Highlights
SPECIAL TOPIC—Order tuning in disordered alloys·COVER ARTICLE
COVER ARTICLE
2025, 74 (16): 166101.
doi: 10.7498/aps.74.20250563
Abstract +
The engineering applications of amorphous alloys are largely restricted by structural relaxation. Notably, the dissipative component of cyclic loading dominates the thermodynamic energy in practical applications of amorphous alloys. Mechanical rejuvenation, achieved through cyclic loading, offers an effective solution to this problem. In this study, we systematically investigate the deformation characteristics and rejuvenation mechanism of Pd20Pt20Cu20Ni20P20 amorphous alloy under mechanical cycling using dynamic mechanical analysis (DMA). By employing a two-phase Kelvin model and continuous relaxation time spectrum, we elucidate the interplay between mechanical deformation and energy dissipation during cyclic loading. The experimental results demonstrate that the strain rate increases significantly with the intensity of mechanical cycling, indicating enhanced dynamic activity in the glassy matrix. At higher cycling intensities, anelastic deformation is promoted, activating a broader spectrum of defects and amplifying dynamic heterogeneity. Through differential scanning calorimetry (DSC), we establish a quantitative correlation between deformation and energetic state, revealing that rejuvenation originates from internal heating induced by anelastic strain. A comparative analysis with creep deformation reveals that mechanical cycling exhibits a superior rejuvenation potential, attributed to its ability to periodically excite multi-scale defect clusters and sustain non-equilibrium states. The key findings of this work include: 1) Deformation mechanism: Cyclic loading enhances atomic mobility and facilitates deformation unit activation; 2) Energy landscape: The enthalpy change (ΔH) measured by DSC provides a direct metric for rejuvenation efficiency; 3) Dynamic heterogeneity: Mechanical cycling broadens the relaxation time spectrum, reflecting increased dynamic heterogeneity.
SPECIAL TOPIC—Order tuning in disordered alloys
EDITOR'S SUGGESTION
2025, 74 (16): 166104.
doi: 10.7498/aps.74.20250862
Abstract +
Amorphous materials avoid the inherent sensitivity to defects in traditional crystalline materials due to their cross-scale structural uniformity. Therefore, they have irreplaceable and important applications in many advanced technical fields. However, due to their thermodynamically non-equilibrium nature, amorphous materials experience structural relaxation towards equilibrium, leading to performance degradation or even failure during use. Additionally, the complex and disordered structure of amorphous materials results in low-energy excitation, such as boson peaks and tunneling two-level systems, which can cause internal friction and thermal noise in the materials. These factors significantly limit their performance in advanced technical applications. Therefore, effectively improving the stability of amorphous materials and suppressing low-energy excitation are key steps towards breaking through their performance limits. Recent studies have shown that atomic-level fabrication based on enhanced surface dynamics can successfully produce ultrastable amorphous materials, achieving unprecedented control over their microstructure, stability, and low-energy excitation, far exceeding the level achievable by traditional methods. The exceptional advantages of ultrastable amorphous materials endow them with significant application potential in advanced domains such as gravitational wave detection. This article delves into the underlying mechanisms of atomic-level fabrication for amorphous materials, highlighting their structural features and superior performances compared with traditional amorphous materials, and it also outlines future research directions and development trends of atomic-level fabrication in this field.
SPECIAL TOPIC—Order tuning in disordered alloys
EDITOR'S SUGGESTION
2025, 74 (16): 166102.
doi: 10.7498/aps.74.20250584
Abstract +
The atomic arrangement of metallic glasses lacks long-range periodicity, and exhibits structural characteristics of an amorphous state. Their unique structural features lead to research methods that differ from traditional metallic crystalline materials, focusing mainly on two scales: one is a macroscopic scale, on which glass-forming ability and mechanical behavior are investigated through alloy design, thermodynamic parameters, and other means; the other is an atomic scale, on which short- to medium-range orders of metallic glass are studied through computational simulations and diffraction techniques. There is a difference of over seven-orders of magnitude between the two scales, which makes it difficult to establish a direct quantitative relationship between them. Therefore, a structural feature is needed that can connect atomic configurations with macroscopic properties on a mesoscopic scale. With the development of amorphous structure characterization technique, it has been found that metallic glasses exhibit spatial heterogeneity at the nanometer and micrometer levels above a short-to-medium range, with their scales ranging between macroscopic and atomic scales. This article introduces experimental characterization methods for spatial heterogeneity, focuses on the electron microscopic characterization methods of spatial heterogeneity and local atomic orders, and discusses their intrinsic correlations with macroscopic properties such as β-relaxation behavior, mechanical behavior, thermodynamic stability, and glass-forming capability. Spatial heterogeneity, as a structural characteristic of metallic glasses on a mesoscopic scale, can serve as a link between short/medium-range orders and macroscopic properties of atoms.
SPECIAL TOPIC—Order tuning in disordered alloys
EDITOR'S SUGGESTION
2025, 74 (16): 166402.
doi: 10.7498/aps.74.20250307
Abstract +
SPECIAL TOPIC—Order tuning in disordered alloys
EDITOR'S SUGGESTION
2025, 74 (16): 166403.
doi: 10.7498/aps.74.20250513
Abstract +
Metallic glass-forming systems exhibit complex dynamic behaviors during the glass transition. Understanding the dynamic nature of metallic glasses and supercooled liquids is a crucial issue in the study of glassy physics. Topological order provides a novel perspective for re-examining the dynamics of glassy systems and elucidating the physical essence of the glassy state and glass transition. In this study, the microscopic dynamics of CuZr melts in the glass transition are investigated using molecular dynamics simulations. The single-particle dynamic characteristics in the supercooled CuZr melt are the random jump motions of atoms after a long-term caging period. To capture these dynamics, the displacement vector field is constructed based on the spatiotemporal distribution of these jump events. The simulation results reveal that there exist the numerous vortex structures in the displacement vector field. Notably, the vortex formation rate, which is defined as the number of vortices generated per unit time, exhibits a sharp drop near the glass transition temperature. The probability distribution of vortex formation rate displays a bimodal pattern on the drops, indicating the coexistence of two different dynamical states related to vortex formation. Multiple high-strain events are observed surrounding these vortices. It is found that the two vortex states during the transition exhibit markedly different characteristic ratios of vortices to high-strain events (1∶4 vs 1∶8), indicating a change in the coupling strength between vortex formation and high-strain activity. The high-strain events predominantly form in the regions between positive and negative vortices, and the specific quantitative relationship between vortices and high-strain events indirectly reflects the presence of strongly interacting vortex-antivortex pairs in the melt. During the vortex state transition, the vortex-to-high-strain-event ratio suddenly doubles, which means that this transition is not only a sudden change in the rate of vortex formation, but also an enhancement of the interactions between vortex-antivortex pairs, representing a change in global topological properties. These findings demonstrate that the vortex transition exhibits the characteristics of a topological phase transition, thereby predicting the existence of a topological phase transition in the displacement vector field of metallic glass-forming systems. Further speculation suggests that vortices and high-strain events are related to multiple secondary relaxation processes. This study provides a new perspective for understanding the dynamics of glass-forming systems and the glass transition.
SPECIAL TOPIC—Quantum information processing
EDITOR'S SUGGESTION
2025, 74 (16): 160301.
doi: 10.7498/aps.74.20250586
Abstract +
Quantum secret sharing (QSS), as a quantum extension of classical secret sharing, uses the basic principles of quantum mechanics to share information safely among multiple parties, providing a new paradigm for information security. As a key foundation for secure multiparty quantum communication and distributed quantum computing, QSS has attracted considerable attention since its emergence. Currently, research in this field includes both classical and quantum scenarios, and continuous progress has been made in both theoretical and experimental aspects. This paper first reviews the current development of QSS for classical information. In this regard, significant and parallel progress has been made in both discrete-variable QSS and continuous-variable QSS. The QSS protocols for sharing classical information, from entangled states to single photons and then to coherent light, have been continuously optimized to better utilize available resources and achieve more efficient implementation under current technological conditions. Meanwhile, round-robin, measurement-device-independent, and other protocols have been steadily improving the security of QSS. Next, one will focus on QSS scheme for quantum secrets, which begins with the symmetry of access structures and introduces basic (k, n) threshold protocols, dynamic schemes that support adaptive agent groups, and symmetric quantum information splitting through entanglement. It further introduces hierarchical quantum secret sharing schemes for asymmetric splitting of quantum information. Considering practical laboratory conditions of quantum states as resources, an overall discussion is conducted on quantum secret sharing with graph states. Afterwards, the design of a continuous-variable scheme for quantum secret sharing is outlined, and entanglement state sharing and quantum teleportation between multiple senders and receivers are introduced. Finally, this review discusses and outlines the future development directions of QSS, thereby inspiring readers to further study and explore the relevant subjects.
SPECIAL TOPIC—Quantum information processing
EDITOR'S SUGGESTION
2025, 74 (16): 160302.
doi: 10.7498/aps.74.20250589
Abstract +
Nonlocal quantum entanglement is a fundamental resource for future quantum networks. However, the efficiency of generating nonlocal entanglement between distant nodes is severely limited by the exponential loss incurred when locally generated entangled states are distributed through lossy quantum channels. This limitation becomes more pronounced in practical scenarios requiring the simultaneous distribution of multiple entangled pairs. Although classical multiplexing approaches, such as spatial, temporal, and frequency multiplexing, can increase the nonlocal entanglement generation rate, they do not improve the single-shot transmission efficiency. In contrast, quantum multiplexing, which can be generated by high-dimensional encoding of single photons, allows for the parallel generation of multiple nonlocal entangled pairs in a single transmission round, thereby enhancing the overall efficiency of nonlocal entanglement generation. Quantum multiplexing thus presents a promising route toward scalable quantum networks. This review introduces the mechanisms of generating nonlocal entanglement through quantum multiplexing, and focuses on two main methods: using high-dimensional single-photon encoding and high-dimensional biphoton entanglement distribution. Then it examines how quantum multiplexing can accelerate the generation of nonlocal quantum logical entanglement. Finally, it briefly explores the potential of quantum multiplexing for building large-scale quantum networks.
SPECIAL TOPIC—Quantum information processing
EDITOR'S SUGGESTION
2025, 74 (16): 160304.
doi: 10.7498/aps.74.20250791
Abstract +
Abstract +
The emergence of large language models has significantly advanced scientific research. Representative models such as ChatGPT and DeepSeek R1 have brought notable changes to the paradigm of scientific research. While these models are general-purpose, they have demonstrated strong generalization capabilities in the field of batteries, especially in solid-state battery research. In this study, we systematically screen 5309268 articles from key journals up to 2024, and accurately extract 124021 papers related to batteries. Additionally, we comprehensively search through 17559750 patent applications and granted patents from the European Patent Office and the United States Patent and Trademark Office up to 2024, identifying 125716 battery-related patents. Utilizing these extensive literature and patents, we conduct numerous experiments to evaluate the structured output capabilities of knowledge base, contextual learning, instruction adherence, and language models. Through multi-dimensional model evaluations and analyses, the following points are found. First, the model exhibits high accuracy in screening literature on inorganic solid-state electrolytes, equivalent to the level of a doctoral student in the relevant field. Based on 10604 data entries, the model demonstrates good recognition capabilities in identifying literature on in-situ polymerization/solidification technology. However, its understanding accuracy for this emerging technology is slightly lower than that for solid-state electrolytes, requiring further fine-tuning to improve accuracy. Second, through testing with 10604 data entries, the model achieves reliable accuracy in extracting inorganic ionic conductivity data. Third, based on solid-state lithium battery patents from four companies in South Korea and Japan over the past 20 years, this model proves effective in analyzing historical patent trends and conducting comparative analyses. Furthermore, the model-generated personalized literature reports based on the latest publications also show high accuracy. Fourth, by utilizing the iterative strategy of the model, we enable DeepSeek to engage in self-reflection thinking, thereby providing more comprehensive responses. The research results indicate that language models possess strong capabilities in content summarization and trend analysis. However, we also observe that the model may occasionally experience issues with numerical hallucinations. Additionally, while processing a large number of battery-related data, there is still room for optimization in engineering applications. According to the characteristics of the model and the above test results, we utilize the DeepSeek V3-0324 model to extract data on inorganic solid electrolyte materials, including 5970 ionic conductivity entries, 387 diffusion coefficient entries, and 3094 migration barrier entries. Additionally, it includes over 1000 data entries related to chemical, electrochemical, and mechanical properties, covering nearly all physical, chemical, and electrochemical properties related to inorganic solid electrolytes. This also means that the application of large language models in scientific research has shifted from auxiliary research to actively promoting its development. The datasets presented in this paper may be available at the website: https://cmpdc.iphy.ac.cn/literature/SSE.html (DOI: https://doi.org/10.57760/sciencedb.j00213.00172 ).
Abstract +
Surface nanobubbles, as nanoscale gaseous domains spontaneously formed at solid-liquid interfaces, exhibit significant potential applications in the biomedical field due to their unique nanoscale size effects, rapid dynamic response characteristics, and favorable biocompatibility. In ultrasonic imaging, surface nanobubbles enhance tissue acoustic contrast by generating strong harmonic scattering signals through nonlinear oscillation under stable cavitation. In antibacterial disinfection applications, the rupture of surface nanobubbles generates a transient high pressure, which synergizes with oxidative damage mediated by reactive oxygen species /hydroxyl radicals to achieve efficient bacterial inactivation. However, in physiological environments, blood flow shear stress and pH fluctuations may induce premature rupture of surface nanobubbles, leading to imaging signal attenuation or risks of non-specific tissue damage, rendering their stability a critical factor determining functional efficacy and biosafety. Notably, the experimental observation of surface nanobubble lifetimes (ranging from hours to days) significantly contradicts the dissolution behavior within microseconds predicted by classical thermodynamic theory, which urgently demands the construction of theoretical models of stability. Although existing theoretical modelshave elucidated the stability mechanisms of surface nanobubbles from multiple perspectives, they arelimited by the lack of intrinsic correlation and inherent limitations, thereby restricting targeted optimization of stability: the contamination barrier model emphasizes that surfactant adsorption inhibits gas diffusion; the dynamic equilibrium model explains that stability arises from the dynamic balance of gas exchange at the gas-liquid interface; the contact line pinning model reveals that substrate heterogeneity constrains the evolution of the three-phase contact line; the local supersaturation model proposes that local high-concentration gas layers formed by substrate adsorption delay dissolution; the interfacial charge enrichment model suggests that electrostatic pressure from the double layer counteracts the Laplace pressure driving dissolution; the internal high-density model assumes that the condensed high-density gas inside reduces diffusion rate and partially counteracts the Laplace pressure. This review systematically summarizes the research progress of the stability mechanisms of surface nanobubbles. It first reviews the discovery history of surface nanobubbles, then deeply analyzes the core mechanisms, intrinsic correlations, and limitations of the aforementioned theoretical models., Finally, it examines the technical challenges faced by surface nanobubbles with the application examples in the biomedical field, and proposes potential optimization strategies and future perspectives based on ther theoretical models of stability.
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