Precursor phase with full phonon softening above the charge-density-wave phase transition in 2H2H-TaSe2_2

Research on charge-density-wave (CDW) ordered transition-metal dichalcogenides continues to unravel new states of quantum matter correlated to the intertwined lattice and electronic degrees of freedom. Here, we report an inelastic x-ray scattering investigation of the lattice dynamics of the canonical CDW compound $2H$-TaSe$_2$ complemented by angle-resolved photoemission spectroscopy. Our results rule out the central-peak scenario for the CDW transition in $2H$-TaSe$_2$ and provide evidence for a novel precursor phase above the CDW transition temperature $T_{CDW}$. The phase at temperatures between $T^{*}\,(= 128.7\,,\rm{K})$ and $T_{CDW}\,(= 121.3\,\rm{K})$ is characterized by a fully softened phonon mode and medium-range ordered ($\xi_{corr} = 100\,\rm{\mathring{A}}- 200\,\rm{\mathring{A}})$ static CDW domains. Only $T_{CDW}$ is detectable in our photoemission experiments. Thus, $2H$-TaSe$_2$ exhibits structural before electronic static order and emphasizes the important lattice contribution to CDW transitions

Soft Phonon Mode Triggering Fast Ag Diffusion in Superionic Argyrodite Ag8_8GeSe6_6

The structural coexistence of dual rigid and mobile sublattices in superionic Argyrodites yields ultralow lattice thermal conductivity along with decent electrical and ionic conductivities and therefore attracts intense interest for batteries, fuel cells, and thermoelectric applications. However, a comprehensive understanding of their underlying lattice and diffusive dynamics in terms of the interplay between phonons and mobile ions is missing. Herein, inelastic neutron scattering is employed to unravel that phonon softening on heating to T$_c$ ≈ 350 K triggers fast Ag diffusion in the canonical superionic Argyrodite Ag$_8$GeSe$_6$. Ab initio molecular dynamics simulations reproduce the experimental neutron scattering signals and identify the partially ultrafast Ag diffusion with a large diffusion coefficient of 10$^{−4}$ cm$^{−2}$ s$^{−1}$. The study illustrates the microscopic interconnection between soft phonons and mobile ions and provides a paradigm for an intertwined interaction of the lattice and diffusive dynamics in superionic materials

Amorphous-Like Ultralow Thermal Transport in Crystalline Argyrodite Cu7PS6

Due to their amorphous-like ultralow lattice thermal conductivity both below and above the superionic phase transition, crystalline Cu- and Ag-based superionic argyrodites have garnered widespread attention as promising thermoelectric materials. However, despite their intriguing properties, quantifying their lattice thermal conductivities and a comprehensive understanding of the microscopic dynamics that drive these extraordinary properties are still lacking. Here, an integrated experimental and theoretical approach is adopted to reveal the presence of Cu-dominated low-energy optical phonons in the Cu-based argyrodite Cu7PS6. These phonons yield strong acoustic-optical phonon scattering through avoided crossing, enabling ultralow lattice thermal conductivity. The Unified Theory of thermal transport is employed to analyze heat conduction and successfully reproduce the experimental amorphous-like ultralow lattice thermal conductivities, ranging from 0.43 to 0.58 W m−1 K−1, in the temperature range of 100–400 K. The study reveals that the amorphous-like ultralow thermal conductivity of Cu7PS6 stems from a significantly dominant wave-like conduction mechanism. Moreover, the simulations elucidate the wave-like thermal transport mainly results from the contribution of Cu-associated low-energy overlapping optical phonons. This study highlights the crucial role of low-energy and overlapping optical modes in facilitating amorphous-like ultralow thermal transport, providing a thorough understanding of the underlying complex dynamics of argyrodites

Precursor region with full phonon softening above the charge-density-wave phase transition in 2H-TaSe2

Research on charge-density-wave (CDW) ordered transition-metal dichalcogenides continues to unravel new states of quantum matter correlated to the intertwined lattice and electronic degrees of freedom. Here, we report an inelastic x-ray scattering investigation of the lattice dynamics of the canonical CDW compound 2H-TaSe2 complemented by angle-resolved photoemission spectroscopy and density functional perturbation theory. Our results rule out the formation of a central-peak without full phonon softening for the CDW transition in 2H-TaSe2 and provide evidence for a novel precursor region above the CDW transition temperature TCDW, which is characterized by an overdamped phonon mode and not detectable in our photoemission experiments. Thus, 2H-TaSe2 exhibits structural before electronic static order and emphasizes the important lattice contribution to CDW transitions. Our ab-initio calculations explain the interplay of electron-phonon coupling and Fermi surface topology triggering the CDW phase transition and predict that the CDW soft phonon mode promotes emergent superconductivity near the pressure-driven CDW quantum critical point

The role of cellular senescence in metabolic diseases and the potential for senotherapeutic interventions

Cellular senescence represents an irreversible state of cell cycle arrest induced by various stimuli strongly associated with aging and several chronic ailments. In recent years, studies have increasingly suggested that the accumulation of senescent cells is an important contributor to the decline of organ metabolism, ultimately resulting in metabolic diseases. Conversely, the elimination of senescent cells can alleviate or postpone the onset and progression of metabolic diseases. Thus, a close relationship between senescent cells and metabolic diseases is found, and targeting senescent cells has emerged as an alternative therapy for the treatment of metabolic diseases. In this review, we summarize the role of cellular senescence in metabolic diseases, explore relevant therapeutic strategies for metabolic diseases by removing senescent cells, and provide new insights into the treatment of metabolic diseases

Enhanced room-temperature Na+ ionic conductivity in Na4.92_{4.92}Y0.92_{0.92}Zr0.08_{0.08}Si4_{4}O12_{12}

Developing cost-effective and reliable solid-state sodium batteries with superior performance is crucial for stationary energy storage. A key component in facilitating their application is a solid-state electrolyte with high conductivity and stability. Herein, we employed aliovalent cation substitution to enhance ionic conductivity while preserving the crystal structure. Optimized substitution of Y3+ with Zr4+ in Na5YSi4O12 introduced Na+ ​ion vacancies, resulting in high bulk and total conductivities of up to 6.5 and 3.3 ​mS ​cm−1, respectively, at room temperature with the composition Na4.92Y0.92Zr0.08Si4O12 (NYZS). NYZS shows exceptional electrochemical stability (up to 10 ​V vs. Na+/Na), favorable interfacial compatibility with Na, and an excellent critical current density of 2.4 ​mA ​cm−2. The enhanced conductivity of Na+ ​ions in NYZS was elucidated using solid-state nuclear magnetic resonance techniques and theoretical simulations, revealing two migration routes facilitated by the synergistic effect of increased Na+ ​ion vacancies and improved chemical environment due to Zr4+ substitution. NYZS extends the list of suitable solid-state electrolytes and enables the facile synthesis of stable, low-cost Na+ ion silicate electrolytes

Three-way group decisions using evidence theory under hesitant fuzzy linguistic environment

Abstract In the actual decision-making process, there will be situations where decision-makers with hesitant attitudes have difficulties in evaluating alternatives numerically, and hesitant fuzzy linguistic term sets can provide decision-makers with an effective way to describe hesitancy in linguistic terms. In multi-attribute group decision-making, each decision maker typically holds different preferences. If the variation in decision makers’ assessment weights across evaluations of each attribute for every alternative is not adequately accounted for, it can result in a problem of coarse-grained calculations, leading to information loss. Additionally, the three-way decision model faces significant challenges in information fusion within the context of the hesitant fuzzy linguistic environment. Therefore, we propose a new three-way decision-making model under the hesitant fuzzy linguistic environment. The model obtains the confidence of different decision makers in attribute evaluations through the fusion of D-S evidence theory, and can perform more fine-grained fusion calculations on the evaluation information of different decision makers. In addition, the model considers the cost function of each alternative in different decision-making actions under hesitant fuzzy linguistic environment, calculates the two thresholds of each alternative in the three-way decision model, and derives the decision rules. The effectiveness of the model is verified through a numerical example and two comparative experiments, therefore, the model can be applied in intelligent classification or recommendation systems of hesitant fuzzy linguistic information systems

Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network

Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment

Enhancing the In-Plane Behavior of a Hybrid Timber Frame–Mud and Stone Infill Wall Using PP Band Mesh on One Side

Traditional village dwellings in China consisting of timber frames with mud and stone infill walls represent an important part of cultural heritage and civilization. Due to the lack of an effective link between the wood frame and the infill and the poor cohesiveness of clay, the masonry infill can collapse during an earthquake, whereas the wood frame suffers minimal damage. In this study, current retrofitting techniques for village buildings were investigated and discussed. A method using polypropylene (PP) band mesh and cement mortar to retrofit the timber frame with a mud and stone infill was proposed and the connection construction details were designed. In-plane static cyclic tests were conducted on two full-scale wood–stone hybrid walls reinforced on one side with different grid sizes of the PP band mesh. The failure behaviors of the reinforced and non-reinforced sides of the specimens were compared, and the failure mechanics and seismic capacity of the two specimens, i.e., the strength, stiffness, ductility, and energy dissipation, were investigated. The results were also compared with those of a previous frame with stone infill without pebbles and no reinforcement. The study indicated that the retrofitting method strengthened the integrity and lateral resistance of the hybrid structure and prevented the collapse of the stone infill of the reinforced surface in a plane earthquake. The grid size of the PP band mesh substantially affected the lateral performance of the reinforced specimens. The hybrid wall with the narrow PP band mesh grid (150 mm × 150 mm) had a higher lateral stiffness (79%) and lateral capacity (50%) than the wall with the wide grid (250 mm × 250 mm). However, the narrow PP band mesh resulted in a lower ductility of the wall than the wide PP band mesh. The involvement of pebbles in the stone infill led to collapses sooner and a weaker lateral resistance than in the structure without pebble infill