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Deciphering the complicated excited-state process is critical for the development of luminescent materials with controllable emissions in different applications. Here we report the emergence of a photo-induced structural distortion accompanied by an electron redistribution in a series of gold nanoclusters. Such unexpected slow process of excited-state transformation results in near-infrared dual emission with extended photoluminescent lifetime. We demonstrate that this dual emission exhibits highly sensitive and ratiometric response to solvent polarity, viscosity, temperature and pressure. Thus, a versatile luminescent nano-sensor for multiple environmental parameters is developed based on this strategy. Furthermore, we fully unravel the atomic-scale structural origin of this unexpected excited-state transformation, and demonstrate control over the transition dynamics by tailoring the bi-tetrahedral core structures of gold nanoclusters. Overall, this work provides a substantial advance in the excited-state physical chemistry of luminescent nanoclusters and a general strategy for the rational design of next-generation nano-probes, sensors and switches. Excited-state structural and electronic changes, observed in molecules, are hampered in nanomaterials. Here the authors identify structural distortion and electron redistribution in three photoexcited gold nanoclusters, connecting molecular and nanocrystal regimes, enabled by flexibility of the tetrahedral core units.

The core pathology of coronavirus disease 2019 (COVID-19) is infection of airway cells by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in excessive inflammation and respiratory disease, with cytokine storm and acute respiratory distress syndrome implicated in the most severe cases. Thrombotic complications are a major cause of morbidity and mortality in patients with COVID-19. Patients with pre-existing cardiovascular disease and/or traditional cardiovascular risk factors, including obesity, diabetes mellitus, hypertension and advanced age, are at the highest risk of death from COVID-19. In this Review, we summarize new lines of evidence that point to both platelet and endothelial dysfunction as essential components of COVID-19 pathology and describe the mechanisms that might account for the contribution of cardiovascular risk factors to the most severe outcomes in COVID-19. We highlight the distinct contributions of coagulopathy, thrombocytopathy and endotheliopathy to the pathogenesis of COVID-19 and discuss potential therapeutic strategies in the management of patients with COVD-19. Harnessing the expertise of the biomedical and clinical communities is imperative to expand the available therapeutics beyond anticoagulants and to target both thrombocytopathy and endotheliopathy. Only with such collaborative efforts can we better prepare for further waves and for future coronavirus-related pandemics.This Review summarizes the latest evidence indicating that platelet and endothelial dysfunction are essential components of COVID-19 pathology, describes the potential mechanisms underlying the contribution of cardiovascular risk factors to the most severe outcomes in COVID-19, and highlights the roles of coagulopathy, thrombocytopathy and endotheliopathy in COVID-19 pathogenesis.

(Quasi-)one-dimensional systems exhibit various fascinating properties such as Luttinger liquid behavior, Peierls transition, novel topological phases, and the accommodation of unique quasiparticles (e.g., spinon, holon, and soliton, etc.). Here we study molybdenum blue bronze A 0.3 MoO 3 ( A  = K, Rb), a canonical quasi-one-dimensional charge-density-wave material, using laser-based angle-resolved photoemission spectroscopy. Our experiment suggests that the normal phase of A 0.3 MoO 3 is a prototypical Luttinger liquid, from which the charge-density-wave emerges with decreasing temperature. Prominently, we observe strong renormalizations of band dispersions, which are recognized as the spectral function of Holstein polaron derived from band-selective electron-phonon coupling in the system. We argue that the strong electron-phonon coupling plays an important role in electronic properties and the charge-density-wave transition in blue bronzes. Our results not only reconcile the long-standing heavy debates on the electronic properties of blue bronzes but also provide a rare platform to study interesting excitations in Luttinger liquid materials. The mechanism of the charge density wave transition in quasi one-dimensional blue bronzes is still debated. Here, the authors report evidence of a Luttinger liquid in the normal state of blue bronzes and Holstein polarons below the transition temperature, revealing the important role of electron-phonon coupling in the transition.

In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general‐purpose inverse design approach is presented using generative inverse design networks. This ML‐based inverse design approach uses backpropagation to calculate the analytical gradients of an objective function with respect to design variables. This inverse design approach is capable of overcoming local minima traps by using backpropagation to provide rapid calculations of gradient information and running millions of optimizations with different initial values. Furthermore, an active learning strategy is adopted in the inverse design approach to improve the performance of candidate materials and reduce the amount of training data needed to do so. Compared to passive learning, the active learning strategy is capable of generating better designs and reducing the amount of training data by at least an order‐of‐magnitude in the case study on composite materials. The inverse design approach is compared with conventional gradient‐based topology optimization and gradient‐free genetic algorithms and the pros and cons of each method are discussed when applied to materials discovery and design problems. A general‐purpose inverse design approach using generative inverse design networks (GIDNs) is proposed. This deep neural network–based approach uses backpropagation and active learning for inverse design. It is shown that GIDNs can overcome local minima traps and be widely applied to materials discovery and design problems.

The shear stiffness of granular material at small strain levels is a subject of both theoretical and practical interest. This paper poses two fundamental questions that appear to be interrelated: (a) whether this stiffness property is dependent on particle size; and (b) whether the effect of testing method exists in terms of laboratory measurements using resonant column (RC) and bender element (BE) tests. For three uniformly graded types of glass beads of different mean sizes (0·195 mm, 0·920 mm and 1·750 mm), laboratory tests were conducted at a range of confining stresses and void ratios, using an apparatus that incorporates both RC and BE functions and thus allows reliable and insightful comparisons. It is shown that the small-strain stiffness, determined by either the RC or BE tests, does not vary appreciably with particle size, and it may be practically assumed to be size independent. The laboratory experiments also indicate that the BE measurements of small-strain stiffness are comparable to the corresponding RC measurements, with differences of less than 10%. Furthermore, the BE measurements for fine glass beads are found to be consistently higher than the RC measurements, especially at large stress levels, whereas this feature becomes less evident for medium-coarse glass beads, and eventually diminishes for coarse glass beads. The study indicates that the characteristics of output signals in BE tests can be largely affected by the frequency of the input signal, the mean particle size of the material and the confining stress level, and that these factors are interrelated. Improper interpretation of wave signals may lead to shear stiffness measurements that are unreasonably low, either showing a substantial increase with particle size or showing the opposite. A micromechanics-based analysis assuming the Hertz–Mindlin contact law is presented to offer an understanding of the size effect from the grain scale.

Purpose Non-alcoholic fatty liver disease (NAFLD) is considered as both a vital risk factor and a consequence of type 2 diabetes mellitus (T2DM). Low total testosterone (TT) is common in men with T2DM, contributing to increased risks of metabolic diseases. This study aimed to investigate the association between TT levels and the prevalence of NAFLD in men with T2DM. Methods In this cross-sectional study, 1005 men with T2DM were enrolled in National Metabolic Management Center (MMC) of First Affiliated Hospital of Wenzhou Medical University between January 2017 and August 2021. NAFLD was diagnosed using ultrasound as described by the Chinese Liver Disease Association. Overweight/obesity was defined as body mass index (BMI) ≥ 25 kg/m 2 according to WHO BMI classifications. Results Individuals without NAFLD had higher serum TT levels than those with NAFLD. After adjustments for potential confounding factors, the top tertile was significantly associated with lower prevalence of NAFLD compared with the bottom tertile of TT level [odds ratio (OR) 0.303, 95% confidence interval (CI) 0.281–0.713; P  < 0.001]. The association between TT with NAFLD in individuals with normal weight (OR 0.175, 95% CI 0.098–0.315; P  < 0.001) was stronger than in individuals with overweight/obesity (OR 0.509, 95% CI 0.267–0.971; P  = 0.040). There was a significant interaction of TT with overweight/obesity ( P for interaction = 0.018 for NAFLD). Conclusion Higher serum TT was significantly associated with a lower prevalence of NAFLD in men with T2DM. We found that the relationship of TT and NAFLD was stronger in individuals with non-overweight/obesity.

Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastography methods generally require both axial and lateral displacement components, making them less effective for clinical applications. Additionally, these methods often rely on the assumption of material incompressibility, which can lead to inaccurate elasticity reconstruction as no materials are truly incompressible. To address these challenges, a new physics‐informed deep‐learning method for elastography is proposed. This new method integrates a displacement network and an elasticity network to reconstruct the Young's modulus field of a heterogeneous object based on only a measured axial displacement field. It also allows for the removal of the assumption of material incompressibility, enabling the reconstruction of both Young's modulus and Poisson's ratio fields simultaneously. The authors demonstrate that using multiple measurements can mitigate the potential error introduced by the “eggshell” effect, in which the presence of stiff material prevents the generation of strain in soft material. These improvements make this new method a valuable tool for a wide range of applications in medical imaging, materials characterization, and beyond. ElastNet learns the Young's modulus field of a heterogeneous object based on a measured displacement field. The predicted stress tensor is calculated by the encoded elastic constitutive relation based on the strain and Young's modulus. The training procedure minimizes the unbalanced forces with a physical constraint and updates the predicted Young's modulus using backpropagation.

Colloidal crystals are used to understand fundamentals of atomic rearrangements in condensed matter and build complex metamaterials with unique functionalities. Simulations predict a multitude of self-assembled crystal structures from anisotropic colloids, but these shapes have been challenging to fabricate. Here, we use two-photon lithography to fabricate Archimedean truncated tetrahedrons and self-assemble them under quasi-2D confinement. These particles self-assemble into a hexagonal phase under an in-plane gravitational potential. Under additional gravitational potential, the hexagonal phase transitions into a quasi-diamond two-unit basis. In-situ imaging reveal this phase transition is initiated by an out-of-plane rotation of a particle at a crystalline defect and causes a chain reaction of neighboring particle rotations. Our results provide a framework of studying different structures from hard-particle self-assembly and demonstrates the ability to use confinement to induce unusual phases. Boundary conditions can give rise to new types of phases during self-assembly. Here the authors show that tetrahedral particles can form a hexagonal phase on a surface, that can transform into a quasi-diamond phase under a gravitational field.

The primary resonance and nonlinear vibrations of the functionally graded graphene platelet (FGGP)-reinforced rotating pretwisted composite blade under combined the external and multiple parametric excitations are investigated with three different distribution patterns. The FGGP-reinforced rotating pretwisted composite blade is simplified to the rotating pretwisted composite cantilever plate reinforced by the functionally graded graphene platelet. It is novel to simplify the leakage of the airflow in the tip clearance to the non-uniform axial excitation. The rotating speed of the steady state adding a small periodic perturbation is considered. The aerodynamic load subjecting to the surface of the plate is simulated as the transverse excitation. Utilizing the first-order shear deformation theory, von Karman nonlinear geometric relationship, Lagrange equation and mode functions satisfying the boundary conditions, three-degree-of-freedom nonlinear ordinary differential equations of motion are derived for the FGGP-reinforced rotating pretwisted composite cantilever plate under combined the external and multiple parametric excitations. The primary resonance and nonlinear dynamic behaviors of the FGGP-reinforced rotating pretwisted composite cantilever plate are analyzed by Runge–Kutta method. The amplitude–frequency response curves, force–frequency response curves, bifurcation diagrams, maximum Lyapunov exponent, phase portraits, waveforms and Poincare map are obtained to investigate the nonlinear dynamic responses of the FGGP-reinforced rotating pretwisted composite cantilever plate under combined the external and multiple parametric excitations.

Colloidal nanocrystals that consist of an inorganic core and an organic ligand shell can be assembled into architected nanocomposites. A wide range of mechanical properties can be achieved by tuning structural parameters such as sample dimensions, ligand length, and nanocrystal packing density. This review article describes the fabrication, structural characterization and nanomechanical testing of solid assemblies of nanocrystals. Self-assembly and solution processing techniques can be used to form freestanding membranes, thin films and 3D crystals that consist of ordered arrays, or disordered aggregates of nanocrystals. Membrane deflection, nano-indentation, and thin film buckling have been used to determine elastic properties, hardness and fracture toughness. Nanocrystal solids have elastic moduli of < 1–20 GPa, and hardness of ~ 40–450 MPa. Crosslinking of ligands and chemical sintering of inorganic nanoparticles can lead to dramatic improvements in stiffness, strength and toughness. The self-assembly of anisotropic nanocrystals can be used to make nanostructured materials with ordered porosity and 3D architectures.