Explore the vast range of titles available.

Coordination polymers (CPs) and their subset, metal–organic frameworks (MOFs), can have porous structures and hybrid physicochemical properties that are useful for diverse applications. Although crystalline CPs and MOFs have received the most attention to date, their amorphous states are of growing interest as they can be directly synthesized under mild conditions. Directly synthesized amorphous CPs (aCPs) can be constructed from a wider range of metals and ligands than their crystalline and crystal-derived counterparts and demonstrate numerous unique material properties, such as higher mechanical robustness, increased stability and greater processability. This Review examines methods for the direct synthesis of aCPs and amorphous MOFs, as well as their properties and characterization routes, and offers a perspective on the opportunities for the widespread adoption of directly synthesized aCPs. Amorphous coordination polymers and metal–organic frameworks can be directly synthesized under mild conditions using a broader range of metals and ligands than their crystalline and crystal-derived counterparts and therefore exhibit different physicochemical properties. This Review discusses the direct synthesis of amorphous coordination polymers, as well as their characterization, properties and applications.

We report a facile strategy for engineering diverse particles based on the supramolecular assembly of natural polyphenols and a self-polymerizable aromatic dithiol. In aqueous conditions, uniform and size-tunable supramolecular particles are assembled through π–π interactions as mediated by polyphenols. Owing to the high binding affinity of phenolic motifs present at the surface, these particles allow for the subsequent deposition of various materials (i.e., organic, inorganic, and hybrid components), producing a variety of monodisperse functional particles. Moreover, the solvent-dependent disassembly of the supramolecular networks enables their removal, generating a wide range of corresponding hollow structures including capsules and yolk–shell structures. The versatility of these supramolecular networks, combined with their negligible cytotoxicity provides a pathway for the rational design of a range of particle systems (including core–shell, hollow, and yolk–shell) with potential in biomedical and environmental applications. Monodisperse colloidal particles with tunable properties show promise for biomedical, energy, and environmental applications and simple routes for fabricating these particles are of interest. Here, the authors report a facile strategy for fabrication of diverse particles based on the supramolecular assembly of phenols and self-polymerizable thiols

The traditional optimal-path algorithm can address a single constraint in small and straightforward networks. However, in complex multipath distributed cloud services, the network nodes no longer exhibit singular or deterministic path characteristics. It requires the optimal paths that not only determines the shortest routes, but also combine the safety, speed, and enhanced service quality across multiple service nodes in the network topology. The Golden Eagle Optimization Algorithm (GEO) is specialized for optimizing these network service combinations. On this basis, the Golden Eagle Optimizer with Double Learning Strategies (GEO-DLS) resolved the multipath optimal service selection issues within intricate network environments. The algorithm modeled the hunting tactics of wild golden eagles, efficiently targeting the best prey in minimal time by dynamically adjusting two critical components, such as the attack and cruising strategies. In GEO-DLS, the enhanced GEO significantly broadened the search scope for food sources by using personalized learning and mirror reflection techniques. These advancements notably enhanced the GEO search capabilities and improved the solution accuracy. Key contribution include GEO-DLS can converge to the optimal solution faster by optimizing the search strategy and parameter settings. This means that in the problem of network service composition, algorithms can quickly find the optimal path that meets the quality of service (QoS) requirements. To validate the effectiveness of GEO, a set of ten standard benchmark functions was utilized to evaluate its performance. The results from these evaluations consistently presented its superior performance in tackling optimization challenges compared to other five metaheuristic algorithms and five enhanced algorithms.

Proteins play a vital role in different biological processes by forming complexes through precise folding with exclusive inter‐ and intra‐molecular interactions. Understanding the structural and regulatory mechanisms underlying protein complex formation provides insights into biophysical processes. Furthermore, the principle of protein assembly gives guidelines for new biomimetic materials with potential applications in medicine, energy, and nanotechnology. Atomic force microscopy (AFM) is a powerful tool for investigating protein assembly and interactions across spatial scales (single molecules to cells) and temporal scales (milliseconds to days). It has significantly contributed to understanding nanoscale architectures, inter‐ and intra‐molecular interactions, and regulatory elements that determine protein structures, assemblies, and functions. This review describes recent advancements in elucidating protein assemblies with in situ AFM. We discuss the structures, diffusions, interactions, and assembly dynamics of proteins captured by conventional and high‐speed AFM in near‐native environments and recent AFM developments in the multimodal high‐resolution imaging, bimodal imaging, live cell imaging, and machine‐learning‐enhanced data analysis. These approaches show the significance of broadening the horizons of AFM and enable unprecedented explorations of protein assembly for biomaterial design and biomedical research. Proteins form complexes through precise folding and interactions, which are crucial for biological processes. Understanding these mechanisms provides valuable biophysical insights and guides the design of biomimetic materials for applications in medicine, energy, and nanotechnology. Atomic force microscopy (AFM) is essential for studying protein assemblies and interactions in situ at various spatial and temporal scales. This review highlights recent advancements in AFM for elucidating protein structures, diffusions, interactions, phase transitions, and dynamics at solid‒liquid interfaces. It emphasizes the expanding role of AFM in exploring protein assembly and its implications for biomaterial and biomedical research.

Synthetic cell exoskeletons created from abiotic materials have attracted interest in materials science and biotechnology, as they can regulate cell behavior and create new functionalities. Here, a facile strategy is reported to mimic microalgal sporulation with on‐demand germination and locomotion via responsive metal–phenolic networks (MPNs). Specifically, MPNs with tunable thickness and composition are deposited on the surface of microalgae cells via one‐step coordination, without any loss of cell viability or intrinsic cell photosynthetic properties. The MPN coating keeps the cells in a dormant state, but can be disassembled on‐demand in response to environmental pH or chemical stimulus, thereby reviving the microalgae within 1 min. Moreover, the artificial sporulation of microalgae resulted in resistance to environmental stresses (e.g., metal ions and antibiotics) akin to the function of natural sporulation. This strategy can regulate the life cycle of complex cells, providing a synthetic strategy for designing hybrid microorganisms. Controlling microbial behavior is important for optimizing metabolite production and stress resistance for advanced biotechnologies. Microalgae armored with artificial metal–phenolic networks retain their intrinsic photosynthetic properties, and this coating modulates the cell life cycle and microbial motility and provides resilience to environmental stresses.

Droplet bouncing on repellent solid surfaces (e.g., the lotus leaf effect) is a common phenomenon that has aroused interest in various fields. However, the scenario of a droplet bouncing off another droplet (either identical or distinct chemical composition) while moving on a solid material (i.e., ricocheting droplets, droplet billiards) is scarcely investigated, despite it having fundamental implications in applications including self‐cleaning, fluid transport, and heat and mass transfer. Here, the dynamics of bouncing collisions between liquid droplets are investigated using a friction‐free platform that ensures ultrahigh locomotion for a wide range of probing liquids. A general prediction on bouncing droplet–droplet contact time is elucidated and bouncing droplet–droplet collision is demonstrated to be an extreme case of droplet bouncing on surfaces. Moreover, the maximum deformation and contact time are highly dependent on the position where the collision occurs (i.e., head‐on or off‐center collisions), which can now be predicted using parameters (i.e., effective velocity, effective diameter) through the concept of an effective interaction region. The results have potential applications in fields ranging from microfluidics to repellent coatings. Droplets colliding and ricocheting on super‐repellent surfaces display dynamics consistent with droplet–surface collisions, but with tunable dynamics between bouncing droplets via control over the angle of incidence. Parameters introduced using the concept of an effective interaction region allow for a universal description and prediction of droplet bouncing dynamics, with implications for wetting characteristics of next‐generation surfaces.

A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client–server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.

In this paper, we introduce a novel class of quasi-symmetric positive tensors, which generalize nonnegative symmetric tensors. We propose a parametric transformation algorithm dedicated to calculating the largest eigenvalue of nonnegative tensors. Leveraging structural information encoded in the tensor’s associated directed graphs, we show that our algorithm has R-linear convergence for weakly irreducible quasi-symmetric positive tensors. Furthermore, we establish a general condition for the linear convergence of the algorithm, thus extending existing linear convergence theories, such as those underlying the Ng-Qi-Zhou (NQZ) algorithm for essentially positive tensors and the Liu-Zhou-Ibrahim (LZI) algorithm for weakly positive tensors. Meanwhile, we perform numerical experiments to compare the computational efficiency of our proposed algorithm with that of the NQZ and LZI algorithms.

One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.

As network services tend to be integrated to provide a better quality of service (QoS) to customers, correlations appear to be useful measurements to better design service compositions of an integrated network. However, as the integration intensifies, the determination of the service compositions of the network requires the ease of computational issues in this dynamic environment, which are resolved by the cloud computing platforms. The manuscript proposes an algorithm based on multi-constraint optimal path selection (MCOPS) that benefits from available notions such as QoS correlation criteria and correlation ratios, and skyline algorithm to construct a novel directed cyclic graph whose calculations are conducted on the cloud platform dynamically. Both cost and delay attributes in the construction of network service compositions for customers are included in the graph. Simulations suggest that both average calculation time and the quality of the path solution are substantially enhanced with the utilization of cloud computing in network service compositions. Consequently, a better service composition plan (SCP) is attained when a correlated structure is assumed to exist.