Science and Technology of Advanced Materials最新文献

This study delves into spin current-induced phenomena, such as spin-Hall magnetoresistance and the spin Seebeck effect within Pt films deposited on a noncollinear magnet, CoCr ${ }_2$ O ${ }_4$ (CCO), particularly at low temperatures. Detailed investigation of the angular dependencies of spin Hall magnetoresistance (SMR) and spin Seebeck effect (SSE) was carried out. The temperature-dependent behavior of both SMR and SSE signals exhibits a discernible variation correlated with different magnetic phases of CCO. To distinguish the contributions arising from magnetic proximity effects, we conducted X-ray magnetic dichroism (XMCD) at the Pt-M ${ }_3$ edge. XMCD data from Pt/CCO heterostructures suggest that any magnetic moment associated with Pt, if present, is below the detection limit. This supports the notion that the observed signals primarily stem from SMR and SSE. This study offers insights into spin-current-driven phenomena, paving the way for potential spintronic applications.

Catalysts' redox reactions are crucial for storage and energy conversion. Therefore, the fabrication of cost-effective, structurally rational, and multifunctional advanced catalytic materials continues to be a crucial task. In this study, we obtained P, Fe, and Co co-doped, nitrogen-rich carbon nanofibers by directly forming carbon nanotubes from metal-organic frameworks through electrospinning and pyrolysis. The P_0.025-FeCo/C catalyst demonstrated outstanding ORR activity, including an ECSA of 1954.3 cm², a limited current density of -3.98 mA/cm², an E_1/2 of ~0.84 V, and an E_onset of ~0.94 V. After 5000 cycles, the P_0.025-FeCo/C catalyst demonstrated remarkable enduring stability. These function enhancements occurred because of the electronic coupling between the metal and phosphorus, which altered the electron distribution at the metal center and optimized its electronic structure, thereby improving catalytic activity and stability. It exhibits good chemical stability in alkaline media and can maintain its catalytic performance for a long time, demonstrating good durability. Its tubular structure provides many active sites and superior electron transport paths owing to its unique channels and cavities, which help improve its activity and stability. Therefore, P_0.025-FeCo/C is expected to become a non-precious metal catalyst for facilitating oxygen reduction reactions.

The development of spintronic emitters of broadband terahertz (THz) pulses relies on designing heterostructures in which the processes of laser-driven spin current generation and subsequent spin-to-charge current conversion are the most efficient. The interface between the ferromagnetic and nonmagnetic layers in an emitter is a critical element. In this study, we experimentally examined single-cycle THz pulse generation from a laser-pulse-excited Pt/Co emitter with a 1.2-nm-thick composition-gradient interface between the Pt and Co and compared it with the emission from a conventional Pt/Co structure with an abrupt interface. We found that the gradient interface improved the efficiency of the optics-to-THz conversion by a factor of two in a wide range of optical fluences up to 3 mJ⋅cm^-2. This enhancement was caused by a pronounced increase in the transmittance of the laser-driven spin-polarized current through the gradient interface compared with the abrupt interface. Moreover, it was evident that such transmission deteriorated with the laser fluence owing to the spin accumulation effect.

Organic semiconductor (OSC) single crystals feature flexibility, solution processability, and high-mobility coherent carrier transport, which are advantageous for printed flexible electronic applications. A mechanical strain sensor is a target device whose high sensitivity and wide measurement range have been demonstrated when OSC single crystals were employed as the active channel. However, there have been limited reports on scalable fabrication of devices and reliable measurements, which limits the use of strain sensors in a wide range of applications. In this study, we present a comprehensive approach to address these issues through advanced device processing, design, and measurements. Our resistive strain sensors showed a small drift owing to the stable and effective p-type chemical doping of the OSC single crystals. A Wheatstone bridge circuit and compact lock-in amplifier were designed to accurately measure resistance changes at low noise levels. The experimental results demonstrated a substantial reduction in noise and achieved high-precision measurements with precision of ± 1.8 ppm. These results demonstrate the scalable fabrication of organic semiconductor strain sensors with high precision and reliability, which opens up the possibility of employing them in various industrial sectors.

Ensuring the safety of researchers by protecting them from exposure to toxic gases in laboratories is of paramount importance. This study investigated the effectiveness of using high-surface-area MgO to remove HCl under atmospheric conditions. Two types of MgO were synthesized through the thermal decomposition 1-1-1, Tennodai, Tsukuba, of Mg(OH)₂ and MgC₂O₄·2 H₂O. HCl diluted with air passed through both MgO samples, and the amounts of HCl removed and morphological changes in the samples were compared. No significant differences in surface area or crystallinity were observed with the decomposition temperatures. X-ray diffraction analysis showed that the sample prepared from MgC₂O₄·2 H₂O reacted with HCl immediately upon introducing HCl gas. In contrast, the sample obtained from Mg(OH)₂ exhibited only MgO peaks, even 30 min after the introduction of HCl gas. Microscopic analysis revealed that the samples derived from Mg(OH)₂ showed no significant changes in shape after the reaction, whereas the MgO prepared from MgC₂O₄·2 H₂O exhibited substantial changes in overall shape. No correlation was observed between the surface area and the amount of HCl removed. When MgO is prepared from MgC₂O₄·2 H₂O, the reaction occurs in the bulk material, whereas when MgO is prepared from Mg(OH)₂, the reaction hardly progresses after HCl adsorbs onto the MgO surface. The order of magnitude of HCl removal was consistent with the base catalytic activity of the decomposition of diacetone alcohol to acetone. These results suggest that, compared with MgO obtained from Mg(OH)₂, MgO derived from MgC₂O₄·2 H₂O generates more active sites, resulting in the reaction with HCl from surface to progress into bulk.

Using a lithium (Li) metal anode is essential for high-energy batteries, however, dendritic Li growth is unavoidable during Li plating and stripping processes. Strategically, a porous carbon structure derived from a metal-organic framework is suggested for directly storing metallic Li, although problems still exist with plating Li from the core to the surface and with stripping Li from the surface. Herein, we strategically utilize the carbon structure of zeolitic imidazolate framework-8 as an anode and replace the inactive residual Zn with Ag through galvanic displacement. The strong affinity of Ag for Li ions facilitates the transfer of plating from the surface of the carbon structure to its interior. After determining the optimal conditions for galvanic displacement by varying reaction times and temperatures, we carefully evaluate the electrochemical performance.

Traditionally employed in alloy corrosion studies, dealloying has evolved into a versatile technique for fabricating advanced porous materials. The unique architecture of interconnected pore channels and continuous metal ligaments endows dealloyed materials with high surface-to-volume ratio, excellent electron conductivity, efficient mass transport and remarkable catalytic activity, positioning them at the forefront of nanomaterial applications with significant potential. However, reproducible synthesis of these structures remains challenging due to limitations in conventional dealloying techniques. Herein, this review attempts to consolidate recent progress in electrochemical and chemical dealloying methods for nanoporous anodes in energy storage and conversion applications. We begin by elucidating the fundamental mechanisms driving dealloying and evaluate key factors influencing dealloying conditions. Through a review of current research, we identify critical properties of dealloyed nanoporous anodes that warrant further investigation. Applications of these materials as anodes in metal-ion batteries, supercapacitors, water splitting and photocatalyst are discussed. Lastly, we address ongoing challenges in this field and propose perspectives on promising research directions. This review aims to inspire new pathways and foster the development of efficient dealloyed porous anodes for sustainable energy technologies.

In this work, we have attempted to predict the mechanical behaviour of light weight Mg-based rare earth alloys fabricated through different mechanical and thermal processes. Our approach involves machine learning techniques across a range of different thermomechanical processes such as solution treatment, homogenization, extrusion and aging behaviour. The effectiveness of machine learning models is evaluated using performance metrics, including Coefficient of determination (R²), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). After modeling and selection of best model, the mechanical behaviour of new alloys was predicted in terms of ultimate tensile strength, yield strength and total elongation. The predicted results highlight the superior predictive accuracy of the K-Nearest Neighbors (KNN) machine learning model, demonstrating its better performance metrics compared with other machine learning approaches. This model has been found to predict the material properties with an effective evaluation matrix (R² = 0.955, MAE = 3.4% and RMSE = 4.5%).

Solid catalyst development has traditionally relied on trial-and-error approaches, limiting the broader application of valuable insights across different catalyst families. To overcome this fragmentation, we introduce a framework that integrates high-throughput experimentation (HTE) and automatic feature engineering (AFE) with active learning to acquire comprehensive catalyst knowledge. The framework is demonstrated for oxidative coupling of methane (OCM), where active learning is continued until the machine learning model achieves robustness for each of the BaO-, CaO-, La₂O₃-, TiO₂-, and ZrO₂-supported catalysts, with 333 catalysts newly tested. The resulting models are utilized to extract catalyst design rules, revealing key synergistic combinations in high-performing catalysts. Moreover, we propose a method for transferring knowledge between supports, showing that features refined on one support can improve predictions on others. This framework advances the understanding of catalyst design and promotes reliable machine learning.