscopy (FNS) method for the quantification of the dECM micro- and nanostructure.

3D printing has emerged as a highly efficient process for fabricating electrodes in hydrogen evolution through water splitting, whereas metals are the most popular choice of materials in hydrogen evolution reactions (HER) due to their catalytic activity. However, current 3D printing solutions face challenges, including high cost, low surface area, and sub-optimal performance. In this work, we introduce metal-deposited 3D printed pyrolytic carbon (PyC) as a facile and cost-effective HER electrode. We adopt an integrated approach of resin 3D printing, pyrolysis, and electrochemical metal deposition. 3D printing of a resin and its subsequent pyrolysis led to 3D complex architectures of the conductive substrate, facilitating the electrochemical metal deposition and leading to layered 3D metal architecture. Both monolayers of metals (such as copper and nickel) and bi-metallic 3D PyC structures are demonstrated. Each metal layer thickness ranges from 6 to10 µm. The metal coatings, particularly the bi-metallic configurations, result in achieving significantly higher mechanical properties under compressive loading and improved electrical properties due to the synergistic contributions from each metal counterpart. The metalized PyC structures are further demonstrated for HER catalysts, contributing to the development of highly efficient and durable catalyst systems for hydrogen production. Among the materials studied here, Ni@Cu bimetallic 3D PyC electrodes are particularly well-suited, demonstrating a low HER overpotential value of 264 mV (100 mA/cm², KOH (1 M)) with corresponding Tafel slopes of 107 mV/dec, with exceptional stability during a 10 h operation at a high applied current of -50 mA/cm².

This review presents computational and machine learning methodologies developed during a 5-year research project on proton-conducting oxides. The main goal was to develop methodologies that could assist in materials discovery or provide new insights into complex proton-conducting oxides. Through these methodologies, three new proton-conducting oxides, including both perovskite and non-perovskites, have been discovered. In terms of gaining insights, octahedral tilt/distortions and oxygen affinity are found to play a critical role in determining proton diffusivities and conductivities in doped barium zirconates. Replica exchange Monte Carlo approach has enabled to reveal realistic defect configurations, hydration behavior, and their temperature dependence in oxides. Our approach 'Materials discovery through interpretation', which integrates new insights or tendencies obtained from computations and experiments to sequential explorations of materials, has also identified perovskites that exhibit proton conductivity exceeding 0.01 S/cm and high chemical stability at 300 $\circ$ C.